Gearbox life assessment method and system

Abstract

The present invention relates to the field of data processing technology, and in particular to a gearbox life assessment method and system. The method comprises the following steps: collecting the shaft current of the gearbox under electromechanical coupling conditions, and correlating the current characteristics of each bearing to generate a characteristic sequence of each bearing current; according to the characteristic sequence of each bearing current, the main flow path and branch of the current between the gear meshing pairs in the gearbox are identified, and an electro-erosion damage assessment is performed to generate the proportion of electro-erosion damage on the bearing-tooth surface; according to the proportion of electro-erosion damage on the bearing-tooth surface, the fatigue life reduction coefficient of the component is calculated, and dynamic linear damage accumulation processing is performed to obtain the fatigue damage contribution value of each component in the cycle; according to the fatigue damage contribution value of each component in the cycle, the gearbox life is assessed to generate the percentage of the remaining life of the gearbox. The present invention accurately predicts the remaining life of the gearbox under electromechanical coupling conditions through the coordinated assessment of electro-erosion damage and vibration amplification effect.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a gearbox life assessment method and system. Background Art

[0002] Gearboxes, as core components in mechanical transmission systems, are currently driven by electric motors, subjecting them to long-term electromechanical coupling. Under these conditions, electromagnetic fields are increasingly impacting gearbox material properties and fatigue life. Gearbox failure modes under electromechanical coupling conditions exhibit complex characteristics. On the one hand, the harmonic torque generated by the motor imposes variable-amplitude cyclic loading on the tooth surfaces, accelerating fatigue crack initiation. On the other hand, the induced currents from the electromagnetic field can generate micro-arcing on the gear contact surfaces, creating microscopic electro-corrosion pits that act as stress concentration sources and significantly reduce the fatigue life of the gears. This electromechanical coupling effect is particularly pronounced under frequent start-stop and variable load conditions. Gearboxes operating in strong electromagnetic fields, particularly those with lightweight, high-power density gear transmission systems, often experience actual service life significantly lower than predicted by traditional methods, and premature failure is common. However, traditional gearbox life assessment methods are mainly based on pure mechanical stress analysis and material fatigue theory. Most of them simplify the working environment to pure mechanical load conditions and ignore the influence of electromagnetic fields on the microstructure and surface properties of the material, resulting in significant deviations between the assessment results and the actual service life. Summary of the Invention

[0003] Based on this, the present invention provides a gearbox life assessment method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, a gearbox life assessment method includes the following steps:

[0005] Step S1: Under electromechanical coupling conditions, the shaft current of the gearbox is collected, and the current characteristics of each bearing are correlated to generate a characteristic sequence of each bearing current; the electromagnetic field of the gearbox is simultaneously collected, and the electromagnetic disturbance level to which each component of the gearbox is subjected is evaluated based on the characteristic sequence of each bearing current, thereby generating internal electromagnetic disturbance snapshot data;

[0006] Step S2: Based on the characteristic sequence of each bearing current, the main flow paths and branches of the current between the gear meshing pairs in the gearbox are identified, and the current distribution ratio is estimated to generate meshing pair-bearing estimated current share data; the gearbox design parameters are obtained; based on the internal electromagnetic disturbance snapshot data and the gearbox design parameters, the electro-erosion damage is assessed using the meshing pair-bearing estimated current share data to generate the bearing-tooth surface electro-erosion damage ratio;

[0007] Step S3: Calculate the component fatigue life reduction factor based on the proportion of electrical corrosion damage on the bearing-tooth surface, and perform dynamic linear damage accumulation processing to obtain the fatigue damage contribution value of each component in the cycle;

[0008] Step S4: Perform fatigue damage analysis on the leading components according to the fatigue damage contribution value of each component in the cycle, and perform gearbox life assessment to generate the remaining life percentage of the gearbox.

[0009] Preferably, the present invention further provides a gearbox life assessment system for executing the gearbox life assessment method described above, the gearbox life assessment system comprising:

[0010] The electromagnetic feature acquisition module is used to collect the gearbox shaft current under electromechanical coupling conditions, correlate the current characteristics of each bearing, and generate a characteristic sequence of each bearing current. It also simultaneously collects the gearbox electromagnetic field and evaluates the electromagnetic disturbance level experienced by each gearbox component based on the characteristic sequence of each bearing current, generating internal electromagnetic disturbance snapshot data.

[0011] The electro-erosion path assessment module is used to identify the main current flow paths and branches between the gear meshing pairs in the gearbox based on the characteristic sequence of each bearing current, estimate the current distribution ratio, and generate estimated meshing pair-bearing current share data; obtain gearbox design parameters; and perform electro-erosion damage assessment based on the meshing pair-bearing estimated current share data based on internal electromagnetic disturbance snapshot data and gearbox design parameters to generate the bearing-tooth surface electro-erosion damage ratio;

[0012] The fatigue damage accumulation module is used to calculate the component fatigue life reduction factor based on the proportion of electrical corrosion damage on the bearing-tooth surface, and perform dynamic linear damage accumulation processing to obtain the fatigue damage contribution value of each component in the cycle;

[0013] The life prediction analysis module is used to analyze the fatigue damage degree of the leading components according to the fatigue damage contribution value of each component in the cycle, and to evaluate the life of the gearbox to generate the remaining life percentage of the gearbox.

[0014] This invention significantly improves the accuracy and reliability of life assessment by comprehensively considering the impact of electromagnetic fields on gearbox life under electro-mechanical coupling conditions. It pioneered a direct correlation between current characteristics and electro-corrosion damage, breaking through the limitations of traditional assessment methods that only consider mechanical stress and ignore electromagnetic factors. By establishing a current flow path model and analyzing the proportion of electro-corrosion damage, the differentiated impact of electromagnetic fields on different gear components is accurately quantified, effectively solving the problem of life prediction deviation caused by insufficient electromagnetic shielding in lightweight gearboxes. It is particularly suitable for frequent start-stop and variable load conditions, and can accurately capture the fatigue acceleration effects caused by harmonic torque and micro-arcing discharges, significantly improving the consistency between predicted results and actual service life. Through a dynamic linear damage accumulation processing mechanism, the fatigue state evolution of each component can be tracked in real time, and potential early failure hazards can be identified in a timely manner. In addition, this method can also adaptively adjust the assessment parameters based on the electromagnetic disturbance characteristics under different operating conditions, so that the assessment results maintain high accuracy under various complex operating conditions. Compared with traditional methods, this assessment method incorporates the influence of electromagnetic fields as a core factor into the calculation model, which improves the assessment accuracy by more than 30%, especially in high-power density transmission systems. By analyzing the fatigue damage degree of the leading components, the weakest link in the gearbox that is most prone to failure can also be accurately located. This method innovatively establishes an electro-mechanical-material multi-field coupling damage mechanism, filling the gap in the field of gearbox life assessment in electromagnetic environments in the existing technology, providing strong technical support for improving the reliability and extending the service life of mechatronic equipment, and has broad engineering application prospects. Therefore, a gearbox life assessment method of the present invention synchronously collects shaft current and electromagnetic field data through a high-frequency response Rogowski coil and a micro-magnetic field probe array, combines the gear meshing topology to analyze the current flow path, and establishes a meshing pair-bearing current share distribution model; introduces ultrasonic oil film thickness monitoring and electrothermal-mechanical disturbance analysis to construct a lubrication state assessment system based on the oil film thickness ratio; innovatively multiplies the vibration amplification factor and the electro-corrosion damage ratio to form an electromechanical coupling high-risk component identification mechanism; combines the coupled vibration enhanced stress history calculation with the electro-corrosion damage reduction coefficient to achieve accurate quantification of the fatigue damage accumulation process; finally, the remaining life of the gearbox is dynamically predicted based on the damage rate of the leading component, solving the assessment deviation problem caused by the traditional method ignoring the electromechanical coupling effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 Schematic diagram of the steps of the gearbox life assessment method of the present invention;

[0016] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0017] To achieve this, please refer to Figure 1The present invention provides a gearbox life assessment method, comprising the following steps:

[0018] Step S1: Under electromechanical coupling conditions, the shaft current of the gearbox is collected, and the current characteristics of each bearing are correlated to generate a characteristic sequence of each bearing current; the electromagnetic field of the gearbox is simultaneously collected, and the electromagnetic disturbance level to which each component of the gearbox is subjected is evaluated based on the characteristic sequence of each bearing current, thereby generating internal electromagnetic disturbance snapshot data;

[0019] Step S2: Based on the characteristic sequence of each bearing current, the main flow paths and branches of the current between the gear meshing pairs in the gearbox are identified, and the current distribution ratio is estimated to generate meshing pair-bearing estimated current share data; the gearbox design parameters are obtained; based on the internal electromagnetic disturbance snapshot data and the gearbox design parameters, the electro-erosion damage is assessed using the meshing pair-bearing estimated current share data to generate the bearing-tooth surface electro-erosion damage ratio;

[0020] Step S3: Calculate the component fatigue life reduction factor based on the proportion of electrical corrosion damage on the bearing-tooth surface, and perform dynamic linear damage accumulation processing to obtain the fatigue damage contribution value of each component in the cycle;

[0021] Step S4: Perform fatigue damage analysis on the leading components according to the fatigue damage contribution value of each component in the cycle, and perform gearbox life assessment to generate the remaining life percentage of the gearbox.

[0022] In an embodiment of the present invention, the gearbox life assessment method includes the following steps:

[0023] Step S1: Under electromechanical coupling conditions, the shaft current of the gearbox is collected, and the current characteristics of each bearing are correlated to generate a characteristic sequence of each bearing current; the electromagnetic field of the gearbox is simultaneously collected, and the electromagnetic disturbance level to which each component of the gearbox is subjected is evaluated based on the characteristic sequence of each bearing current, thereby generating internal electromagnetic disturbance snapshot data;

[0024] In this embodiment of the present invention, HRC-50 high-frequency Rogowski coils with an inner diameter of 40 mm, an outer diameter of 60 mm, and a thickness of 10 mm are installed on the gearbox's sun gear input bearing, planetary carrier output bearing, and the outer rings of the four planetary gear bearings. Their frequency response range is 10 Hz to 100 kHz. An array of 16 MS-100 miniature magnetic field probes is placed on the outer wall of the gearbox's planetary gear bearing area and near the ring gear's fixed end. Wideband sampling of the shaft current is performed using a DAQ-9000 high-speed data acquisition device, with a sampling frequency of 500 kHz and a sampling duration of 60 seconds. The gearbox operates at a rated speed of 1500 rpm and a load of 75% of the rated load. A 4096-point FFT transform is performed on the raw shaft current time-domain sequence, using a Hanning window as the window function and a 50% overlap between adjacent windows. The main harmonic components are filtered using a 5% amplitude threshold to generate the main harmonic amplitude and frequency sequences of the shaft current, which are then correlated to form characteristic sequences for each bearing current. A three-axis magnetic field probe was used to collect magnetic field data on the gearbox surface at a sampling frequency of 10kHz. The internal space of the gearbox was divided into 100,000 grid cells using the finite element method to reconstruct the internal magnetic field distribution. The internal space was divided into key functional areas, and the statistical characteristics of the magnetic field strength in each area were calculated. The electromagnetic disturbance level was assessed using the current characteristic sequence, generating internal electromagnetic disturbance snapshot data containing component name, location coordinates, magnetic field strength, main excitation frequency, and disturbance level.

[0025] Step S2: Based on the characteristic sequence of each bearing current, the main flow paths and branches of the current between the gear meshing pairs in the gearbox are identified, and the current distribution ratio is estimated to generate meshing pair-bearing estimated current share data; the gearbox design parameters are obtained; based on the internal electromagnetic disturbance snapshot data and the gearbox design parameters, the electro-erosion damage is assessed using the meshing pair-bearing estimated current share data to generate the bearing-tooth surface electro-erosion damage ratio;

[0026] In this embodiment of the present invention, based on the characteristic current sequences of each bearing obtained in step S1, the effective current values of the sun gear input bearing (150 mA), the planet carrier output bearing (105 mA), and the four planet gear bearings (38 mA, 35 mA, 39 mA, and 37 mA, respectively) were extracted. A current flow network model was established based on the sun gear-planet gear-ring gear connection topology (sun gear with 25 teeth, planet gear with 35 teeth, ring gear with 95 teeth, and four planet gears). Analysis showed that the 150 mA current flowing into the sun gear input bearing was distributed to the four planet gears in the proportions of 25%, 23%, 27%, and 25%. The number of equivalent conductive contact points in the gearbox bearings was set to 5 for the sun gear input bearing, 7 for the planet carrier output bearing, and 4 for the planet gear bearings. The contact resistance ranges were 15-25 ohms for the sun gear input bearing, 10-18 ohms for the planet carrier output bearing, and 20-35 ohms for the planet gear bearings. The current distribution ratios for the sun gear-planet gear meshing pair and the planet gear-ring gear meshing pair were calculated to generate estimated current share data for the meshing pair and bearing. Material properties were derived from the gearbox design parameters: 20CrMnTi gear material, critical electrocorrosion current density threshold of 0.8A / mm²; GCr15 bearing material, critical electrocorrosion current density threshold of 0.6A / mm². The instantaneous effective conductive contact area and bearing contact area at each meshing point were calculated to determine the average current density on the tooth surface and the distribution range of the bearing current density. The meshing area severity index was calculated using the electrothermal-mechanical disturbance data on the tooth surface, the electrocorrosion damage rate was assessed, and the bearing-tooth surface electrocorrosion damage ratio was calculated.

[0027] Step S3: Calculate the component fatigue life reduction factor based on the proportion of electrical corrosion damage on the bearing-tooth surface, and perform dynamic linear damage accumulation processing to obtain the fatigue damage contribution value of each component in the cycle;

[0028] In this embodiment of the present invention, high-bandwidth torque sensors (models TM-5000 and TM-8000) with rated ranges of 5000 N·m and 8000 N·m, respectively, and a sampling frequency of 20 kHz are installed on the input shaft flange and output shaft splines of the gearbox. Six PCB-356A33 triaxial high-frequency accelerometers (models PCB-356A33) with a sampling frequency of 25.6 kHz are installed on the planetary carrier bearing seat and sun gear bearing seat. A FFT is applied to the double-ended torque time-domain series to extract the amplitude and phase of the harmonic torque components, generating a harmonic torque component spectrum. Spectral analysis is performed on the multi-channel vibration data and compared with potential electromagnetic excitation frequency data from internal electromagnetic disturbance snapshot data to screen for effective electromagnetic vibration response frequencies. Frequency alignment and proximity matching are used to identify potential electromechanical coupling frequency pairs, calculate the vibration amplification factor, and generate high-risk coupled vibration data for each component based on the bearing-tooth surface electro-erosion damage ratio. The contact stress history during gear meshing is calculated using Hertzian contact theory, and enhanced using the high-risk coupled vibration data to generate an enhanced coupled vibration stress history. The component fatigue life reduction factors were calculated based on the proportion of electrical corrosion damage on the bearing tooth surfaces. The reduction factors for the sun gear-planet gear 1 meshing pair were 1.65, and the reduction factors for the sun gear input bearing were 1.29. Fatigue damage was calculated using linear damage accumulation theory. The fatigue damage contribution value of the sun gear-planet gear 1 meshing pair during the current monitoring cycle was 0.2475. The fatigue damage contribution values of each component during the cycle were then calculated.

[0029] Step S4: Perform fatigue damage analysis on the leading components according to the fatigue damage contribution value of each component in the cycle, and perform gearbox life assessment to generate the remaining life percentage of the gearbox.

[0030] In this embodiment of the present invention, a gearbox life assessment model is established based on the fatigue damage contribution of each component over a given period. First, the fatigue damage contribution of each component is accumulated, and the total operating time and accumulated fatigue damage of the gearbox are recorded. For the sun gear-planet gear 1 meshing pair, the current accumulated fatigue damage is 3.75, representing 37.5% of the design life; the accumulated fatigue damage of the sun gear input bearing is 2.15, representing 21.5% of the design life; and the fatigue damage of all other components is less than 20%. By analyzing the fatigue damage contribution of each component, the sun gear-planet gear 1 meshing pair is identified as the dominant component, with a damage rate of 7.5% per 1000 hours of operation. Based on the damage accumulation rate of the dominant component, the remaining life of the gearbox is predicted. The calculation formula takes into account the effects of accelerated electro-erosion damage, enhanced vibration coupling, and material degradation. A comprehensive assessment results in a remaining life of 8333 hours, equivalent to 62.5% of the design life. The system generates a gearbox remaining life percentage report, which includes the damage status, damage rate, predicted remaining life, and maintenance recommendations for each key component, providing equipment managers with a sound basis for maintenance decisions.

[0031] Preferably, in step S1, collecting the shaft current of the gearbox under the electromechanical coupling working condition and correlating the current characteristics of each bearing include:

[0032] Install high-frequency Rogowski coils on the sun gear input bearing seat, planet carrier output bearing seat, and outer ring of at least one planet gear bearing of the gearbox; arrange a micro magnetic field probe array on the outer wall of the planet gear bearing area and the outer wall near the fixed end of the gear ring in the gearbox body;

[0033] The high-frequency response Rogowski coil is used to perform wide-band sampling of the shaft current to obtain the original shaft current time domain series;

[0034] Perform fast Fourier transform on the original shaft current time domain series, and then extract the amplitude and phase of each frequency component to generate the original shaft current spectrum data;

[0035] The main harmonic component analysis of the shaft current original spectrum data is performed through a preset amplitude threshold value to generate a shaft current main harmonic amplitude sequence;

[0036] Extract the harmonic frequencies corresponding to the main harmonic amplitude sequence of the shaft current according to the original spectrum data of the shaft current, and obtain the main harmonic frequency sequence of the shaft current;

[0037] The shaft current main harmonic amplitude sequence and the shaft current main harmonic frequency sequence are correlated with the bearing current characteristics to generate the bearing current characteristic sequence.

[0038] In this embodiment of the present invention, a high-frequency Rogowski coil (model HRC-50) is installed on the gearbox's sun gear input bearing seat. The coil has an inner diameter of 40 mm, an outer diameter of 60 mm, and a thickness of 10 mm. The coil winding uses 120 turns of 0.5 mm enameled wire, with a frequency response range of 10 Hz to 100 kHz. A Rogowski coil of the same specifications is installed on the planetary carrier's output bearing seat. For the planetary gear bearings, a small high-frequency Rogowski coil (model HRC-30) with an inner diameter of 25 mm, an outer diameter of 40 mm, and a thickness of 8 mm is installed on the outer ring of each planetary gear bearing. Eight MS-100 miniature magnetic field probes are arranged on the outer wall of the planetary gear bearing area of the gearbox housing, with the probes spaced 45 degrees apart, forming a circular array. Eight similar miniature magnetic field probes are also arranged on the outer wall near the fixed end of the ring gear, forming a probe array. All coils and probes are connected to a data acquisition system via shielded cables. A DAQ-9000 high-speed data acquisition device was used for wideband shaft current sampling, with a sampling frequency of 500 kHz and a sampling duration of 60 seconds. Each sampling period collected Rogowski coil signals from the sun gear input bearing seat, the planetary carrier output bearing seat, and the outer races of all planetary gear bearings. The data acquisition device's input impedance was set to 10 MΩ, the analog-to-digital conversion accuracy was 16 bits, and the acquisition gain was set to 10x. During the acquisition process, the gearbox operated at a rated speed of 1500 rpm and a load of 75% of the rated load. The acquired raw shaft current time-domain series was stored according to bearing position in a binary floating-point array format, with the current amplitude at each sampling point recorded in milliamperes (mA). A 4096-point FFT window was used for the acquired raw shaft current time-domain series, with a Hanning window function and a 50% overlap between adjacent windows. The FFT calculation first segmented the time-domain series according to the window length, multiplied each segment by the Hanning window function, and then calculated the FFT of each segment to obtain a complex frequency-domain representation. The modulus of the complex result is then extracted as the amplitude, and the phase angle of the complex result is extracted as the phase, forming a frequency-amplitude-phase triplet. The calculated spectral resolution is 122 Hz (sampling frequency divided by the number of FFT points). The time domain series for each bearing position is processed separately to generate the corresponding raw shaft current spectrum data, which contains all frequency components in the range of 0 Hz to 250 kHz. The amplitude threshold is set at 5% of the maximum amplitude in the spectrum. This threshold is determined by analyzing the shaft current spectrum characteristics of healthy gearboxes in historical data. By calculating the spectrum data of 100 healthy gearboxes under different operating conditions and statistically analyzing the amplitude ratio of minor harmonics to major harmonics, it is determined that a threshold of 5% can effectively distinguish major harmonics from minor harmonics. During the screening process, all frequency points in the spectrum are traversed. When the amplitude of a frequency point is greater than or equal to the threshold, the corresponding amplitude is extracted and arranged in ascending order of frequency to form a shaft current main harmonic amplitude sequence.This sequence typically contains 10 to 20 primary harmonic components, recording the amplitudes of frequency points where energy in the gearbox shaft current is concentrated. For each primary harmonic amplitude identified in step 4, the corresponding frequency value is searched in the original spectrum data. This extraction process uses a precise indexing approach, directly obtaining the corresponding frequency value based on the amplitude's position in the spectrum data. To improve frequency identification accuracy, quadratic interpolation is performed near the original frequency point, and the exact frequency corresponding to the amplitude peak is determined through a three-point quadratic polynomial fit. The extracted frequency values are arranged in the same order as the primary harmonic amplitude sequence to form a shaft current primary harmonic frequency sequence. The frequency values in this sequence are accurate to 0.1 Hz and record the exact frequency locations of the primary energy distribution in the shaft current. The shaft current primary harmonic amplitude sequence obtained at each measurement point is integrated and analyzed with the frequency sequence to establish a bearing current characteristic correlation model. First, the characteristic frequencies of the sun gear input bearing, planetary carrier output bearing, and planetary gear bearings are identified. These frequencies are precalculated based on bearing geometry (inner ring diameter, outer ring diameter, rolling element diameter, number of rolling elements) and rotational speed. The measured main harmonic frequency is then matched with the characteristic frequency of each bearing. Harmonic components and their amplitudes that are close to the characteristic frequency (with an error of less than 1%) are extracted to generate the current signature sequence for each bearing. These signature sequences contain the current harmonic information unique to each bearing and reflect the electrical condition of the bearing.

[0039] Preferably, synchronously collecting the electromagnetic field of the gearbox in step S1 and evaluating the electromagnetic disturbance level to which each component of the gearbox is subjected based on the characteristic sequence of each bearing current includes:

[0040] The micro magnetic field probe array is used to synchronously collect the magnetic field intensity at each measuring point to obtain the discrete magnetic field data on the box surface;

[0041] Perform triaxial component analysis on the discrete magnetic field data on the box surface to evaluate the electromagnetic field leakage intensity and direction at the measuring point and obtain spatial electromagnetic field distribution data;

[0042] The gearbox is mapped in three-dimensional space based on the spatial electromagnetic field distribution data, and the spatial magnetic field is estimated to reconstruct the equivalent leakage magnetic field intensity distribution near the inner ring of the planetary carrier bearing and the meshing line between the sun gear and the planetary gear in the gearbox, generating internal equivalent magnetic field intensity cloud data.

[0043] The internal equivalent magnetic field intensity cloud data is spatially divided, and the average and peak magnetic field intensities of the planetary gear bearing raceways, sun gear tooth surfaces, and planetary gear tooth surfaces in the gearbox are calculated to generate magnetic induction characteristic data on the surfaces of key components.

[0044] Evaluate the electromagnetic excitation of the planetary gear train's rotating components based on the characteristic current sequence of each bearing, and generate potential electromagnetic excitation frequency data;

[0045] The electromagnetic disturbance level to which each gearbox component is subjected is evaluated based on the potential electromagnetic excitation frequency data and the surface magnetic induction characteristic data of key components, and the electromagnetic disturbance characteristics are extracted to generate internal electromagnetic disturbance snapshot data.

[0046] In this embodiment of the present invention, an MF-300 three-axis magnetic field probe array was used to collect magnetic field intensity on the gearbox surface. The probes had a sensitivity of 0.5 μT, a measurement range of ±2000 μT, and a sampling frequency of 10 kHz. Eight probes were installed in a circular array on the outer wall of the gearbox housing in the bearing area of the planetary gears, with a probe spacing of 45 degrees. Eight probes were also installed on the outer wall near the fixed end of the ring gear, also in a circular array. All probes collected signals synchronously using a 16-channel synchronous data acquisition device for 60 seconds. During the acquisition process, the gearbox maintained a rated speed of 1500 rpm and a load of 75% of the rated load. Each probe collected magnetic field intensity data in the X, Y, and Z directions, generating 600,000 sampling points in each direction to form a three-dimensional matrix data structure. After acquisition, all probe data were calibrated according to spatial position to generate discrete magnetic field data on the housing surface containing position information. The data format is [measurement point number, X coordinate, Y coordinate, Z coordinate, time point, magnetic field intensity in the X direction, magnetic field intensity in the Y direction, magnetic field intensity in the Z direction]. When performing three-axis component analysis on the collected discrete magnetic field data on the box surface, first perform time domain analysis on the magnetic field data in the X, Y, and Z directions of each measuring point to calculate the mean, standard deviation, maximum, and minimum values. Then, the time domain signal is converted into a frequency domain signal through fast Fourier transform, and the amplitude and phase of each frequency component are analyzed. For the frequency domain signal, the first 10 main frequency components are extracted. These components usually correspond to the characteristic frequencies of the mechanical components in the gearbox. The magnetic field data in the three directions are synthesized into the total magnetic field intensity, and the calculation formula is: , where Mt represents the total magnetic field intensity, and Mx, My, and Mz represent the magnetic field intensities in the X, Y, and Z directions, respectively. By analyzing the direction and magnitude of the total magnetic field intensity, the electromagnetic field leakage intensity at each measurement point is determined, ranging from 5 μT to 60 μT, and the main magnetic field direction is determined. The analysis results from all measurement points are integrated to generate spatial electromagnetic field distribution data containing spatial coordinates and three-axis magnetic field components. A spatial coordinate system is established based on the 3D CAD model of the gearbox, with the origin set at the center of the gearbox, the X-axis pointing forward, the Y-axis pointing to the right, and the Z-axis pointing upward. The 16 measurement points are precisely positioned within this coordinate system, with the measurement point accuracy controlled within ±1 mm. The internal magnetic field of the gearbox is inferred using the inverse magnetic field problem solution. Specifically, the finite element method is used to divide the gearbox interior into 100,000 grid cells, with each cell having a side length of no more than 2 mm. Based on the surface-measured magnetic field intensity and direction, the internal magnetic field distribution is solved using Maxwell's equations. The solution takes into account the magnetic permeability of the gearbox material (the relative magnetic permeability of steel is assumed to be 100), as well as the geometry and material properties of components such as gears and bearings. An iterative calculation (500 iterations with a convergence threshold of 0.1%) reconstructs the three-dimensional magnetic field distribution within the gearbox, specifically the magnetic field intensity distribution near the inner ring of the planet carrier bearing and the meshing line between the sun gear and the planet gears. The results form an internal equivalent magnetic field intensity cloud with an accuracy of 1 μT. Based on the gearbox's structural characteristics, the internal space is divided into key functional areas: the sun gear area, the planet gear area, the inner ring area, the planet carrier area, and the bearing areas. Each area is further subdivided into multiple subareas. For example, the planet gear bearing area is subdivided into the inner ring, outer ring, rolling element, and cage subareas. Statistical analysis of the magnetic field intensity data within each subarea is performed to calculate the average magnetic field intensity, standard deviation, maximum, and minimum values. For the planetary gear bearing raceways, the calculated average magnetic field strength is 25 μT, with a peak magnetic field strength of 45 μT. For the sun gear tooth surfaces, the average magnetic field strength is 30 μT, with a peak magnetic field strength of 55 μT. For the planetary gear tooth surfaces, the average magnetic field strength is 28 μT, with a peak magnetic field strength of 50 μT. Furthermore, the spatial gradient distribution of the magnetic field strength is analyzed to identify regions with dramatic variations in magnetic field strength, which are typically locations of strong electromagnetic disturbances. By comprehensively analyzing the magnetic field strength distribution characteristics of each key component surface, magnetic induction signature data for the key component surfaces is generated. This data includes component name, location coordinates, average magnetic field strength, peak magnetic field strength, and the main magnetic field direction. The acquired bearing current signature sequences are combined with the gearbox kinematic parameters to analyze the relationship between current frequency and mechanical component motion frequency. The gearbox kinematic parameters include: sun gear speed of 1500 rpm (corresponding to a frequency of 25 Hz), planet gear speed of 428.6 rpm (corresponding to a frequency of 7.14 Hz), and planet carrier speed of 150 rpm (corresponding to a frequency of 2.5 Hz).Harmonic analysis is used to extract components of the bearing current that are related to these mechanical frequencies and their multiples. For the sun gear input bearing, the primary electromagnetic excitation frequencies are 25 Hz, 50 Hz, and 75 Hz; for the planetary carrier output bearing, the primary electromagnetic excitation frequencies are 2.5 Hz, 5 Hz, 7.5 Hz, and 25 Hz; and for the planetary gear bearings, the primary electromagnetic excitation frequencies are 7.14 Hz, 14.28 Hz, and 21.42 Hz. The energy distribution of each frequency component is calculated to assess the electromagnetic excitation intensity of each component at different frequencies. For the sun gear-planet gear meshing frequency (calculated by multiplying the number of sun gear teeth by the sun gear speed frequency, i.e., 25 × 25 Hz = 625 Hz), the representation of this frequency and its multiples and fractions in the current characteristic sequence is analyzed to determine the electromagnetic excitation during the meshing process. By integrating the energy and distribution characteristics of each frequency component, potential electromagnetic excitation frequency data is generated, including information such as frequency value, energy level, associated components, and excitation intensity. An electromagnetic disturbance level assessment standard has been established, categorizing disturbance levels into five levels: weak (0-10μT), mild (10-30μT), moderate (30-50μT), strong (50-80μT), and severe (>80μT). The electromagnetic disturbance level of each gearbox component is assessed by combining data on potential electromagnetic excitation frequencies and surface magnetic induction characteristics of key components. The assessment considers three key factors: magnetic field strength, frequency characteristics, and exposure duration. For the sun gear tooth surface, the average magnetic field strength is 30μT, mainly excited by a 25Hz frequency, and is assessed as a medium disturbance level. For the planet gear tooth surface, the average magnetic field strength is 28μT, mainly excited by 7.14Hz and 25Hz frequencies, and is assessed as a medium disturbance level. For the planet gear bearing raceway, the average magnetic field strength is 25μT, mainly excited by 7.14Hz frequency, and is assessed as a slight disturbance level. For the planet carrier bearing, the average magnetic field strength is 35μT, mainly excited by 2.5Hz frequency, and is assessed as a medium disturbance level. For the sun gear input bearing, the average magnetic field strength is 45μT, mainly excited by 25Hz frequency, and is assessed as a medium disturbance level. The assessment results are summarized into internal electromagnetic disturbance snapshot data, which contains information such as component name, location coordinates, magnetic field strength, main excitation frequency, disturbance level, and potential impact.

[0047] Preferably, in step S2, determining the main flow paths and branches of the current between the gear meshing pairs in the gearbox according to the characteristic sequence of each bearing current, and estimating the current distribution ratio includes:

[0048] Obtain the connection topology of the sun gear, planet gear and ring gear in the gearbox and the geometric constraint data of the planetary gear system;

[0049] Extracting the current value of each bearing according to the characteristic sequence of each bearing current;

[0050] Based on the sun gear-planet gear-ring gear connection topology, the conductive connection topology analysis of each bearing current value is performed. Based on the geometric constraints of the planetary gear system, the main flow paths and branches of the current between the gear meshing pairs are identified to obtain the main current path data between the gears. The gear meshing pairs include the sun gear-planet gear meshing pair and the planet gear-ring gear meshing pair.

[0051] According to the main current path data between gears, the number of equivalent conductive contact points and the contact resistance range of the bearings in the gearbox are set to generate the bearing shunt characteristic data;

[0052] Based on the bearing current shunt characteristic data and the main current path data between gears, the current distribution ratio of the sun gear-planet gear meshing pair and the planet gear-inner ring gear meshing pair is estimated to generate the meshing pair-bearing estimated current share data.

[0053] In this embodiment of the present invention, the sun gear-planet gear-ring gear connection topology is obtained from the gearbox design drawings. The topology includes 25 sun gear teeth, 35 planet gear teeth, and 95 ring gear teeth. There are four planet gears, evenly distributed around the sun gear, with an angle of 90 degrees between adjacent planet gears. The sun gear forms a meshing pair with each planet gear, and each planet gear forms a meshing pair with the ring gear, for a total of eight meshing pairs. Geometric constraints for the planetary gear train are recorded, including the center distances: 75 mm from sun gear to planet gear, 125 mm from planet gear to ring gear; 40 mm sun gear input shaft diameter, 60 mm planet carrier output shaft diameter; 25 mm planet gear shaft diameter; sun gear bearing model 6208, planet carrier bearing model 6212, and planet gear bearing model 6205. The gear material is 20CrMnTi, with a heat treatment hardness of HRC 58-62, and a tooth surface accuracy grade of 6. The frequencies in the characteristic sequences were analyzed, and the spectral characteristics of different bearing positions were compared. For the sun gear input bearing current characteristic sequence, the current amplitudes for main harmonic frequencies of 25Hz, 50Hz, and 75Hz were extracted, which were 120mA, 85mA, and 40mA, respectively. For the planetary carrier output bearing current characteristic sequence, the current amplitudes for main harmonic frequencies of 25Hz, 50Hz, and 75Hz were extracted, which were 80mA, 60mA, and 30mA, respectively. For the four planetary gear bearing current characteristic sequences, the current amplitudes for main harmonic frequencies of 25Hz, 50Hz, and 75Hz were extracted, which were 30mA, 20mA, and 10mA for planetary gear 1, 28mA, 19mA, and 9mA for planetary gear 2, 31mA, 21mA, and 11mA for planetary gear 3, and 29mA, 20mA, and 10mA for planetary gear 4. The effective current values for each bearing were calculated through time-domain integration. The effective current value for the sun gear input bearing was 150 mA, the effective current value for the planet carrier output bearing was 105 mA, and the effective current values for the four planet gear bearings were 38 mA, 35 mA, 39 mA, and 37 mA, respectively. A current flow network model was established based on the sun gear-planet gear-ring gear connection topology, treating each component in the gearbox as a node and the gear meshing pairs as conductive paths. In this model, current enters the sun gear input shaft, flows through the sun gear-planet gear meshing pair to each planet gear, then flows through the planet gear-ring gear meshing pair to the gearbox, and finally flows out through the planet carrier output shaft. Based on the measured current values for each bearing, network flow analysis was used to calculate the current distribution between each node.The analysis results show that the 150mA current flowing into the sun gear input bearing is distributed to the four planetary gears in proportions of 25%, 23%, 27%, and 25%, namely 37.5mA, 34.5mA, 40.5mA, and 37.5mA, respectively. Of the current received by the planetary gears, 80% flows to the housing through the planetary gear-annular gear meshing pair, and 20% flows directly to the planetary carrier through the planetary gear bearings. The main current path is sun gear input shaft → sun gear → planetary gear → annular gear → housing → planetary carrier → output shaft, and the current circulation ratio on this path is approximately 75%. The number of equivalent conductive contact points is calculated based on bearing type and size. For the sun gear input bearing (type 6208), there are 9 rolling elements, each forming a contact point with each inner and outer ring, for a total of 18 contact points. Considering load distribution, the actual number of equivalent conductive contact points is 5. For the planetary carrier output bearing (type 6212), there are 12 rolling elements and 7 equivalent conductive contact points. For the planetary gear bearing (type 6205), there are 8 rolling elements and 4 equivalent conductive contact points. The contact resistance range is calculated based on material resistivity, contact area, and lubricant film thickness: the sun gear input bearing contact resistance range is 15-25 ohms, the planetary carrier output bearing contact resistance range is 10-18 ohms, and the planetary gear bearing contact resistance range is 20-35 ohms. The number of contact points in a gear mesh depends on the contact line length and tooth width. The sun-planet mesh has eight equivalent contact points, with a contact resistance range of 8-15 ohms; the planet-ring mesh has ten equivalent contact points, with a contact resistance range of 6-12 ohms. Using the parallel circuit calculation principle and bearing current shunt characteristic data, the current distribution ratios for the sun-planet mesh and planet-ring mesh were estimated. First, the equivalent resistance of each component was calculated: the sun gear input bearing had an equivalent resistance of 20 ohms, the planet carrier output bearing had an equivalent resistance of 14 ohms, the planet bearings had an equivalent resistance of 28 ohms, the sun-planet mesh had an equivalent resistance of 12 ohms, and the planet-ring mesh had an equivalent resistance of 9 ohms. Based on these equivalent resistance values, the distribution ratio of the 150mA current flowing from the sun gear input shaft to each path is calculated: the current share of the sun gear-planet gear 1 meshing pair is 38mA (25.3% of the total current), the current share of the sun gear-planet gear 2 meshing pair is 36mA (24.0% of the total current), the current share of the sun gear-planet gear 3 meshing pair is 39mA (26.0% of the total current), and the current share of the sun gear-planet gear 4 meshing pair is 37mA (24.7% of the total current). Of the current received by the planetary gears, the current share of the planetary gear 1-ring gear meshing pair is 30.4mA, and the current share of the planetary gear 1 bearing is 7.6mA. The current distribution of the remaining planetary gears is estimated using similar ratios, ultimately generating the estimated current share data for the meshing pairs and bearings.

[0054] Preferably, in step S2, performing electro-corrosion damage assessment based on the internal electromagnetic disturbance snapshot data and the gearbox design parameters through the meshing pair-bearing estimated current share data includes:

[0055] Set the critical electrical corrosion damage threshold of gears / bearings according to gearbox design parameters;

[0056] Extract the instantaneous effective conductive contact area of each meshing point and the conductive area of the bearing roller / raceway contact point based on the gearbox design parameters;

[0057] The average current density on the tooth surface is calculated based on the estimated current share data of the meshing pair and the bearing and the instantaneous effective conductive contact area of each meshing point to obtain the tooth surface meshing current density data;

[0058] The bearing current density is estimated based on the estimated current share data of the meshing pair and the bearing and the conductive area of the bearing roller / raceway contact point, and the bearing current density distribution range is obtained;

[0059] Analyze the electrothermal-mechanical disturbance characteristics of the tooth surface based on the internal electromagnetic disturbance snapshot data and the tooth surface meshing current density data to generate the tooth surface electrothermal-mechanical disturbance data;

[0060] Based on the critical electro-corrosion damage threshold of gears / bearings, the electro-corrosion damage is evaluated using the electro-thermal-mechanical disturbance data of the tooth surface and the bearing current density distribution range to generate the bearing-tooth surface electro-corrosion damage ratio.

[0061] In this embodiment of the present invention, the critical electro-corrosion damage threshold is determined based on gearbox design parameters and a model that relates current density to electro-corrosion damage. For gears made of 20CrMnTi with a surface hardness of HRC58-62, the critical current density threshold is determined through accelerated electro-corrosion testing. During the test, pulsed currents were applied to gear samples at varying current densities for 500 hours. The damage initiation point was determined through surface roughness changes, microstructure analysis, and hardness testing. The test results indicate that the critical electro-corrosion current density threshold for gear tooth surfaces is 0.8 A / mm². Below this value, the tooth surface material exhibits no significant electro-corrosion damage. For bearings, the critical electro-corrosion current density threshold for rolling elements and raceways was determined to be 0.6 A / mm², based on the material (GCr15), heat treatment condition (HRC60-65), surface finishing process (grinding Ra 0.4 μm), and lubrication conditions (ISO VG320 gear oil). In addition, based on the analysis of long-term operating data, the time threshold for cumulative electro-corrosion damage is determined to be 3000 hours. If this time is exceeded and the current density exceeds the critical value, significant electro-corrosion damage will occur. The instantaneous effective conductive contact area of each meshing point is calculated based on the gear meshing theory and the Hertz contact theory. For the sun gear-planet gear meshing pair, the gear module is 4mm, the tooth width is 65mm, the pressure angle is 20 degrees, and the gear accuracy grade is 6. The Hertz contact equation is used to calculate the major axis of the contact ellipse of a single pair of teeth to be 2.8mm, the minor axis is 0.4mm, and the instantaneous contact area of a single pair of teeth is 3.52mm². Considering the overlap of 1.6, an average of 1.6 pairs of teeth are engaged simultaneously during the actual meshing process, and the total instantaneous effective contact area is 5.63mm². For the planetary gear-inner ring meshing pair, the major axis of the contact ellipse is 3.2mm, the minor axis is 0.45mm, the instantaneous contact area of a single pair of teeth is 4.52mm², the overlap is 1.8, and the total instantaneous effective contact area is 8.14mm². For the bearing contact points, the Hertz contact theory is used to calculate the area of each contact point based on the bearing model and load conditions: the major axis of the contact ellipse between the rolling element and the inner / outer ring of the sun gear input bearing (type 6208) is 1.2mm, the minor axis is 0.15mm, and the area of a single contact point is 0.57mm²; the contact point area of the planetary carrier output bearing (type 6212) is 0.85mm²; and the contact point area of the planetary gear bearing (type 6205) is 0.42mm². Considering the load distribution, the actual number of load-bearing contact points is 5, 7, and 4, respectively. The estimated current share data of the meshing pair-bearing is combined with the instantaneous effective conductive contact area of each meshing point to calculate the average current density on the tooth surface. The calculation formula is , where J represents current density (A / mm²), I represents current value (A), and A represents contact area (mm²). For the sun gear-planet gear 1 meshing pair, the estimated current share is 38 mA, the instantaneous effective contact area is 5.63 mm², and the calculated average current density on the tooth surface is 0.0675 A / mm². For the sun gear-planet gear 2 meshing pair, the estimated current share is 36 mA, the instantaneous effective contact area is 5.63 mm², and the calculated average current density on the tooth surface is 0.0639 A / mm². For the sun gear-planet gear 3 meshing pair, the estimated current share is 39 mA, the instantaneous effective contact area is 5.63 mm², and the calculated average current density on the tooth surface is 0.0693 A / mm². For the sun gear-planet gear 4 meshing pair, the estimated current share is 37 mA, the instantaneous effective contact area is 5.63 mm², and the calculated average current density on the tooth surface is 0.0657 A / mm². For the planetary gear-ring meshing pair, the calculated current density for planetary gear 1-ring meshing pair is 0.0373 A / mm², for planetary gear 2-ring meshing pair is 0.0353 A / mm², for planetary gear 3-ring meshing pair is 0.0381 A / mm², and for planetary gear 4-ring meshing pair is 0.0362 A / mm². These data are aggregated to form the tooth surface meshing current density data. The bearing current density is calculated based on the estimated current share data for the meshing pair-bearing and the conductive area of the bearing roller / raceway contact points. For the sun gear input bearing, the estimated current share is 150 mA, the number of effective contact points is 5, the area of each contact point is 0.57 mm², and the total contact area is 2.85 mm². The calculated average current density is 0.0526 A / mm². Taking into account the uneven load distribution, the current density actually exhibits an uneven distribution. The maximum contact point current density is 1.8 times the average, or 0.0947 A / mm², while the minimum contact point current density is 0.5 times the average, or 0.0263 A / mm², resulting in a current density distribution range of [0.0263 A / mm², 0.0947 A / mm²]. For the planetary carrier output bearing, the estimated current share is 105 mA, with seven effective contact points, each with an area of 0.85 mm², and a total contact area of 5.95 mm². The calculated average current density is 0.0176 A / mm², and the current density distribution range is [0.0088 A / mm², 0.0317 A / mm²].For the planetary gear bearings, the estimated current shares are 7.6mA, 7.2mA, 7.9mA, and 7.4mA, respectively. There are four effective contact points, each with an area of 0.42mm², for a total contact area of 1.68mm². The calculated average current densities for the four planetary gear bearings are 0.0045A / mm², 0.0043A / mm², 0.0047A / mm², and 0.0044A / mm², respectively, with a current density distribution range of [0.0022A / mm², 0.0085A / mm²]. The internal electromagnetic disturbance snapshot data is combined with the tooth surface meshing current density data to analyze the electrothermal-mechanical disturbance characteristics of the tooth surface. The electrothermal analysis uses the Joule heating model, calculated using the following formula: , where Q represents the heat per unit volume (J / mm³), ρ represents the material resistivity (Ω·mm), J represents the current density (A / mm²), and t represents the current duration (s). For a 20CrMnTi gear with a resistivity of 0.22Ω·mm, an average current density of 0.0666A / mm² in the sun-planet gear meshing pair, and a meshing time of 0.016s (calculated based on the gear speed and meshing angle), the heat generated by a single meshing is calculated to be: Considering the long-term operation of the gearbox, the accumulated heat will cause local temperature rise. The temperature rise of the meshing area is calculated to be 2.8℃ through the heat conduction model. The mechanical disturbance analysis is based on the magnetic field force model, and the calculation formula is , where F is force (N), B is magnetic induction intensity (T), I is current (A), and L is conductor length (mm). Based on the internal electromagnetic disturbance snapshot data, the average magnetic field intensity on the sun gear tooth surface is 30μT, the current is 38mA, and the contact wire length is 65mm. The additional electromagnetic force is calculated to be , which is almost negligible compared to the normal meshing load (about 1500N). The results of the electrothermal and mechanical disturbance analysis are combined to generate the electrothermal-mechanical disturbance data of the tooth surface, including the temperature rise value, electromagnetic force value and comprehensive disturbance level of each meshing pair. Based on the previously set critical electro-corrosion damage threshold (0.8A / mm² for gears and 0.6A / mm² for bearings), combined with the electro-thermal-mechanical disturbance data of the tooth surface and the bearing current density distribution range, an electro-corrosion damage assessment is performed. The assessment adopts the electro-corrosion damage rate model, and the calculation formula is: , where D is the damage rate (dimensionless, range 0-1), k is the material constant (experimentally determined to be 0.0015), and J is the actual current density (A / mm²). is the critical current density (A / mm²), a is the exponent (determined experimentally to be 2.5), and t is the cumulative time (h). For the sun gear-planet gear meshing pair, the average current density is 0.0666 A / mm², the critical current density is 0.8 A / mm², and assuming a cumulative operating time of 5000 hours, the calculated damage rate is 0.0055, accounting for 0.55% of the critical damage. For the planet gear-ring gear meshing pair, the average current density is 0.0367 A / mm², and the calculated damage rate is 0.0012, accounting for 0.12% of the critical damage. For the sun gear input bearing, the maximum current density is 0.0947 A / mm², the critical current density is 0.6 A / mm², and the calculated damage rate is 0.0385, accounting for 3.85% of the critical damage. For the planet carrier output bearing, the maximum current density is 0.0317 A / mm², and the calculated damage rate is 0.0024, accounting for 0.24% of the critical damage. For the planetary gear bearings, the maximum current density was 0.0085A / mm², resulting in a calculated damage rate of 0.00005, representing 0.005% of the critical damage threshold. Comprehensive assessment results indicate that at this current level, the sun gear input bearing faces the highest risk of electro-corrosion damage, but this risk remains well below the critical damage threshold. The electro-corrosion damage rates for all gearbox components remain within a safe range.

[0062] Of particular importance is the analysis of the electrothermal-mechanical disturbance characteristics of the tooth surface based on the internal electromagnetic disturbance snapshot data and the tooth surface meshing current density data.

[0063] The electrical conductivity of the tooth surface material is extracted through the gearbox design parameters, and the instantaneous Joule heat power of the meshing area is analyzed based on the tooth surface meshing current density data to obtain the Joule heat generation rate of the meshing area;

[0064] The thermal conductivity and specific heat capacity of the gear material and the heat dissipation boundary conditions of the gear body are extracted from the gearbox design parameters. The instantaneous local temperature rise in the tooth surface contact area is estimated based on the Joule heat generation rate in the meshing area to obtain the instantaneous temperature rise distribution data of the tooth surface.

[0065] The thermal expansion coefficient and elastic modulus of the gear material are extracted through the gearbox design parameters, and the corresponding local thermal stress of the tooth surface is calculated based on the instantaneous temperature rise distribution data of the tooth surface to generate the local thermal stress data of the tooth surface.

[0066] Based on the internal electromagnetic disturbance snapshot data and the tooth surface meshing current density data, the electromagnetic force acting on the gear is preliminarily estimated using the Lorentz force calculation formula, and then decomposed into the change in the gear axial force / radial force ratio to obtain the change in the electromagnetic-induced additional force ratio.

[0067] The maximum thermal stress value in the local thermal stress data of the tooth surface, the maximum current density value in the tooth surface meshing current density data, and the change in the electromagnetic-induced additional force ratio are sorted out in time series to generate the tooth surface electrothermal-mechanical disturbance data.

[0068] In the embodiment of the present invention, the gear material is extracted from the gear box design parameters as 20CrMnTi alloy steel. The conductivity of the material in the heat treatment state (quenching and tempering + carburizing and quenching, HRC58-62) is found in the material manual. (Equivalent to resistivity The conductivity is converted into SI units. According to the previously calculated tooth surface meshing current density data, the average current density of the sun gear-planet gear 1 meshing pair is 0.0675A / mm², which is converted to the international unit system. The calculation formula for Joule heat power density is: ,in represents power density (W / m³), represents the current density (A / m²), Indicates the conductivity ( ). Substituting the numerical calculation, the instantaneous Joule heat power density of the sun gear-planet gear 1 meshing pair is: The meshing area volume is the contact ellipse area (3.52mm²) multiplied by the effective conductive depth (estimated to be the depth of the material surface hardening layer 0.8mm), that is, Therefore, the total instantaneous Joule heat power in the meshing area is . The same method is used to calculate the instantaneous Joule heat power of other meshing pairs to form a complete Joule heat generation rate data table for the meshing area, including the instantaneous heat generation rate of all meshing pairs at different meshing positions. The thermal conductivity of 20CrMnTi alloy steel extracted from the gearbox design parameters is 42W / (m·K), the density is 7850kg / m³, and the specific heat capacity is 460J / (kg·K). The heat dissipation boundary conditions of the gear body are: the gear is immersed in ISOVG320 gear oil, the oil temperature is 60°C, and the heat convection coefficient is 800W / (m²·K). Based on the transient heat conduction model, the local temperature rise calculation formula of the tooth surface is: , where ΔT represents temperature rise (K), q represents heat power (W), t represents heat generation time (s), ρ represents material density (kg / m³), c represents specific heat capacity (J / (kg·K)), and V represents heated volume (m³). The instantaneous Joule heat power of the sun gear-planet gear 1 meshing pair is The single meshing contact time is 0.016s (calculated based on a gear speed of 1500rpm, 25 teeth and a meshing angle of 20°), and the heated volume is , substituted into the formula to calculate the instantaneous temperature rise caused by a single meshing is 0.0021K. Taking into account the heat accumulation effect, the temperature change during continuous meshing is calculated by numerically solving the heat conduction equation. The finite difference method is used, the time step is 0.001s, the spatial grid size is 0.1mm, and the calculation duration is 10s. The calculation results show that under steady-state conditions, the maximum temperature rise in the tooth surface contact area is 2.8K, and the average temperature rise is 1.5K. The temperature rise distribution is elliptical, with the highest temperature in the center and decreasing outward, forming the instantaneous temperature rise distribution data of the tooth surface. The thermal expansion coefficient of 20CrMnTi alloy steel is extracted from the gearbox design parameters as follows: , the elastic modulus is 210GPa, and the Poisson's ratio is 0.3. Based on the thermoelastic theory, the thermal stress calculation adopts the three-dimensional stress-strain relationship. Due to the uneven temperature distribution, the thermal stress calculation needs to consider the temperature gradient effect. A rectangular coordinate system is established with the center of the contact ellipse as the origin, the x-axis along the long axis of the contact ellipse, the y-axis along the short axis, and the z-axis perpendicular to the contact surface. The temperature distribution function is expressed as ,in represents the maximum temperature rise (2.8K), a, b, and c are the characteristic lengths of the temperature distribution in three directions (1.4mm, 0.2mm, and 0.4mm respectively). The thermal stress tensor calculation formula is: , where G represents the shear modulus (G=E / (2(1+ν))=80.8GPa), α represents the thermal expansion coefficient, and ΔT represents the temperature rise. represents the Kronecker function, ν represents the Poisson's ratio, and Represents the displacement gradient. By solving the displacement equation, the local thermal stress distribution on the tooth surface is calculated. The calculation results show that the maximum thermal stress occurs at the center of the contact ellipse, with a value of 25.2MPa, presenting a biaxial tensile state. The stress distribution is elliptical and decreases with increasing distance from the contact center. The stress distribution data is organized into a three-dimensional matrix form, which contains the three principal stress values of each grid point to form the local thermal stress data of the tooth surface. Based on the magnetic field intensity distribution and tooth surface meshing current density data in the internal electromagnetic disturbance snapshot data, the electromagnetic force acting on the gear is estimated by the Lorentz force calculation formula. The Lorentz force calculation formula is , where F represents force (N), J represents current density vector (A / m²), B represents magnetic induction intensity vector (T), and dV represents volume element (m³). For the sun gear-planet gear 1 meshing pair, the average current density on the tooth surface is , the magnetic field strength is , the current direction is tangential to the tooth surface, and the magnetic field direction is approximately perpendicular to the tooth surface. The volume of the contact area is , and the magnitude of the electromagnetic force is calculated as . This force is decomposed into axial and radial components: the axial component , radial component , where θ represents the pressure angle (20°). The normal gear meshing force is 1500N, of which the axial component is 513N and the radial component is 1410N. Calculate the change in the axial force / radial force ratio caused by the electromagnetic force: the original ratio is 513 / 1410=0.3638, and the ratio after adding the electromagnetic force is , the change is , which is almost negligible. The same method is used to calculate the variation of the electromagnetic-induced additional force ratio of other meshing pairs. The results show that the influence of electromagnetic force on the gear meshing force ratio is extremely small. Based on the actual operating conditions of the gearbox, the data of each minute during the 60-minute operation is recorded to form a time series feature data set. For the sun gear-planet gear 1 meshing pair, the maximum thermal stress value in the local thermal stress data of the tooth surface is extracted to form a time series {25.2, 25.3, 25.1, 25.4, ..., 25.2} MPa, a total of 60 data points; the maximum current density value in the tooth surface meshing current density data is extracted to form a time series {0.0675, 0.0678, 0.0672, 0.0680, ..., 0.0675} A / mm², a total of 60 data points; the variation of the electromagnetic-induced additional force ratio is extracted to form a time series , a total of 60 data points. Statistical features of these three time series were extracted, and the mean, standard deviation, maximum, minimum and coefficient of variation were calculated. The results showed that the mean of the maximum thermal stress was 25.25MPa, the standard deviation was 0.12MPa, and the coefficient of variation was 0.0048; the mean of the maximum current density was 0.0676A / mm², the standard deviation was 0.0003A / mm², and the coefficient of variation was 0.0044; the mean of the change in the ratio of electromagnetically induced additional force was , the standard deviation is , with a coefficient of variation of 0.0312. These statistical features were combined with the original time series data to generate the electrothermal-mechanical disturbance data for the sun gear-planet gear 1 meshing pair. The same method was used to process the data for the other meshing pairs, ultimately generating a complete dataset of electrothermal-mechanical disturbances for the tooth surfaces.

[0069] Preferably, the electrical corrosion damage assessment based on the critical electrical corrosion damage threshold of the gear / bearing using the electrothermal-mechanical disturbance data of the tooth surface and the bearing current density distribution range includes:

[0070] An ultrasonic oil film thickness sensor is installed in the lubricating oil channel near the planetary gear meshing area in the gearbox to measure the minimum oil film thickness of the lubricating oil channel in real time and obtain a real-time sampling value of the oil film thickness;

[0071] Calculate the minimum oil film thickness ratio in the critical area based on the real-time sampling value of the oil film thickness;

[0072] Based on the tooth surface electrothermal-mechanical disturbance data, the current cycle lubrication status analysis is performed on the minimum oil film thickness ratio in the critical area and the dynamic monitoring curve of the oil resistivity. The electro-erosion risk assessment of the meshing area is also performed to generate the meshing area working condition severity index.

[0073] Based on the tooth surface meshing current density data and the meshing area working condition severity index, the preset gear material electrocorrosion rate correlation table is used to estimate the tooth surface equivalent micro-electrocorrosion pit density increment generated on the tooth surface during the current monitoring period;

[0074] Estimate the density increment of the equivalent micro-electrolytic pits of the bearing raceway generated by the bearing during the current monitoring period according to the maximum current density estimated value in the bearing current density distribution interval;

[0075] The equivalent micro-electrolytic pit density increments of the tooth surface and the bearing raceway are periodically accumulated and divided by the corresponding gear / bearing critical electrolytic damage threshold to obtain the bearing-tooth surface electrolytic damage ratio.

[0076] In this embodiment of the present invention, a UT-8000 ultrasonic oil film thickness sensor is installed in the lubricating oil channel near the meshing area of the planetary gears in the gearbox. The sensor has an 8mm diameter, a measurement range of 0.1-500μm, and an accuracy of ±0.05μm. The sensor is mounted on the oil channel wall 20mm below the meshing point between each planetary gear and the inner ring gear. The sensor probe is flush with the inner wall of the oil channel and is polished to a surface roughness of less than Ra 0.2μm. The sensor has a transmission frequency of 10MHz and a pulse repetition frequency of 1000Hz, and uses the longitudinal wave ultrasonic measurement principle. The sensor is mounted on the gearbox housing via a dedicated sealing joint with an M12×1.5 threaded connection, and the torque is controlled at 25N·m. The sensor signal is connected to a UM-2000 ultrasonic oil film meter via a shielded cable. The sampling frequency is set to 100Hz, and each sampling duration is 0.1ms. The data is collected continuously for 60 minutes to generate time-series data on the oil film thickness. The data recording format is [timestamp (s), oil film thickness (μm)]. The measured data is digitally filtered with a filter bandwidth of 10-1000Hz to eliminate the influence of high-frequency noise. Based on the real-time sampling value of the oil film thickness, the minimum oil film thickness ratio in the critical area is calculated using the elastohydrodynamic lubrication theory. The calculation formula is: , where λ represents the minimum oil film thickness ratio (dimensionless), Indicates the minimum oil film thickness measured (μm), Indicates the arithmetic mean roughness of the gear tooth surface (μm), Indicates the arithmetic mean roughness of the inner ring gear tooth surface (μm). The arithmetic mean roughness of the gear tooth surface is extracted from the gearbox design parameters. =0.4μm, arithmetic mean roughness of the inner ring tooth surface =0.5μm, and we can calculate it by substituting it into the formula. =0.64μm. Taking planetary gear 1 as an example, the minimum oil film thickness measured by the ultrasonic sensor is 2.8μm, and the minimum oil film thickness ratio λ=2.8 / 0.64=4.38 is calculated. For all planetary gears, the corresponding minimum oil film thickness ratios are calculated respectively. The minimum oil film thickness ratio of planetary gear 2 is 4.22, the minimum oil film thickness ratio of planetary gear 3 is 4.53, and the minimum oil film thickness ratio of planetary gear 4 is 4.31. Based on the elastohydrodynamic lubrication theory, λ<1 indicates boundary lubrication, 1≤λ<3 indicates mixed lubrication, and λ≥3 indicates complete fluid lubrication. Therefore, all planetary gear meshing areas are currently in a state of complete fluid lubrication. Based on the previously obtained tooth surface electrothermal-mechanical disturbance data, the lubrication state analysis is performed in combination with the minimum oil film thickness ratio in the critical area. First, the dynamic monitoring data of the oil resistivity is extracted from the gearbox monitoring system. The current resistivity of ISOVG320 gear oil is , monitored in real time by an online oil monitor. The electrothermal-mechanical disturbance data for the tooth surface showed that the average maximum thermal stress of the sun gear-planet gear 1 meshing pair was 25.25 MPa, and the average maximum current density was 0.0676 A / mm². Combining these data with the minimum oil film thickness ratio λ = 4.38, a lubrication condition assessment model was constructed. The assessment model uses a weighted factor method, calculated using the formula: , where RI represents the meshing area working condition severity index (dimensionless), λ represents the actual minimum oil film thickness ratio, represents the benchmark minimum oil film thickness ratio (value is 3.0), J represents the actual current density (A / mm²), represents the reference current density (the value is 0.05A / mm²), σ represents the actual thermal stress (MPa), represents the benchmark thermal stress (valued at 20MPa), are weight coefficients (determined experimentally to be 0.5, 0.3, and 0.2). Substituting numerical values into the calculation, the severity index of the sun gear-planet gear 1 meshing pair is 0.63. The same method is used to calculate the severity index of the other meshing pairs: 0.65 for planet gear 2-ring gear, 0.60 for planet gear 3-ring gear, and 0.62 for planet gear 4-ring gear. The preset correlation table for the electrocorrosion rate of gear materials was obtained through laboratory accelerated electrocorrosion testing. During the test, current was applied to 20CrMnTi gear material samples at different current densities (0.01-1A / mm²) and different lubrication conditions (oil film thickness ratio of 1-10) for 100 hours. The number and size of the micro-electrocorrosion pits generated on the surface were then observed and counted using a microscope. A relationship model between the electrocorrosion pit density, current density, and the severity index was established. The data structure of the association table is [current density (A / mm²), operating severity index, pit density increment (number / mm²·h)]. For example, [0.05, 0.5, 0.8] indicates that under the conditions of a current density of 0.05 A / mm² and a severe operating index of 0.5, 0.8 micro pits are generated per square millimeter per hour. The pit density increment under the current conditions is calculated through interpolation. For the sun gear-planet gear 1 meshing pair, the current density is 0.0676 A / mm² and the severe operating index is 0.63. Bilinear interpolation calculates the pit density increment to be 1.25 numbers / mm²·h. Given a 60-minute monitoring period, the equivalent micro pit density increment generated on the tooth surface of this meshing pair during this period is 1.25 numbers / mm²·h × 1h = 1.25 numbers / mm². The same method is used to calculate the increment of the electro-corrosion pit density of other meshing pairs: planetary gear 2-inner gear is 1.30 / mm², planetary gear 3-inner gear is 1.20 / mm², and planetary gear 4-inner gear is 1.23 / mm². The maximum current density estimate is extracted from the previously calculated bearing current density distribution range: the sun gear input bearing is 0.0947A / mm², the planetary carrier output bearing is 0.0317A / mm², and the planetary gear bearing is 0.0085A / mm². The increment of the equivalent micro-electro-corrosion pit density of the bearing raceway is calculated by the bearing material electro-corrosion model, which is established based on the electro-corrosion characteristics test of GCr15 bearing steel. In the model, the calculation formula for the electro-corrosion pit density increment is: , where N is the pit density increment (pieces / mm²), k is the material constant (determined to be 25 by experiment), J is the current density (A / mm²), α is the exponent (determined to be 1.8 by experiment), t is the time (h), and f(λ) is the lubrication coefficient (a function related to the minimum oil film thickness ratio, . For the sun gear input bearing, the maximum current density is 0.0947A / mm², the lubrication coefficient f(λ)=exp(-0.5×4.38)=0.114, and the monitoring period is 1 hour. Substituting the formula into the equation, the calculated increment of the electro-corrosion pit density is 25×(0.0947)^1.8×1×0.114=0.235 pieces / mm². For the planetary carrier output bearing, the maximum current density is 0.0317A / mm², and the lubrication coefficient is the same. The calculated increment of the electro-corrosion pit density is 0.031 pieces / mm². For the planetary gear bearing, the maximum current density is 0.0085A / mm², and the calculated increment of the electro-corrosion pit density is 0.002 pieces / mm². The accumulation of the electro-corrosion pit density is implemented using a cyclically updated accumulator. The initial value of the accumulator is set to zero. After each monitoring period, the increment of the electro-corrosion pit density of the current period is added to the accumulator. In specific implementation, a data structure [component name, cumulative operating time (h), cumulative pit density (pits / mm²)] is established to record the cumulative electrocorrosion damage of each component. Taking the sun gear-planet gear 1 meshing pair as an example, assuming the current cumulative operating time is 1000 hours, the previous cumulative pit density was 1250 pits / mm², and the increment in this cycle is 1.25 pits / mm², the updated cumulative pit density is 1251.25 pits / mm². For the sun gear input bearing, assuming the current cumulative operating time is 1000 hours, the previous cumulative pit density was 200 pits / mm², and the increment in this cycle is 0.235 pits / mm², the updated cumulative pit density is 200.235 pits / mm². The critical electrocorrosion damage threshold for gears determined from material testing is 2000 pits / mm², and the critical electrocorrosion damage threshold for bearings is 500 pits / mm². Calculating the damage percentage: The electro-erosion damage percentage of the sun gear-planet gear 1 meshing pair is 1251.25 / 2000 = 0.6256 (62.56%), and the electro-erosion damage percentage of the sun gear input bearing is 200.235 / 500 = 0.4005 (40.05%). The same method is used to calculate the electro-erosion damage percentages of other components: 62.87% for the planet gear 2-ring gear, 61.10% for the planet gear 3-ring gear, 61.54% for the planet gear 4-ring gear, 6.23% for the planet carrier output bearing, and 0.41% for the planet gear bearings. A radar chart of the electro-erosion damage percentage is generated to visually display the damage severity of each component and predict the remaining time until each component reaches its critical damage threshold under continued operation under the current operating conditions.

[0077] Preferably, based on the tooth surface electrothermal-mechanical disturbance data, the current cycle lubrication status analysis is performed on the minimum oil film thickness ratio in the critical area and the dynamic monitoring curve of the oil resistivity, and the electro-erosion risk assessment of the meshing area is performed, including:

[0078] When the minimum oil film thickness ratio in the critical area is continuously greater than 2.0, or the maximum current density value in the electrothermal-mechanical disturbance data of the tooth surface is less than 0.05A / mm², the boundary lubrication coefficient is set to a high value and the electro-erosion risk level is set to a low value, indicating good lubrication, and low-risk lubrication-electro-erosion status data is generated;

[0079] When the minimum oil film thickness ratio in the critical area is between 1.0 and 2.0, and the maximum current density value in the electrothermal-mechanical disturbance data of the tooth surface is higher than 0.1A / mm², the boundary lubrication coefficient is set to the medium value, the electrocorrosion risk level is set to medium, and the medium-risk lubrication-electrocorrosion state data is generated;

[0080] When the minimum oil film thickness ratio in the critical area is less than 1.0, the boundary lubrication coefficient is set to a low value, the electro-corrosion risk level is set to high, and high-risk lubrication-electro-corrosion state data is generated;

[0081] Based on the low-risk lubrication-electroerosion state data, medium-risk lubrication-electroerosion state data, and high-risk lubrication-electroerosion state data, the weighted index of the severity of the current meshing area is quantified through the maximum thermal stress value and the change in the electromagnetic-induced additional force ratio in the tooth surface electrothermal-mechanical disturbance data to generate the meshing area working condition severity index.

[0082] In this embodiment of the present invention, data recorded by the real-time monitoring system showed that the minimum oil film thickness ratio in the critical area of planetary gear 1 was 4.38, exceeding the threshold of 2.0 for 120 minutes. Simultaneously, the maximum current density in the electrothermal-mechanical disturbance data on the tooth surface was 0.0676 A / mm². Based on pre-set judgment logic, the system set the boundary lubrication coefficient to 0.9 (a high value, ranging from 0 to 1), indicating good lubrication conditions. The electrocorrosion risk assessment module set the risk level to 1 (low risk, ranging from 1 to 5). The data processing unit generated low-risk lubrication-electrocorrosion status data with the structure [bearing / meshing pair ID, timestamp, minimum oil film thickness ratio, maximum current density, boundary lubrication coefficient, risk level]. Low-risk lubrication-electrocorrosion status data was also generated for planetary gears 2, 3, and 4, as their minimum oil film thickness ratios were all greater than 2.0 (4.22, 4.53, and 4.31, respectively). The system stored this data in the monitoring database and displayed a green indicator on the monitoring interface, indicating good lubrication conditions. The low-risk lubrication-corrosion status data also includes the oil resistivity and records. In the simulated high-load working condition test, the minimum oil film thickness ratio in the critical area of planetary gear 1 dropped to 1.8, which is in the medium lubrication range of 1.0 to 2.0, and lasted for 30 minutes. At the same time, the electrothermal-mechanical disturbance data of the tooth surface showed that the maximum current density value rose to 0.12A / mm², exceeding the threshold of 0.1A / mm². According to the preset judgment criteria, the system sets the boundary lubrication coefficient to 0.5 (median value, range 0-1), indicating that the lubrication state is at a medium level. The electro-corrosion risk assessment module sets the risk level to 3 (medium risk, range 1-5). The data processing unit generates medium-risk lubrication-electro-corrosion state data, and the data structure is [bearing / meshing pair ID, timestamp, minimum oil film thickness ratio, maximum current density, boundary lubrication coefficient, risk level, oil resistivity]. At this time, the oil resistivity has dropped slightly to , indicating slight oil degradation. The system also generated medium-risk lubrication-electrocorrosion data for other planetary gears (e.g., planetary gear 2, with a minimum oil film thickness ratio of 1.7 and a maximum current density of 0.115 A / mm²). A yellow warning light appeared on the monitoring interface, indicating a high-risk lubrication condition. Under this medium-risk condition, the system increased data collection frequency from the standard 60-second interval to every 15 seconds. During extreme operating conditions testing, the minimum oil film thickness ratio in the critical area of planetary gear 1 dropped sharply to 0.85, below the critical value of 1.0, for 10 minutes, indicating that the gear mesh had entered a boundary lubrication state. Electrothermal-mechanical disturbance data on the tooth surface showed a further increase in maximum current density to 0.18 A / mm². The system immediately set the boundary lubrication coefficient to 0.2 (low value, on a scale of 0-1), indicating a critical lubrication condition. The electrocorrosion risk assessment module set the risk level to 5 (high risk, on a scale of 1-5). The data processing unit generates high-risk lubrication-electrocorrosion status data with the data structure of [bearing / meshing pair ID, timestamp, minimum oil film thickness ratio, maximum current density, boundary lubrication coefficient, risk level, oil resistivity, temperature]. Additional parameters recorded at this time include oil resistivity reduced to , the oil temperature rises to 75°C. The system also generates high-risk lubrication-electro-erosion status data for other planetary gears (the minimum oil film thickness ratio of planetary gear 2 is 0.88, planetary gear 3 is 0.90, and planetary gear 4 is 0.87). The monitoring interface displays a red alarm light and triggers an audible alarm signal. The system enters high-frequency data acquisition mode, and the sampling frequency is increased to once every 5 seconds. At the same time, the magnetic field neutralization device is started to try to reduce the current density in the gear meshing area. The meshing area working condition severity index is calculated based on the key parameters in the tooth surface electrothermal-mechanical disturbance data. The calculation formula is: , where WI represents the meshing zone working condition severity index (dimensionless, range 0-10), BL represents the boundary lubrication coefficient (0.9, 0.5, and 0.2 for low, medium, and high risk conditions, respectively), and σ represents the maximum thermal stress value (MPa). Indicates the reference thermal stress value (take 20MPa), Indicates the change in the ratio of electromagnetically induced additional force, Indicates the reference change (take ), represents the minimum oil film thickness ratio, represents the reference oil film thickness ratio (taken as 2.0), α, β, γ, δ represent weight coefficients (determined by regression analysis to be 0.4, 0.3, 0.1, 0.2). For planetary gear 1 under normal operating conditions (low risk state), σ = 25.25 MPa, , λ = 4.38, which is calculated to be 0.87. For medium-risk conditions, WI = 3.25; for high-risk conditions, WI = 7.83. The system stores these severity index values in a database and sets different maintenance warning thresholds based on the index size: WI < 3 indicates normal operation, 3 ≤ WI < 6 indicates the warning range, and WI ≥ 6 indicates the critical range, requiring immediate maintenance measures.

[0083] Preferably, step S3 includes the following steps:

[0084] Step S31: installing high-bandwidth torque sensors at the input shaft flange and output shaft spline of the gearbox, respectively, synchronously collecting dynamic signals of input and output torques, and obtaining a double-end torque time domain series;

[0085] Step S32: Installing a three-axis high-frequency acceleration sensor on the planetary carrier bearing seat and the sun gear bearing seat of the gearbox to synchronously collect multi-point and multi-directional vibration signals to obtain multi-channel vibration data;

[0086] Step S33: extracting the amplitude and phase of the mechanical load harmonic torque component according to the double-end torque time domain sequence to generate a harmonic torque component spectrum;

[0087] Step S34: performing spectrum analysis on the multi-channel vibration data, and screening electromagnetic excitation components whose corresponding vibration response amplitudes exceed a preset noise threshold through potential electromagnetic excitation frequency data in the internal electromagnetic disturbance snapshot data, to obtain effective electromagnetic vibration response frequency points;

[0088] Step S35: identifying high-risk vibration coupling components through harmonic torque component spectra and effective electromagnetic vibration response frequencies, and generating high-risk coupling vibration data for each component;

[0089] Step S36: analyzing the gearbox contact stress history based on the harmonic torque component spectrum, and performing coupled vibration enhancement using the high-risk coupled vibration data of each component to generate a coupled vibration enhanced stress history;

[0090] Step S37: Calculate the component fatigue life reduction factor based on the proportion of electrical corrosion damage on the bearing-tooth surface, and perform linear damage accumulation processing based on the coupled vibration enhanced stress history to obtain the fatigue damage contribution value of each component in the cycle.

[0091] In an embodiment of the present invention, a high-bandwidth torque sensor of model TM-5000 is installed at the input shaft flange of the gearbox, with a rated range of 5000N·m, an accuracy level of 0.1, and a frequency response range of 0-5kHz. The sensor is installed using a standard flange connection method, using 8 M12×1.5 high-strength bolts, and the bolt torque is controlled at 85N·m. A torque sensor of model TM-8000 is installed at the output shaft spline, with a rated range of 8000N·m. It is fixed using a coaxial spline connection method, and molybdenum-based grease is applied to the connection surface to reduce contact friction. The two sensors are respectively connected to the high-speed data acquisition system through a signal conditioning unit. The acquisition card model is NI-9234, the sampling frequency is set to 20kHz, the acquisition accuracy is 24 bits, and the voltage range is ±5V. Data acquisition lasted 60 seconds. A synchronous trigger signal was provided by a photoelectric encoder. The two-channel torque signals were processed through a low-pass filter (cutoff frequency 5 kHz) to form a two-terminal torque time-domain sequence. Data was stored in the format [time (s), input torque (N·m), output torque (N·m)]. Four PCB-356A33 triaxial high-frequency accelerometers were installed on the planetary carrier bearing seat of the gearbox. These sensors have a sensitivity of 100 mV / g, a measurement range of ±50 g, and a frequency response of 0.5-10 kHz. The sensors were secured to a pre-machined surface on the outer surface of the bearing seat using specialized screws (M5×0.8). The mounting torque was controlled at 5 N·m, and thermal grease was applied to the mounting surface to improve thermal coupling between the sensor and the mounting surface. Two sensors of the same model were installed on either side of the sun gear bearing seat to monitor axial and radial vibrations, respectively. Each sensor provides vibration signals in the X, Y, and Z directions, for a total of 18 vibration signal channels. The signal was connected to a DAS-8500 multi-channel data acquisition system via shielded cables. The sampling frequency was set to 25.6 kHz, and each channel underwent independent A / D conversion with a resolution of 16 bits. During data acquisition, the gearbox maintained stable operation with an input speed of 1500 rpm and a load of 75% of the rated load. The collected vibration signals were bandpass filtered (20 Hz-10 kHz) to form a multi-channel vibration data matrix with the structure [time point, sensor 1-X, sensor 1-Y, sensor 1-Z, ..., sensor 6-Z]. The acquired double-ended torque time-domain series was subjected to fast Fourier transform (FFT) spectral analysis using a Hanning window with a window length of 4096 points and a window overlap of 50%. The input shaft torque signal was first normalized to convert the time-domain signal into the angular domain to eliminate the influence of speed fluctuations. The complex spectrum was obtained after the FFT calculation, and amplitude and phase information was extracted.Based on the gearbox's transmission structure (25 sun gear teeth, 35 planetary gear teeth, and 95 ring gear teeth), key harmonic frequencies were calculated: a sun gear rotation frequency of 25 Hz, a meshing fundamental frequency of 625 Hz (25 × 25 Hz), and harmonic frequencies (1250 Hz, 1875 Hz, and so on). For each harmonic frequency, the corresponding amplitude and phase were extracted from the spectrum. For the input shaft torque, the fundamental frequency amplitude was 120 N·m and the phase was 32 degrees; the second harmonic amplitude was 85 N·m and the phase was 128 degrees; and the third harmonic amplitude was 42 N·m and the phase was 225 degrees. The same process was performed on the output shaft torque, extracting the harmonic components of the corresponding frequencies. The extracted harmonic component amplitudes and phases were combined to form a harmonic torque component spectrum, with the data structure being [harmonic order, input torque amplitude (N·m), input torque phase (degrees), output torque amplitude (N·m), output torque phase (degrees)]. Fast Fourier transform (FFT) spectral analysis was performed on the acquired multi-channel vibration data. A flat-top window with an 8192-point window length and a 75% overlap was used to achieve higher amplitude accuracy. Spectra were calculated for each sensor in the X, Y, and Z directions, generating 18 spectral data sets. The preset noise threshold was determined by analyzing the vibration signal of the gearbox during no-load operation and was 2.5 times the average value of the vibration spectrum, resulting in a value of 0.15g. Potential electromagnetic excitation frequencies were extracted from the acquired internal electromagnetic disturbance snapshot data, including 25Hz (sun gear speed frequency), 50Hz (power supply frequency), 75Hz (the sum of the power supply frequency and sun gear speed frequency), and 625Hz (gear meshing frequency). For each of these potential electromagnetic excitation frequencies, the amplitude of the corresponding frequency point in the vibration spectrum was searched. If the amplitude exceeded the preset noise threshold of 0.15g, a valid electromagnetic vibration response was considered to exist at that frequency point. The screening results showed that at the 25Hz frequency point, five of the six sensors detected vibration responses exceeding the threshold; at the 625Hz frequency point, all sensors detected a significant vibration response. These effective electromagnetic vibration response frequencies and their amplitudes are recorded as a data structure consisting of [frequency (Hz), sensor ID, direction, amplitude (g)]. A characteristic frequency table is established for each component of the gearbox transmission chain, including the characteristic frequencies of the sun gear, planet gears, ring gear, planet carrier, and bearings. This table is based on the gearbox's structural parameters and kinematic analysis. For example, the sun gear rotational frequency is 25 Hz, the planet gear rotational frequency is 7.14 Hz, the planet carrier rotational frequency is 2.5 Hz, and the sun-planet gear meshing frequency is 625 Hz. The harmonic torque component spectrum is analyzed for significant harmonics (amplitudes greater than 1.5 times the average) and the effective electromagnetic vibration response frequencies. If a component's characteristic frequency appears in both the harmonic torque component spectrum and the effective electromagnetic vibration response frequency, and the amplitude ratio (vibration amplitude / torque amplitude) exceeds a preset threshold of 0.025 g / N·m, the component is identified as a high-risk vibration coupling component.The analysis results show that the sun gear-planet gear 1 meshing area, the sun gear input bearing, and the planet gear 1 bearing are high-risk vibration coupling components. For each high-risk component, its characteristic frequency, torque harmonic amplitude, vibration response amplitude, and their ratios were recorded to generate high-risk coupling vibration data for each component. Hertz contact theory was used to calculate the contact stress history during gear meshing. The calculation formula is: , where σH represents the contact stress (MPa), ZE is the material elastic coefficient (taken as 200MPa), Ft is the tangential force (N) (calculated from the torque and gear geometry parameters), b is the tooth width (mm), d is the pitch circle diameter (mm), and i is the contact ratio. The harmonics of each order in the harmonic torque component spectrum are synthesized into a time domain torque signal, which is then converted into the tooth surface contact force, and the contact stress time domain history is further calculated. For the sun gear-planet gear 1 meshing area, the basic contact stress is 1250MPa. The high-risk coupling vibration data of each component is used to enhance the contact stress by superimposing the additional stress caused by vibration on the basic contact stress. The additional stress calculation formula is: , where σa represents the additional stress (MPa), kv is the vibration-stress conversion coefficient (determined by experiment to be 0.15MPa·s² / m), a is the vibration acceleration amplitude (m / s²), m is the equivalent mass (kg), ω is the vibration angular frequency (rad / s), and A is the contact area (mm²). For the sun gear-planet gear 1 meshing area, the additional stress caused by vibration is calculated to be 75MPa, and the maximum contact stress after coupled vibration enhancement is 1325MPa, forming a complete coupled vibration enhanced stress history. The fatigue life reduction factor of the component is calculated based on the proportion of bearing-tooth surface electro-erosion damage. The reduction factor calculation formula is: , where Rf represents the fatigue life reduction factor, D represents the electrocorrosion damage fraction (a decimal between 0 and 1), and n represents the material sensitivity index (determined experimentally to be 0.5). For the sun gear / planet gear 1 meshing pair, the electrocorrosion damage fraction is 0.6256, resulting in a calculated fatigue life reduction factor of 1.65. For the sun gear input bearing, the electrocorrosion damage fraction is 0.4005, resulting in a fatigue life reduction factor of 1.29. Fatigue damage is calculated using the linear damage accumulation theory (Miner's criterion) based on the coupled vibration-enhanced stress history. The damage contribution per stress cycle is calculated as di = 1 / Ni, where di represents the damage contribution per cycle and Ni represents the fatigue life at that stress level (determined by the SN curve). For the sun gear / planet gear 1 meshing pair, the SN curve parameters are σf' = 1500 MPa and b = -0.085, resulting in a fatigue life of 10^7 cycles at a stress level of 1325 MPa. During the current monitoring cycle (60 minutes), the number of meshing engagements is 1.5 × 10^6. The fatigue damage contribution for a single monitoring cycle is 1.5 × 10^6 / 10^7 = 0.15. Taking into account the impact of electrolytic corrosion damage, the actual fatigue damage contribution is 0.15 × 1.65 = 0.2475. Perform the same calculation for each high-risk component to obtain the fatigue damage contribution for each component during the cycle.

[0092] Preferably, identifying high-risk vibration coupling components through harmonic torque component spectra and effective electromagnetic vibration response frequencies includes:

[0093] The vibration frequency of the harmonic torque component spectrum is aligned through the effective electromagnetic vibration response frequency point, and the mechanical natural vibration response amplitude is analyzed to obtain the dominant frequency response amplitude of the mechanical resonance;

[0094] The effective electromagnetic vibration response frequency point is matched with the dominant frequency response amplitude of the mechanical resonance. When the frequency difference between the two is less than the preset coupling identification bandwidth, it is marked as a potential coupling pair to obtain the potential electromechanical coupling frequency pair.

[0095] For a potential electromechanical coupling frequency pair, calculate the ratio of its vibration amplitude at the electromagnetic excitation frequency to the baseline vibration amplitude of the corresponding mechanical natural frequency in the absence of significant electromagnetic excitation, as the vibration amplification factor of the coupling pair;

[0096] Multiply the vibration amplification factor by the corresponding bearing-tooth surface electro-erosion damage ratio, and screen out coupling pairs that exceed the preset risk threshold to generate high-risk coupled vibration events.

[0097] The gearbox components are mapped through high-risk coupled vibration events to generate high-risk coupled vibration data for each component.

[0098] In an embodiment of the present invention, a frequency lookup table is constructed, and the effective electromagnetic vibration response frequency points are arranged in ascending order of frequency value to form a frequency sequence [25Hz, 50Hz, 75Hz, 625Hz, 1250Hz, 1875Hz]. Then, the same or closest frequency points are extracted from the harmonic torque component spectrum to form a corresponding torque harmonic component sequence. During the frequency alignment process, the frequency matching tolerance is set to 0.5Hz. For frequency points that cannot be accurately matched, the corresponding torque value is calculated using a linear interpolation method. After completing the frequency alignment, the mechanical natural vibration response amplitude analysis is performed. The specific method is to obtain the mechanical system transfer function through an impact hammer test under static conditions of the gearbox and determine the key natural frequencies. In this example, the main natural frequencies determined by the test are 612Hz, 1263Hz and 1854Hz. By comparing the effective electromagnetic vibration response frequency points, the vibration response amplitudes at these natural frequencies are calculated to be 2.35g, 1.86g and 0.74g, respectively. These values constitute the mechanical resonance dominant frequency response amplitude data set. For each natural frequency point in the mechanical resonance dominant frequency response amplitude dataset, a preset coupling identification bandwidth of 15 Hz was set. This bandwidth value was determined through statistical analysis of vibration frequency offsets observed in historical operating data. Taking the natural frequency of 612 Hz as an example, the frequency differences between it and each valid electromagnetic vibration response frequency point were calculated: |612-25| = 587 Hz, |612-50| = 562 Hz, |612-75| = 537 Hz, |612-625| = 13 Hz, |612-1250| = 638 Hz, and |612-1875| = 1263 Hz. Because the frequency difference between 612 Hz and 625 Hz is 13 Hz, which is less than the preset coupling identification bandwidth of 15 Hz, the natural frequency of 612 Hz and the electromagnetic vibration response frequency point of 625 Hz are marked as potential coupling pairs. Similarly, for the natural frequency of 1263 Hz, the frequency difference between it and the electromagnetic vibration response frequency of 1250 Hz is 13 Hz, which is also less than the coupling identification bandwidth and is therefore marked as a potential coupling pair. For the natural frequency of 1854 Hz, the frequency difference between it and the electromagnetic vibration response frequency of 1875 Hz is 21 Hz, which is greater than the coupling identification bandwidth and is therefore not marked as a potential coupling pair. Ultimately, two potential electromechanical coupling frequency pairs were identified: [612 Hz, 625 Hz] and [1263 Hz, 1250 Hz]. Their frequency differences, vibration response amplitudes, and torque harmonic amplitudes were recorded. Baseline vibration amplitudes were obtained—that is, the vibration amplitude at the mechanical natural frequency in the absence of significant electromagnetic excitation. Baseline vibration data were obtained from vibration signals collected while the gearbox was operating under electrical isolation (power disconnected and mechanically driven). Under these conditions, the baseline vibration amplitudes measured at the natural frequency of 612 Hz were 0.35 g, and at the natural frequency of 1263 Hz were 0.28 g.For the first coupling frequency pair [612 Hz, 625 Hz], the vibration amplitude at the 625 Hz electromagnetic excitation frequency was 2.35 g, and the calculated vibration amplification factor was 2.35 g / 0.35 g = 6.71. For the second coupling frequency pair [1263 Hz, 1250 Hz], the vibration amplitude at the 1250 Hz electromagnetic excitation frequency was 1.86 g, and the calculated vibration amplification factor was 1.86 g / 0.28 g = 6.64. The vibration amplification factor reflects the enhancement effect of electromagnetic excitation on mechanical vibration; larger values indicate a more significant impact of electromagnetic excitation on mechanical vibration. These vibration amplification factors are recorded together with the corresponding potential electromechanical coupling frequency pairs to form a coupled frequency pair dataset with amplification factor attributes. Data on the percentage of electrical erosion damage on the tooth surfaces of the bearings of various components show that the sun gear-planet gear 1 meshing pair has a value of 0.6256, the sun gear input bearing has a value of 0.4005, and the planet gear 1 bearing has a value of 0.0041. Frequency characteristic analysis determined that the first pair of coupling frequencies (612Hz, 625Hz) primarily affects the sun gear-planet gear 1 meshing pair, while the second pair of coupling frequencies (1263Hz, 1250Hz) primarily affects the sun gear input bearing. The comprehensive risk factor (R / F) is calculated using the formula RF = AF × D, where RF represents the comprehensive risk factor, AF represents the vibration amplification factor, and D represents the percentage of electrocorrosion damage. For the first pair of coupling frequencies, the calculated comprehensive risk factor is 6.71 × 0.6256 = 4.20; for the second pair, the calculated comprehensive risk factor is 6.64 × 0.4005 = 2.66. A preset risk threshold of 2.5, determined through analysis of historical failure cases, is set. When the comprehensive risk factor exceeds this threshold, the corresponding coupling pair is marked as a high-risk coupled vibration event. Therefore, the sun gear-planet gear 1 meshing pair corresponding to the first coupling frequency pair [612Hz, 625Hz] and the sun gear input bearing corresponding to the second coupling frequency pair [1263Hz, 1250Hz] were both identified as high-risk coupled vibration events. Their frequency characteristics, vibration amplification factors, electro-corrosion damage percentage, and comprehensive risk factors were recorded. Based on the identified high-risk coupled vibration events, a gearbox component mapping table was established. The mapping table contains component names, associated high-risk coupling frequency pairs, vibration amplification factors, electro-corrosion damage percentages, comprehensive risk factors, and vibration sensor location information. For the sun gear-planet gear 1 meshing pair, the associated high-risk coupling frequency pair is [612Hz, 625Hz], the vibration amplification factor is 6.71, the electro-corrosion damage percentage is 0.6256, the comprehensive risk factor is 4.20, and the associated vibration sensors are sensors 1 and 2. For the sun gear input bearing, the associated high-risk coupling frequency pair is [1263 Hz, 1250 Hz], the vibration amplification factor is 6.64, the proportion of electro-corrosion damage is 0.4005, the overall risk factor is 2.66, and the relevant vibration sensors are sensors 5 and 6. Based on the spatial location of each component in the gearbox, a 3D component map was created, annotating the locations of high-risk components and their corresponding overall risk factors.Finally, the high-risk coupled vibration data of each component is formed, and the data structure is [component ID, component name, coupling frequency pair, vibration amplification factor, electrical corrosion damage ratio, comprehensive risk factor, and associated sensor ID].

[0099] Preferably, the present invention further provides a gearbox life assessment system for executing the gearbox life assessment method described above, the gearbox life assessment system comprising:

[0100] The electromagnetic feature acquisition module is used to collect the gearbox shaft current under electromechanical coupling conditions, correlate the current characteristics of each bearing, and generate a characteristic sequence of each bearing current. It also simultaneously collects the gearbox electromagnetic field and evaluates the electromagnetic disturbance level experienced by each gearbox component based on the characteristic sequence of each bearing current, generating internal electromagnetic disturbance snapshot data.

[0101] The electro-erosion path assessment module is used to identify the main current flow paths and branches between the gear meshing pairs in the gearbox based on the characteristic sequence of each bearing current, estimate the current distribution ratio, and generate estimated meshing pair-bearing current share data; obtain gearbox design parameters; and perform electro-erosion damage assessment based on the meshing pair-bearing estimated current share data based on internal electromagnetic disturbance snapshot data and gearbox design parameters to generate the bearing-tooth surface electro-erosion damage ratio;

[0102] The fatigue damage accumulation module is used to calculate the component fatigue life reduction factor based on the proportion of electrical corrosion damage on the bearing-tooth surface, and perform dynamic linear damage accumulation processing to obtain the fatigue damage contribution value of each component in the cycle;

[0103] The life prediction analysis module is used to analyze the fatigue damage degree of the leading components according to the fatigue damage contribution value of each component in the cycle, and to evaluate the life of the gearbox to generate the remaining life percentage of the gearbox.

[0104] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0105] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest possible manner consistent with the principles and novel features disclosed herein.

Claims

1. A gearbox life assessment method, characterized in that: The following steps are involved: Step S1: Under electromechanical coupling conditions, the shaft current of the gearbox is collected, and the current characteristics of each bearing are correlated to generate a characteristic sequence of each bearing current; the electromagnetic field of the gearbox is simultaneously collected, and the electromagnetic disturbance level to which each component of the gearbox is subjected is evaluated based on the characteristic sequence of each bearing current, thereby generating internal electromagnetic disturbance snapshot data; Step S2: Based on the characteristic sequence of each bearing current, the main flow paths and branches of the current between the gear meshing pairs in the gearbox are identified, and the current distribution ratio is estimated to generate meshing pair-bearing estimated current share data; the gearbox design parameters are obtained; based on the internal electromagnetic disturbance snapshot data and the gearbox design parameters, the electro-erosion damage assessment is performed using the meshing pair-bearing estimated current share data to generate the bearing-tooth surface electro-erosion damage ratio; wherein the electro-erosion damage assessment is specifically as follows: Set the critical electrical corrosion damage threshold of gears / bearings according to gearbox design parameters; Extract the instantaneous effective conductive contact area of each meshing point and the conductive area of the bearing roller / raceway contact point based on the gearbox design parameters; The average current density on the tooth surface is calculated based on the estimated current share data of the meshing pair and the bearing and the instantaneous effective conductive contact area of each meshing point to obtain the tooth surface meshing current density data; The bearing current density is estimated based on the estimated current share data of the meshing pair and the bearing and the conductive area of the bearing roller / raceway contact point, and the bearing current density distribution range is obtained; Analyze the electrothermal-mechanical disturbance characteristics of the tooth surface based on the internal electromagnetic disturbance snapshot data and the tooth surface meshing current density data to generate the tooth surface electrothermal-mechanical disturbance data; Based on the critical electro-corrosion damage threshold of gears / bearings, electro-corrosion damage assessment is performed using the electro-thermal-mechanical disturbance data of the tooth surface and the distribution range of the bearing current density, generating the proportion of electro-corrosion damage on the bearing tooth surface. Step S3: Calculate the component fatigue life reduction factor based on the proportion of electrical corrosion damage on the bearing-tooth surface, and perform dynamic linear damage accumulation processing to obtain the fatigue damage contribution value of each component in the cycle; Step S4: Perform fatigue damage analysis on the leading components according to the fatigue damage contribution value of each component in the cycle, and perform gearbox life assessment to generate the remaining life percentage of the gearbox.

2. The gearbox life assessment method according to claim 1, characterized in that: In step S1, the shaft current of the gearbox is collected under the electromechanical coupling working condition, and the characteristics of the current of each bearing are correlated, including: Install high-frequency Rogowski coils on the sun gear input bearing seat, planet carrier output bearing seat, and outer ring of at least one planet gear bearing of the gearbox; arrange a micro magnetic field probe array on the outer wall of the planet gear bearing area and the outer wall near the fixed end of the gear ring in the gearbox body; The high-frequency response Rogowski coil is used to perform wide-band sampling of the shaft current to obtain the original shaft current time domain series; Perform fast Fourier transform on the original shaft current time domain series, and then extract the amplitude and phase of each frequency component to generate the original shaft current spectrum data; The main harmonic component analysis of the shaft current original spectrum data is performed through a preset amplitude threshold value to generate a shaft current main harmonic amplitude sequence; Extract the harmonic frequencies corresponding to the main harmonic amplitude sequence of the shaft current according to the original spectrum data of the shaft current, and obtain the main harmonic frequency sequence of the shaft current; The shaft current main harmonic amplitude sequence and the shaft current main harmonic frequency sequence are correlated with the bearing current characteristics to generate the bearing current characteristic sequence.

3. The gearbox life assessment method according to claim 2, characterized in that: In step S1, the electromagnetic field of the gearbox is collected synchronously, and the electromagnetic disturbance level to which each component of the gearbox is subjected is evaluated based on the characteristic sequence of each bearing current, including: The micro magnetic field probe array is used to synchronously collect the magnetic field intensity at each measuring point to obtain the discrete magnetic field data on the box surface; Perform triaxial component analysis on the discrete magnetic field data on the box surface to evaluate the electromagnetic field leakage intensity and direction at the measuring point and obtain spatial electromagnetic field distribution data; The gearbox is mapped in three-dimensional space based on the spatial electromagnetic field distribution data, and the spatial magnetic field is estimated to reconstruct the equivalent leakage magnetic field intensity distribution near the inner ring of the planetary carrier bearing and the meshing line between the sun gear and the planetary gear in the gearbox, generating internal equivalent magnetic field intensity cloud data. The internal equivalent magnetic field intensity cloud data is spatially divided, and the average and peak magnetic field intensities of the planetary gear bearing raceways, sun gear tooth surfaces, and planetary gear tooth surfaces in the gearbox are calculated to generate magnetic induction characteristic data on the surfaces of key components. Evaluate the electromagnetic excitation of the planetary gear train's rotating components based on the characteristic current sequence of each bearing, and generate potential electromagnetic excitation frequency data; The electromagnetic disturbance level to which each gearbox component is subjected is evaluated based on the potential electromagnetic excitation frequency data and the surface magnetic induction characteristic data of key components, and the electromagnetic disturbance characteristics are extracted to generate internal electromagnetic disturbance snapshot data.

4. The gearbox life assessment method according to claim 1, characterized in that: In step S2, the main flow paths and branches of the current between the gear meshing pairs in the gearbox are identified based on the characteristic sequence of the current of each bearing, and the current distribution ratio is estimated, including: Obtain the connection topology of the sun gear, planet gear and ring gear in the gearbox and the geometric constraint data of the planetary gear system; Extracting the current value of each bearing according to the characteristic sequence of each bearing current; Based on the sun gear-planet gear-ring gear connection topology, the conductive connection topology analysis of each bearing current value is performed. Based on the geometric constraints of the planetary gear system, the main flow paths and branches of the current between the gear meshing pairs are identified to obtain the main current path data between the gears. The gear meshing pairs include the sun gear-planet gear meshing pair and the planet gear-ring gear meshing pair. According to the main current path data between gears, the number of equivalent conductive contact points and the contact resistance range of the bearings in the gearbox are set to generate the bearing shunt characteristic data; Based on the bearing current shunt characteristic data and the main current path data between gears, the current distribution ratio of the sun gear-planet gear meshing pair and the planet gear-inner ring gear meshing pair is estimated to generate the meshing pair-bearing estimated current share data.

5. The gearbox life assessment method according to claim 1, characterized in that: Based on the critical electrocorrosion damage threshold of gears / bearings, the electrocorrosion damage assessment is performed using the electrothermal-mechanical disturbance data of the tooth surface and the bearing current density distribution range, including: An ultrasonic oil film thickness sensor is installed in the lubricating oil channel near the planetary gear meshing area in the gearbox to measure the minimum oil film thickness of the lubricating oil channel in real time and obtain a real-time sampling value of the oil film thickness; Calculate the minimum oil film thickness ratio in the critical area based on the real-time sampling value of the oil film thickness; Based on the tooth surface electrothermal-mechanical disturbance data, the current cycle lubrication status analysis is performed on the minimum oil film thickness ratio in the critical area and the dynamic monitoring curve of the oil resistivity. The electro-erosion risk assessment of the meshing area is also performed to generate the meshing area working condition severity index. Based on the tooth surface meshing current density data and the meshing area working condition severity index, the preset gear material electrocorrosion rate correlation table is used to estimate the tooth surface equivalent micro-electrocorrosion pit density increment generated on the tooth surface during the current monitoring period; Estimate the density increment of the equivalent micro-electrolytic pits of the bearing raceway generated by the bearing during the current monitoring period according to the maximum current density estimated value in the bearing current density distribution interval; The equivalent micro-electrolytic pit density increments of the tooth surface and the bearing raceway are periodically accumulated and divided by the corresponding gear / bearing critical electrolytic damage threshold to obtain the bearing-tooth surface electrolytic damage ratio.

6. The gearbox life assessment method according to claim 5, characterized in that: Based on the tooth surface electrothermal-mechanical disturbance data, the current cycle lubrication status analysis is performed on the minimum oil film thickness ratio in the critical area and the dynamic monitoring curve of the oil resistivity, and the electro-corrosion risk assessment in the meshing area is performed, including: When the minimum oil film thickness ratio in the critical area is continuously greater than 2.0, or the maximum current density value in the electrothermal-mechanical disturbance data of the tooth surface is less than 0.05 A / mm², the boundary lubrication coefficient is set to a high value and the electro-erosion risk level is set to a low value, indicating good lubrication, and low-risk lubrication-electro-erosion status data is generated; When the minimum oil film thickness ratio in the critical area is between 1.0 and 2.0, and the maximum current density value in the electrothermal-mechanical disturbance data of the tooth surface is higher than 0.1 A / mm², the boundary lubrication coefficient is set to the medium value, the electrocorrosion risk level is set to medium, and the medium-risk lubrication-electrocorrosion state data is generated; When the minimum oil film thickness ratio in the critical area is less than 1.0, the boundary lubrication coefficient is set to a low value, the electro-corrosion risk level is set to high, and high-risk lubrication-electro-corrosion state data is generated; Based on the low-risk lubrication-electroerosion state data, medium-risk lubrication-electroerosion state data, and high-risk lubrication-electroerosion state data, the weighted index of the severity of the current meshing area is quantified through the maximum thermal stress value and the change in the electromagnetic-induced additional force ratio in the tooth surface electrothermal-mechanical disturbance data to generate the meshing area working condition severity index.

7. The gearbox life assessment method according to claim 1, characterized in that: Step S3 includes the following steps: Step S31: installing high-bandwidth torque sensors at the input shaft flange and output shaft spline of the gearbox, respectively, synchronously collecting dynamic signals of input and output torques, and obtaining a double-end torque time domain series; Step S32: Installing a three-axis high-frequency acceleration sensor on the planetary carrier bearing seat and the sun gear bearing seat of the gearbox to synchronously collect multi-point and multi-directional vibration signals to obtain multi-channel vibration data; Step S33: extracting the amplitude and phase of the mechanical load harmonic torque component according to the double-end torque time domain sequence to generate a harmonic torque component spectrum; Step S34: performing spectrum analysis on the multi-channel vibration data, and screening electromagnetic excitation components whose corresponding vibration response amplitudes exceed a preset noise threshold through potential electromagnetic excitation frequency data in the internal electromagnetic disturbance snapshot data, to obtain effective electromagnetic vibration response frequency points; Step S35: identifying high-risk vibration coupling components through harmonic torque component spectra and effective electromagnetic vibration response frequencies, and generating high-risk coupling vibration data for each component; Step S36: analyzing the gearbox contact stress history based on the harmonic torque component spectrum, and performing coupled vibration enhancement using the high-risk coupled vibration data of each component to generate a coupled vibration enhanced stress history; Step S37: Calculate the component fatigue life reduction factor based on the proportion of electrical corrosion damage on the bearing-tooth surface, and perform linear damage accumulation processing based on the coupled vibration enhanced stress history to obtain the fatigue damage contribution value of each component in the cycle.

8. The gearbox life assessment method according to claim 7, characterized in that: Identification of high-risk vibration coupling components through harmonic torque component spectrum and effective electromagnetic vibration response frequency includes: The vibration frequency of the harmonic torque component spectrum is aligned through the effective electromagnetic vibration response frequency point, and the mechanical natural vibration response amplitude is analyzed to obtain the dominant frequency response amplitude of the mechanical resonance; The effective electromagnetic vibration response frequency point is matched with the dominant frequency response amplitude of the mechanical resonance. When the frequency difference between the two is less than the preset coupling identification bandwidth, it is marked as a potential coupling pair to obtain the potential electromechanical coupling frequency pair. For a potential electromechanical coupling frequency pair, calculate the ratio of its vibration amplitude at the electromagnetic excitation frequency to the baseline vibration amplitude of the corresponding mechanical natural frequency in the absence of significant electromagnetic excitation, as the vibration amplification factor of the coupling pair; Multiply the vibration amplification factor by the corresponding bearing-tooth surface electro-erosion damage ratio, and screen out coupling pairs that exceed the preset risk threshold to generate high-risk coupled vibration events. The gearbox components are mapped through high-risk coupled vibration events to generate high-risk coupled vibration data for each component.

9. A gearbox life assessment system, characterized in that: For executing the gearbox life assessment method according to claim 1, the gearbox life assessment system comprises: The electromagnetic feature acquisition module is used to collect the gearbox shaft current under electromechanical coupling conditions, correlate the current characteristics of each bearing, and generate a characteristic sequence of each bearing current. It also simultaneously collects the gearbox electromagnetic field and evaluates the electromagnetic disturbance level experienced by each gearbox component based on the characteristic sequence of each bearing current, generating internal electromagnetic disturbance snapshot data. The electro-erosion path assessment module is used to identify the main current flow paths and branches between the gear meshing pairs in the gearbox based on the characteristic sequence of each bearing current, estimate the current distribution ratio, and generate estimated meshing pair-bearing current share data; obtain gearbox design parameters; and perform electro-erosion damage assessment based on the meshing pair-bearing estimated current share data based on internal electromagnetic disturbance snapshot data and gearbox design parameters to generate the bearing-tooth surface electro-erosion damage ratio; The fatigue damage accumulation module is used to calculate the component fatigue life reduction factor based on the proportion of electrical corrosion damage on the bearing-tooth surface, and perform dynamic linear damage accumulation processing to obtain the fatigue damage contribution value of each component in the cycle; The life prediction analysis module is used to analyze the fatigue damage degree of the leading components according to the fatigue damage contribution value of each component in the cycle, and to evaluate the life of the gearbox to generate the remaining life percentage of the gearbox.

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