A method for measuring the slip rate of a three-phase asynchronous motor

Abstract

The present invention relates to the field of electrical machinery parameter measurement technology, and in particular to a slip measurement method for a three-phase asynchronous motor. The method comprises the following steps: performing temperature gradient analysis and copper loss temperature rise ratio calculation on the three-phase asynchronous motor to obtain a thermal characteristic vector; performing air gap region capacitance measurement on the motor stator to obtain a fused capacitance signal; performing rotor period feature extraction on the fused capacitance signal to obtain a rotor slot frequency characteristic spectrum; performing thermal field-capacitance field characteristic correlation on the thermal characteristic vector and the rotor slot frequency characteristic spectrum to obtain a characteristic correlation matrix; performing dynamic allocation of dual-field reliability weights under different working conditions according to the characteristic correlation matrix to obtain a fused credibility index; calculating preliminary slip estimation values ​​based on the thermal characteristic vector and the rotor slot frequency characteristic spectrum respectively to obtain a dual-source slip value. The present invention significantly improves the precision and accuracy of slip measurement in a variable temperature environment through electrical machinery parameter measurement technology.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical machinery parameter measurement, and in particular to a slip measurement method for a three-phase asynchronous motor. Background Art

[0002] Existing direct speed measurement methods (such as encoders and tachogenerators) require physical installation on the motor shaft, making them invasive or contact-based. This is difficult to implement in harsh operating conditions such as explosive environments, high temperatures, high humidity, and severe corrosion, requiring downtime and modification, or even making installation impossible. While methods based on electrical signal harmonic analysis offer non-contact electrical measurement, the signal sources they rely on (current and voltage) are susceptible to various factors, including grid quality fluctuations, inverter harmonics, and internal electromagnetic interference within the motor. These methods are not truly non-contact and stable sources of speed information in the truest sense of the word.

[0003] Whether directly measuring speed or analyzing electrical harmonics, both rely on a single source of measurement information. This single source can significantly reduce the accuracy and stability of measurement results when exposed to severe environmental interference (such as strong electromagnetic interference and vibration), complex operating conditions (such as frequent starts and stops, variable loads, and operation at non-rated voltage or frequency), or when the sensor or signal source itself experiences anomalies. This poor robustness makes continuous and reliable monitoring difficult.

[0004] During motor operation, the temperatures of components such as the windings, core, and bearings fluctuate significantly. These temperature variations directly affect motor equivalent circuit parameters (such as winding resistance, magnetizing reactance, and iron resistance) as well as mechanical losses. Existing methods often fail to fully account for or accurately compensate for these temperature effects on slip calculation models. This leads to systematic deviations in measurement results under varying temperature conditions, particularly when the temperature rise is high, compromising accuracy.

[0005] In summary, existing technologies have problems such as measurement methods being limited by working conditions, low reliability of single information sources, and insufficient compensation for temperature effects that need to be urgently addressed. Summary of the Invention

[0006] Based on this, it is necessary to provide a slip measurement method for a three-phase asynchronous motor to solve at least one of the above technical problems.

[0007] To achieve the above object, a method for measuring the slip rate of a three-phase asynchronous motor comprises the following steps:

[0008] Step S1: performing temperature gradient analysis on different regions of the three-phase asynchronous motor to obtain regional hotspot data and a temperature gradient matrix; calculating the copper loss temperature rise ratio based on the regional hotspot data and the temperature gradient matrix to obtain a thermal characteristic vector;

[0009] Step S2: measuring the capacitance of the motor stator in the air gap region to obtain a fused capacitance signal; extracting rotor period features from the fused capacitance signal to obtain a rotor slot frequency characteristic spectrum;

[0010] Step S3: Correlate the thermal field-capacitance field characteristics of the thermal feature vector and the rotor slot frequency feature spectrum to obtain a feature correlation matrix; dynamically allocate the dual-field reliability weights under different working conditions based on the feature correlation matrix to obtain a fusion credibility index;

[0011] Step S4: Calculate preliminary slip estimation values ​​based on the thermal characteristic vector and the rotor slot frequency characteristic spectrum respectively to obtain a dual-source slip value; perform conductor temperature characteristic analysis based on the thermal characteristic vector to obtain a temperature correction coefficient; perform weight mapping and weighted fusion of the dual-source slip values ​​based on the fusion credibility index to obtain a preliminary fused slip; perform temperature characteristic compensation on the preliminary fused slip based on the temperature correction coefficient to obtain an optimized slip.

[0012] The present invention uses a high-precision infrared thermal imager to perform all-round, high-frequency surface thermal imaging, and then performs sophisticated denoising, correction, and regional segmentation on the original thermal image. This allows accurate capture of subtle temperature differences and hotspot distribution on the motor surface, overcoming the limitations of traditional single-point temperature measurement. By extracting the relative temperature rise, temperature rise ratio, and temperature gradient information of each functional area, particularly focusing on the regional temperature rise characteristics related to the rotor copper loss heat dissipation path, a method is established to indirectly perceive the rotor loss state without directly contacting the rotor or measuring current. The constructed multidimensional thermal feature vector comprehensively reflects the distribution intensity and heat transfer pattern of the internal heat source of the motor, laying a solid foundation for subsequent slip estimation based on thermal information. In particular, under steady-state heavy-load conditions, the heat generated by the rotor copper loss is an important indicator of slip, providing a reliable thermal basis. An array of microcapacitive sensors installed on the inner surface of the stator enables non-invasive, real-time monitoring of dynamic physical property changes in the air gap region. These changes are directly caused by the passage of the rotor slot tooth structure, providing a more direct source of rotor mechanical speed information that is less susceptible to grid interference than current and voltage harmonic analysis. A high-frequency oscillator circuit drives the sensor and performs high-speed data acquisition, ensuring accurate capture of high-frequency slot-passing signals. Bandpass filtering, wavelet noise reduction, and envelope detection are applied to the raw capacitance signal, effectively improving the signal-to-noise ratio and highlighting the periodic characteristics of rotor slot passage. Spatial capacitance field fusion, combining information from multiple sensors, further enhances the signal's interference resistance and stability. Time-frequency analysis, such as FFT and STFT, on the fused signal precisely locates the rotor slot-passing frequency, its harmonics, and modulation components. These modulation features (frequency modulation depth and phase modulation) are closely correlated with the rotor current distribution and load conditions, providing rich rotor dynamic information. The constructed rotor slot frequency signature directly reflects the rotor's high-precision mechanical speed, providing a critical, physical displacement-based measurement basis for slip calculation and exhibiting strong robustness to power quality and electromagnetic interference. Precise time alignment of thermal eigenvectors and rotor slot frequency signatures, acquired at different frequencies, ensures temporal synchronization between the two physical characteristic data, facilitating subsequent joint analysis. By calculating the basic correlation coefficient, lagged correlation coefficient, and nonlinear correlation metric between thermal and slot-pass characteristics, and performing significance tests, we can deeply explore and quantify the inherent correlation patterns between the two different physical phenomena, identifying which thermal characteristics have reliable correspondences with which air gap capacitance characteristics. Based on the feature correlation matrix and current data, the motor operating conditions are identified, enabling subsequent signal quality assessment and weight assignment to be targeted. By performing separate signal quality assessments on the thermal and slot-pass characteristics, we can determine in real time which measurement method provides the most reliable data under the current operating conditions.The fusion weights of the two data sources are dynamically assigned based on signal quality, operating condition type, and feature relevance, achieving adaptive weighted fusion. When the quality of one signal degrades or its reliability is low under specific operating conditions, its weight is reduced and the weight of the other signal is increased. This effectively overcomes the limitations of single measurement methods and enhances the robustness of the overall measurement. A fusion credibility index is calculated, providing a quantitative reliability indicator for the final slip estimation result. Preliminary slip estimates can be independently calculated based on the thermal eigenvector and the rotor slot frequency characteristic spectrum, providing two independent verification paths for subsequent fusion. By estimating the temperature of key motor components using the thermal eigenvector and mapping it to stator windings, rotor conductors, and other components, real-time temperature information of the motor's internal conductors can be obtained, which is a prerequisite for accurate temperature compensation. Temperature correction coefficients for parameters such as resistance, mechanical loss, and iron loss are calculated based on conductor temperature and other component temperatures (such as bearings), providing a basis for temperature correction of equivalent circuit model parameters. Dynamically adjusting the weights of the two preliminary slip estimates based on the fusion credibility index for weighted fusion leverages the strengths of both data sources to obtain a more stable, closer-to-true preliminary fused slip under different operating conditions. A comprehensive compensation factor is calculated based on temperature-corrected motor parameters and a number of detailed temperature effects (the main effect of rotor resistance, the interaction effect of stator impedance, the impact of mechanical losses, and magnetic characteristic corrections). An iterative optimization method is then used to temperature-compensate the initial fused slip. This method accurately corrects the impact of temperature changes on the motor's equivalent parameters and operating characteristics, significantly improving the precision and accuracy of slip measurement in variable temperature environments. The resulting optimized slip is a high-precision result derived from multi-source information fusion, adaptive weighting, and comprehensive temperature compensation, providing reliable data support for motor condition monitoring, efficiency assessment, and fault diagnosis.

[0013] Therefore, the present invention provides a slip measurement method for a three-phase asynchronous motor. This method uses infrared thermal imaging technology to achieve non-contact acquisition of the motor surface temperature distribution. Combined with a stator inner surface micro-capacitive sensor array to detect the dynamic characteristics of the air gap, a thermal field-capacitive field dual physical characteristic measurement system is established. An adaptive weighted fusion algorithm is used to dynamically assign weights to the two physical characteristic data, optimizing the fusion of measurement results based on signal reliability under different operating conditions. A complete temperature characteristic compensation mechanism is also established, taking into account the impact of temperature on the parameters of various motor components, and achieving high-precision slip calculation through iterative optimization. This method does not require downtime for installation, is suitable for harsh industrial environments, and maintains high-precision measurements across the full operating range, providing reliable technical support for three-phase asynchronous motor state monitoring and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 The figure is a flow chart of the steps of a method for measuring the slip rate of a three-phase asynchronous motor.

[0015] 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

[0016] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts are within the scope of protection of the present invention.

[0017] In addition, the accompanying drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor and / or microcontroller approaches.

[0018] It should be understood that although the terms "first," "second," and the like may be used herein to describe various elements, these elements should not be limited by these terms. These terms are used solely to distinguish one element from another. For example, a first element may be referred to as a second element, and similarly, a second element may be referred to as a first element, without departing from the scope of the exemplary embodiments. The term "and / or" as used herein includes any and all combinations of one or more of the listed associated items.

[0019] In the embodiment of the present invention, reference Figure 1 FIG. 1 is a flow chart showing the steps of a method for measuring the slip rate of a three-phase asynchronous motor according to the present invention. In this example, the method for measuring the slip rate of a three-phase asynchronous motor includes the following steps:

[0020] Step S1: performing temperature gradient analysis on different regions of the three-phase asynchronous motor to obtain regional hotspot data and a temperature gradient matrix; calculating the copper loss temperature rise ratio based on the regional hotspot data and the temperature gradient matrix to obtain a thermal characteristic vector;

[0021] In the embodiment of the present invention, a high-resolution infrared thermal imager is used to collect the original thermal image sequence of the motor surface at a frequency of 8Hz, and a corrected thermal image is obtained after geometric correction, Gaussian filtering and background temperature compensation; the corrected thermal image is divided into six functional areas based on the motor structure, and the adaptive threshold method is used to identify and extract the coordinates, area and peak temperature of the three characteristic hot spots with the highest temperature in each area to form regional hot spot data; the temperature gradient is calculated along 8 radial directions with the characteristic hot spot as the center, and the temperature transition between adjacent functional areas is calculated to construct a temperature gradient matrix; a regional cold state temperature table is established in the cold state of the motor as a basis The relative temperature rise of each region and hotspot is calculated accurately and in real time to form regional temperature rise data, and the temperature rise rate and steady-state temperature rise are analyzed to obtain the dynamic characteristics of temperature rise. The temperature rise ratios between different regions are calculated to form a regional temperature rise ratio set, especially the temperature rise ratio between the top of the casing and the end cover. The key parameters sensitive to the slip are extracted from the regional temperature rise ratio set and the dynamic characteristics of temperature rise to form the slip thermal characteristic parameters, which are integrated with the regional temperature rise ratio set to obtain the temperature rise characteristic data. Finally, the regional hotspot data, temperature gradient matrix and temperature rise characteristic data are normalized and integrated to construct a multi-dimensional thermal characteristic vector that describes the distribution state of the heat source inside the motor.

[0022] Step S2: measuring the capacitance of the motor stator in the air gap region to obtain a fused capacitance signal; extracting rotor period features from the fused capacitance signal to obtain a rotor slot frequency characteristic spectrum;

[0023] In an embodiment of the present invention, nine 4mm×4mm×0.8mm miniature capacitance sensors are installed on the inner surface of the stator along the A, B, and C phase positions according to the structural parameters of the motor stator to form a sensor position map; each sensor is driven by a high-frequency LC oscillation circuit operating at 2MHz, and a high-speed acquisition system with an 800kHz sampling rate is used to synchronously acquire the sensor output signal to capture the change in air gap capacitance caused by the passage of the rotor slot teeth to obtain an original capacitance signal; the original capacitance signal is subjected to 100Hz-10kHz bandpass filtering and wavelet noise reduction, and then the modulation characteristics are extracted by envelope detection to obtain a noise-reduced capacitance signal; the noise-reduced capacitance signal is subjected to time phase correction and weighted average fusion based on signal quality according to the sensor position map to obtain a fused capacitance signal with a high signal-to-noise ratio; The fusion capacitor signal is subjected to FFT to calculate the spectrum, and the rotor slot passing frequency is accurately located through peak detection to obtain the precise slot frequency, and the amplitude ratio and phase difference of its first few harmonics are calculated to construct the harmonic relationship parameters; the sideband amplitude near the precise slot frequency is analyzed to calculate the frequency modulation depth to obtain the frequency modulation parameters; the fusion capacitor signal is synchronously demodulated and the instantaneous phase fluctuation is analyzed to calculate the phase modulation index to obtain the phase modulation characteristics; the harmonic relationship parameters, frequency modulation parameters and phase modulation characteristics are input into the pre-trained model for load feature correlation analysis to obtain the load correlation characteristics; the rotor mechanical speed is calculated according to the precise slot frequency and the number of rotor slots to obtain the speed characteristic data; finally, the speed characteristic data and load correlation characteristics are integrated to construct a rotor slot frequency characteristic spectrum containing rotor speed and dynamic modulation information.

[0024] Step S3: Correlate the thermal field-capacitance field characteristics of the thermal feature vector and the rotor slot frequency feature spectrum to obtain a feature correlation matrix; dynamically allocate the dual-field reliability weights under different working conditions based on the feature correlation matrix to obtain a fusion credibility index;

[0025] In an embodiment of the present invention, the thermal feature vector (5 Hz) and the rotor slot frequency feature spectrum (100 Hz) are time-series aligned according to the timestamp, for example, by time-downsampling the rotor slot frequency feature spectrum or interpolating the thermal feature vector to form a series of synchronous data pairs with consistent timestamps; key parameter features such as the casing temperature rise ratio, rotor speed, and frequency modulation depth are extracted from the synchronous data pairs; the Pearson correlation coefficients between the key parameter features are calculated to form an original correlation matrix; the mutual correlation coefficients under different time lags are calculated to find the maximum lag correlation and its lag time to form lag correlation data; the nonlinear correlation strength between the key parameters is mined using methods such as mutual information or MIC to form nonlinear correlation data; a statistical significance test is performed on the correlation measure values ​​in the original correlation matrix, lag correlation data, and nonlinear correlation data, and statistically reliable correlation relationships are marked to form significance test results; the most important key correlation feature pairs are screened out based on the significance test results and the correlation strength; the screened key correlation feature pairs and their correlation measure values ​​are organized into a feature correlation matrix to reflect the most significant correlation between thermal field and capacitance field data. According to the feature correlation matrix and the parameter features of the current synchronous data pair, the current motor operating condition type is identified through the classification model to obtain the operating condition type identification; based on the operating condition type identification and the feature correlation matrix, the signal quality indicators such as temperature stability and frequency stability of the thermal feature vector and the rotor slot frequency characteristic spectrum under the current operating condition are evaluated to obtain a quality score table; based on the quality score table, the operating condition type identification and the feature correlation matrix, a weighted sum model or fuzzy logic system is used to dynamically calculate the fusion weight coefficient of the thermal features and slot pass features in the slip estimation, so that the data source with high quality, high correlation and better adaptation to the current operating condition obtains greater weight; finally, based on the fusion weight coefficient and the quality score table, and considering the consistency of the preliminary slip estimation values ​​of the two methods, the overall fusion credibility index of the current slip estimation result is calculated.

[0026] Step S4: Calculate preliminary slip estimation values ​​based on the thermal characteristic vector and the rotor slot frequency characteristic spectrum respectively to obtain a dual-source slip value; perform conductor temperature characteristic analysis based on the thermal characteristic vector to obtain a temperature correction coefficient; perform weight mapping and weighted fusion of the dual-source slip values ​​based on the fusion credibility index to obtain a preliminary fused slip; perform temperature characteristic compensation on the preliminary fused slip based on the temperature correction coefficient to obtain an optimized slip.

[0027] In an embodiment of the present invention, the mapping relationship between the temperature rise ratio and the slip rate in the thermal characteristic vector and the difference between the rotor speed and the synchronous speed in the rotor slot frequency characteristic spectrum are respectively used to calculate the preliminary slip rate based on the thermal information and the preliminary slip rate based on the speed information to obtain the dual-source slip rate value; the maximum temperature of the casing, the end cover temperature, the bearing seat temperature, etc. are extracted from the thermal characteristic vector, and the average temperature of the stator winding, the rotor conductor and the bearing is estimated through the thermal model or the mapping relationship to form a key component temperature set; the stator and rotor resistance correction coefficients are calculated according to the estimated conductor temperature and the copper resistance temperature characteristics to form a resistance temperature correction value; the lubrication viscosity change rate is estimated according to the bearing temperature; the mechanical loss correction factor is calculated by using the viscosity change rate and the bearing temperature through the mechanical loss model; the iron loss temperature coefficient is estimated according to the estimated core temperature; the resistance temperature correction value, the mechanical loss correction factor and the iron loss temperature coefficient are integrated to form a temperature correction coefficient; the thermal characteristics and slot passage are dynamically adjusted according to the relative reliability information contained in the fusion credibility index. The weight of the preliminary slip rate of the characteristic is weighted and averaged and smoothed to obtain the preliminary fused slip rate; the temperature correction coefficient is used to update the motor equivalent circuit parameters and construct the temperature-corrected motor parameters; the main increment of the slip rate caused by the temperature change of the rotor resistance is calculated as the rotor main effect coefficient; based on the temperature-corrected motor parameters and the rotor main effect coefficient, the indirect influence of the stator resistance and leakage reactance changes on the slip rate is analyzed through the equivalent circuit to obtain the stator-rotor interaction coefficient; according to the preliminary fused slip rate and the mechanical loss correction factor, the influence of the mechanical loss on the slip rate is calculated to obtain the mechanical influence coefficient; according to the temperature correction coefficient and the temperature-corrected motor parameters, the influence of the magnetic characteristic temperature change on the slip rate is evaluated to obtain the magnetic characteristic coefficient; the rotor main effect coefficient, the stator-rotor interaction coefficient, the mechanical influence coefficient and the magnetic characteristic coefficient are added to generate a comprehensive compensation factor; finally, using the preliminary fused slip rate as the starting point, the comprehensive compensation factor is applied for iterative correction until the slip rate converges to obtain the optimized slip rate after comprehensive temperature characteristic compensation.

[0028] Preferably, the temperature gradient analysis in step S1 is specifically as follows:

[0029] The running three-phase asynchronous motor is scanned and imaged in all directions by an infrared thermal imager at a frequency of more than 5 Hz to obtain the original thermal image sequence;

[0030] Filter the thermal field noise of the original thermal map sequence to obtain the corrected thermal map;

[0031] The corrected heat map is divided into functional areas, and then the hotspot area features are extracted to obtain regional hotspot data;

[0032] The heat flux density gradient is extracted from the corrected thermal map according to the regional hotspot data to obtain the temperature gradient matrix.

[0033] In an embodiment of the present invention, a high-precision infrared thermal imager, such as a device with a resolution of 640×480 pixels and a temperature resolution of 0.03°C, is fixed at a distance of 1.5 meters from the outer surface of the three-phase asynchronous motor to be measured. The viewing angle of the thermal imager is adjusted to ensure that the main heat dissipation surfaces such as the motor housing, front and rear end covers, and bearing seats are all completely within the field of view. The acquisition frequency of the thermal imager is set to 8Hz, and the running motor is continuously scanned and imaged. The thermal imager captures the infrared radiation signal emitted by the motor surface in real time and converts it into a raw digital image containing the temperature information of each pixel. These images are arranged in chronological order to form a raw thermal map data stream, that is, a raw thermal map sequence.

[0034] Each frame in the acquired raw thermal image sequence is processed. First, geometric correction is performed. By identifying four non-collinear markers (e.g., small black circles with high emissivity) pre-applied to the motor surface, a homography matrix is ​​calculated between the current image and a standard reference image. This matrix is ​​then applied to the image for perspective transformation to correct for geometric distortion caused by slight variations in the camera's position or angle. Next, noise filtering is performed. A 2D convolution operation with a 5×5 Gaussian filter is applied to the corrected image to smooth the image and remove random high-frequency temperature noise caused by fluctuations in ambient heat sources, dust, and sensor noise. Finally, background temperature compensation is performed. Using the ambient temperature measured by an independent Pt100 ambient temperature sensor and a fixed emissivity parameter (e.g., 0.95) for the motor surface material, each pixel temperature in the thermal image is corrected based on the principle of radiative heat transfer to eliminate the influence of background radiation on the measurement results. This process results in a series of clear, standardized corrected thermal images.

[0035] On the obtained correction heat map, six main functional areas are predefined and automatically identified according to the standard structural size and layout of the target three-phase asynchronous motor: the front cover area, the rear cover area, the housing top area, the housing side area, the front bearing seat area, and the rear bearing seat area. In each functional area, an adaptive threshold segmentation algorithm is used to identify local hot spots. The specific method is to calculate the average temperature μ and standard deviation σ of the current area, and set the threshold T=μ+k·σ, where k is a coefficient that can be adjusted according to the motor type and load (for example, k=1.5). The area with a pixel temperature higher than T and interconnected is identified as a potential hot spot. For each identified hot spot, its pixel center coordinates ( , ), the number of pixels contained (representing the area ), maximum temperature value and its two-dimensional boundary pixel set. In each functional area, according to Sort all identified hot spots in descending order, select the three hot spots with the highest temperature as the characteristic hot spots of the area, and store them ( , ), 、 and boundary data to form regional hotspot data.

[0036] For each feature hotspot identified in the previous step, its center coordinates ( , ) as the starting point, extend a fixed distance L (for example, L = 3 cm) outward along 8 equally spaced radial directions (0°, 45°, 90°, ..., 315°). In these 8 radial directions, collect temperature values ​​every fixed step length Δr (for example, Δr = 0.5 cm). Calculate the temperature change rate in each direction and within each Δr interval, that is, the temperature gradient component. For example, along the 0° direction, at a distance and The temperature T( ) and T( ), then the temperature gradient of this section is approximately (T( )-T( )) / ( ). The gradient components in these eight directions are combined to form the temperature gradient vector around the hotspot. At the same time, for each pair of adjacent functional areas (for example, the top area of ​​the casing and the front cover area), one or more sampling line segments perpendicular to the boundary are defined at or near their boundaries. Temperature profile data are collected along these sampling line segments, and the average temperature difference and spatial temperature change rate (i.e., the temperature gradient between regions) across the region boundaries are calculated. The temperature gradient vectors around all characteristic hotspots and the temperature gradient values ​​between all adjacent functional regions are organized into a multidimensional data structure, namely the temperature gradient matrix. This matrix records the temperature change rate distribution information between key hotspot areas and different functional areas on the motor surface.

[0037] Preferably, the calculation of the copper consumption temperature rise ratio in step S1 is specifically as follows:

[0038] Establish the cold state benchmark temperature for the regional hot spot data and obtain the regional cold state temperature table;

[0039] Calculate the relative temperature rise based on the regional cold temperature table and regional hot spot data to obtain the regional temperature rise data;

[0040] Analyze the temperature rise time characteristics of regional temperature rise data to obtain the temperature rise dynamic characteristics;

[0041] The regional temperature rise ratio is constructed based on the regional temperature rise data and the temperature gradient matrix to obtain the regional temperature rise ratio set;

[0042] Extract slip rate thermal related parameters based on the regional temperature rise ratio set and temperature rise dynamic characteristics to obtain slip rate thermal characteristic parameters;

[0043] Integrate the slip rate thermal characteristic parameters and the regional temperature rise ratio set to obtain temperature rise characteristic data;

[0044] Multi-dimensional thermal features are constructed based on regional hotspot data, temperature gradient matrix and temperature rise characteristic data to obtain thermal feature vectors.

[0045] In an embodiment of the present invention, before a three-phase asynchronous motor starts running, when the motor is in a long-term shutdown state and the surface temperature is basically the same as the ambient temperature, an infrared thermal imager is used to collect the average temperature of each functional area of ​​the motor (front cover, rear cover, housing top, housing side, front bearing seat, rear bearing seat) and the temperature of each characteristic hotspot at this moment. These temperature values ​​are recorded as the reference temperature of the motor in the cold state. For example, the average temperature of the front cover area is recorded as 25.1°C, the average temperature of the housing top area is recorded as 24.9°C, the characteristic hotspot temperature of the front bearing seat is recorded as 25.3°C, etc. These cold temperature values ​​are stored as a data structure to form a regional cold temperature table. This table provides a zero-point reference for calculating the relative temperature rise during operation.

[0046] During motor operation, the average temperature of each functional area and the temperature of each characteristic hotspot are acquired in real time (this data is included in the regional hotspot data). For each area or hotspot, the corresponding cold-state baseline temperature is subtracted from its current temperature value to obtain the temperature rise (ΔT) relative to the cold state. For example, if the current average temperature of the top area of ​​the casing is 58.7°C and the cold-state baseline temperature is 24.9°C, the relative temperature rise is 58.7°C - 24.9°C = 33.8°C. The average temperature rise of all functional areas and the temperature rise of all characteristic hotspots are calculated and recorded to form regional temperature rise data. These temperature rise values ​​directly reflect the heat accumulation and distribution generated by motor operation.

[0047] Continuously monitor the relative temperature rise of each functional area and characteristic hotspot during motor operation. Record time series data for each area's temperature rise. Analyze this time series data to calculate key parameters. For example, calculate the average rate of temperature rise (ΔT / Δt) for each area from startup to the current moment. Identify inflection points in the temperature rise curve to determine whether the motor has entered thermal steady-state. Once the motor reaches thermal steady-state, record the steady-state temperature rise value for each area. These dynamic parameters describe the characteristics of the motor's thermal process, forming dynamic temperature rise characteristic data.

[0048] Calculate the relative temperature rise ratios between different functional areas. Pay particular attention to areas directly related to rotor copper loss heat generation. For example, calculate the ratio of the average temperature rise in the casing top area to the average temperature rise in the front cover area (ΔT_CasingTop / ΔT_Front Cover), or the ratio of the temperature rise of a characteristic hotspot in the casing top area to that of a characteristic hotspot in the rear cover area. These ratios reflect the differences in the conduction and convection efficiency of heat generated by different heat sources within the motor (such as stator copper loss, rotor copper loss, iron loss, and mechanical loss) to the surface through different paths. Since heat generated by rotor copper loss is primarily transferred through the air gap and shaft, it has a greater impact on the temperature rise in the casing top and bearing seat areas, while stator copper loss has a greater impact on the overall temperature rise of the stator core and casing. Therefore, the temperature rise ratios of different areas are related to the proportion of rotor copper loss to total losses, and thus to the slip rate. Combined with the temperature transition characteristics between regions described in the temperature gradient matrix, the calculation of regional temperature rise ratios can be further refined and a set of regional temperature rise ratios can be constructed.

[0049] Key ratios that are sensitive to slip changes are extracted from the regional temperature rise ratios, such as the ratio of the temperature rise at the top of the casing to the temperature rise at the end cover, or the temperature rise ratio of specific characteristic hot spots. These ratios are directly used as candidates for thermally related parameters of the slip. At the same time, parameters reflecting the speed or steady-state degree of the thermal process are extracted from the dynamic characteristics of the temperature rise, such as the temperature rise rate, the time to reach steady state, etc. These parameters are also indirectly related to the motor load and slip. These extracted key ratios and dynamic parameters are combined to form slip thermal characteristic parameters. For example, the slip thermal characteristic parameters include: the ratio of the casing top temperature rise / end cover temperature rise in steady state, the casing top temperature rise rate at the initial startup, the time required to reach 90% steady-state temperature rise, etc. Among them, the rotor copper loss P_cu2 is proportional to the square of the slip s and the square of the rotor current:

[0050] P_cu2= ;

[0051] in is the square value of the rotor current, which represents the square of the rotor current and is used to calculate the rotor copper loss; is the rotor resistance (converted to the stator side), which represents the resistance value of the rotor winding. , rotor leakage reactance When the change is not big and near the rated slip rate, P_cu2 is about is proportional to Proportional. The heat generated by the rotor copper loss is mainly conducted to the surface of the casing through the rotor core, air gap, and stator core, among which the heat flux component conducted to the top and sides of the casing through the air gap is highly correlated with the rotor copper loss. Therefore, there is an approximate nonlinear monotonic relationship between the temperature rise ΔT_top in the top area of ​​the casing and the rotor copper loss P_cu2, for example, ΔT_top≈ f(P_cu2), where f is an increasing function. The ratio of the temperature rise at the top of the casing to the temperature rise of the end cover (ΔT_top / ΔT_endcap) can partially offset the impact of changes in ambient temperature and overall ventilation and heat dissipation conditions, and focus more on the proportion of rotor copper loss in the total loss, where ΔT_endcap is the end cover temperature rise value, which indicates the temperature rise value of the motor end cover area relative to the cold reference temperature. When constructing slip thermal related parameters, for example, the (ΔT_top / ΔT_endcap) value under steady-state conditions can be extracted, and a mapping model between it and the slip s can be established, such as fitting a polynomial through experimental data. , where a, b, and c are polynomial coefficients representing the mapping relationship between temperature rise ratio and slip, or a lookup table is used. The slip thermal characteristic parameters include these strongly correlated temperature rise ratios and temperature rise dynamic parameters.

[0052] The slip thermal characteristic parameters extracted in the previous step (refined key ratios and dynamic parameters) are combined with a more comprehensive set of regional temperature rise ratios (including all calculated inter-regional temperature rise ratios). This creates a comprehensive data set containing multifaceted temperature rise information, known as the temperature rise characteristic data. This data set includes both the temperature rise ratios and dynamic parameters directly related to slip, as well as temperature rise information for other regions, providing comprehensive thermal information for subsequent multidimensional feature construction.

[0053] The regional hotspot data (including hotspot location, area, temperature, etc.), temperature gradient matrix (describing the spatial rate of temperature change) and temperature rise characteristic data (including relative temperature rise, temperature rise ratio, and temperature rise dynamic parameters) obtained above are integrated and normalized. A unified multi-dimensional thermal feature vector is constructed. The dimension of this vector is high and contains rich information on the heat distribution of the motor surface. For example, the thermal feature vector may include: the average temperature rise of each functional area, the temperature rise of the first three characteristic hotspots in each area, the normalized coordinates of the hotspot position, the average temperature gradient along the radial direction of the top area of ​​the casing, the average temperature gradient between the casing and the end cover, the steady-state ratio of the temperature rise of the top of the casing to the temperature rise of the front cover, the temperature rise rate of the top of the casing at the initial startup, etc. All numerical feature parameters are normalized, for example, using the Z-score normalization method ( =(x-μ_x) / σ_x), where is the standardized value, x is the original value, μ_x is the mean of x, and σ_x is the standard deviation of x. This eliminates the influence of different physical dimensions and numerical ranges, making the components of the eigenvector comparable. This multidimensional thermal eigenvector comprehensively reflects the internal heat source distribution and heat transfer characteristics of the motor, providing critical thermal information input for subsequent slip estimation.

[0054] Preferably, the capacitance measurement of the air gap region in step S2 is specifically as follows:

[0055] Obtaining the structural parameters of the inner surface of the motor stator; installing capacitive sensors along the A, B, and C phases of the three-phase winding of the motor stator according to the structural parameters of the inner surface of the motor stator, with each capacitive sensor having a size not exceeding 5 mm × 5 mm × 1 mm, to form a sensor position map;

[0056] According to the sensor position diagram, each capacitance sensor is driven by a high-frequency oscillation circuit to capture the air gap characteristic changes caused by three-phase imbalance and obtain the original capacitance signal;

[0057] Performing response signal noise reduction on the original capacitance signal to obtain a noise-reduced capacitance signal;

[0058] The noise reduction capacitor signal is spatially fused according to the sensor position map to obtain a fused capacitor signal.

[0059] In this embodiment of the present invention, detailed design parameters of the three-phase asynchronous motor under test are first obtained, including the stator inner diameter, number of stator slots, number of rotor slots, air gap length, and stator winding distribution information (the starting and ending angles of the three-phase windings A, B, and C on the circumference). Based on these structural parameters, the locations for installing miniature capacitive sensors along the circumference of the stator inner surface are determined. Metal film capacitive sensors with dimensions of 4 mm × 4 mm × 0.8 mm are selected. To capture the electromagnetic field distribution of the three-phase windings during operation and the dielectric changes caused by the passage of rotor slot teeth, nine capacitive sensors (three sensors per phase) are evenly spaced along the circumference of the stator inner surface, corresponding to the approximate centers of the three-phase windings A, B, and C, forming a total array of nine sensors. The sensors are mounted directly on the stator teeth near the air gap, or with non-conductive fasteners, with the electrodes facing the rotor, to form capacitive coupling with the rotor's outer surface. During installation, ensure that the sensor surface is in close contact with the inner arc surface of the stator and that the sensor leads are insulated and properly secured to prevent interference with rotor rotation. The precise angular position of each sensor on the stator circumference (relative to a reference point on the stator, such as the center line of the starting edge of the A-phase winding) is recorded to form a sensor position map.

[0060] Each mounted miniature capacitive sensor is connected to an independent high-frequency LC oscillator circuit. The operating frequency of the oscillator circuit is set at 2MHz. The capacitive sensor acts as a capacitive element in the LC circuit. When the rotor rotates, the slots (larger air gap, lower equivalent dielectric constant) and teeth (smaller air gap, higher equivalent dielectric constant) on the rotor surface periodically pass under the sensor. This periodic structural change causes a slight change in the equivalent capacitance C of the air gap between the sensor and the rotor. According to the principle of the LC oscillator circuit, the oscillation frequency The relationship with capacitance C is =1 / (2π ), where π is the ratio of the circumference of a circle to the circumference of a circle, is the inductance value, representing the inductance of the inductor in the oscillating circuit. Therefore, slight changes in the air gap capacitance cause slight modulations in the oscillating circuit frequency (frequency modulation). A high-speed data acquisition system, such as a data acquisition card with nine synchronized acquisition channels and a sampling rate of 800kHz, is connected to the output of each oscillating circuit. The data acquisition system synchronously records the frequency-modulated signal output by each sensor, forming nine raw capacitance signal data streams containing information on air gap capacitance changes. These signals reflect the periodic changes in the air gap dielectric properties caused by rotor rotation and include additional changes due to magnetic field distortion caused by three-phase current imbalance.

[0061] The nine raw capacitance signals acquired at high speed are digitally processed. First, a digital bandpass filter with a passband of 100 Hz to 10 kHz is applied to remove interference from the power frequency (50 / 60 Hz) and its low-order harmonics, as well as high-frequency random noise. Next, wavelet decomposition techniques are applied to the filtered signals. For example, a three-layer decomposition using Daubechies wavelet basis functions is used to identify and remove wavelet coefficients corresponding to interference sources such as motor vibration and power supply transients, while retaining signal components related to the rotor slot tooth passage frequency. Envelope detection is then performed on the processed signals. Since changes in air gap capacitance result in frequency modulation, a frequency demodulation circuit or software demodulation technique is used to convert the frequency variation into a voltage or amplitude variation signal, and its envelope is extracted. The periodic variations in this envelope signal further highlight the characteristic frequencies caused by the passage of rotor slot teeth. After the aforementioned filtering, wavelet noise reduction, and envelope detection, nine de-noised capacitance signals with a higher signal-to-noise ratio are obtained.

[0062] Using the precise angle information of each sensor recorded in the sensor position map, the nine noise-reducing capacitor signals are temporally phase-corrected. Since the sensors are evenly spaced along the circumference, there are fixed time delays when a rotor tooth passes each sensor. These delays are calculated based on the approximate rotor speed and the angular spacing between sensors, and the signals are time-shifted accordingly to align all sensor signals in time, as if measured at a single point. Next, the nine phase-corrected signals are fused using a weighted average. The weighting coefficients are dynamically determined based on the signal quality of each sensor (e.g., amplitude and signal-to-noise ratio), with higher weights assigned to sensors with higher signal quality. This spatial fusion method effectively suppresses random noise and local anomalies from individual sensors, improving the robustness and accuracy of the overall signal. For example, simple averaging or signal-to-noise ratio-weighted averaging can be used. The fused signal is a comprehensive capacitance change signal, known as a fused capacitance signal, which more stably reflects the overall dynamic characteristics of the air gap medium.

[0063] Preferably, the rotor period feature extraction in step S2 is specifically as follows:

[0064] Performing slot-tooth modulation characteristic analysis on the fused capacitor signal to obtain spectrum data;

[0065] Accurately locate the slot frequency of the spectrum data to obtain the precise slot frequency;

[0066] The harmonic structure characteristics of the spectrum data are constructed according to the precise slot frequency to obtain the harmonic relationship parameters;

[0067] Perform frequency modulation depth analysis on spectrum data based on precise slot frequency to obtain frequency modulation parameters;

[0068] The phase modulation characteristics of the fused capacitor signal are extracted according to the precise slot frequency to obtain the phase modulation features;

[0069] Perform load characteristic correlation analysis on harmonic relationship parameters, frequency modulation parameters and phase modulation characteristics to obtain load correlation characteristics;

[0070] Extract the speed information from the precise slot frequency and spectrum data to obtain the speed characteristic data;

[0071] The rotor slot frequency characteristic spectrum is constructed based on the speed characteristic data and load correlation characteristics.

[0072] In an embodiment of the present invention, a fast Fourier transform (FFT) is performed on the fused capacitor signal. The FFT window length is set to 16384 sampling points, the overlap rate is 50%, and the Hanning window function is used. The power spectral density or amplitude spectrum of the signal is calculated. In the obtained spectrum, the components related to the rotor slot pass frequency are identified. The rotor slot pass frequency f_slot = N_r × f_r, where f_slot is the rotor slot pass frequency, which indicates the frequency at which the rotor slot tooth structure passes through a fixed point on the stator, N_r is the number of rotor slots, and f_r is the rotor mechanical speed (unit: revolutions per second). On the spectrum diagram, the rotor slot pass frequency and its multiples (harmonics) usually appear as prominent spectral lines. Accurately measure the frequency values, amplitudes, and phase information of these spectral lines. For example, for a motor with 28 rotor slots and 2 pole pairs, if the operating frequency is 50 Hz and the slip ratio is 0.03, the synchronous speed is 25 rpm, the rotor speed is approximately 24.25 rpm, and the slot passing frequency is approximately 28 × 24.25 = 679 Hz. The spectrum line near 679 Hz is searched and precisely located. Simultaneously, a short-time Fourier transform (STFT) or wavelet time-frequency analysis method is applied to analyze the joint distribution of the fused capacitor signal in time and frequency, generating a time-frequency plot. The time-frequency plot shows how the rotor slot passing frequency changes over time, reflecting subtle fluctuations in the rotor speed. The frequency, amplitude, and phase information obtained from the FFT analysis and the time-frequency plot data from the STFT are combined to form the spectrum data.

[0073] In the resulting spectrum data, identify the spectral lines with the highest energy, whose frequencies are roughly within the expected rotor slot pass frequency range. The expected rotor slot pass frequency range can be estimated based on the motor's rated synchronous speed and the number of rotor slots. For example, for a motor with a rated synchronous speed of 1500 rpm (25 rpm / s) and 28 rotor slots, the slot pass frequency should be around 25 × 28 = 700 Hz. Taking slip into account, the actual slot frequency will be slightly lower than 700 Hz. Using a peak detection algorithm, search for local maxima in the spectrum graph and combine frequency and amplitude thresholds to precisely locate the rotor slot pass frequency f_slot. For example, search for a peak in the range of 650 Hz to 700 Hz. If the peak amplitude is 10 dB above the background noise level, the frequency corresponding to the peak is considered the exact slot frequency. Recording this exact frequency value provides the precise slot frequency.

[0074] In the spectrum data, in addition to the precise slot frequency f_slot, its multiples, namely harmonic components such as 2f_slot, 3f_slot, etc., also appear. The amplitude distribution and phase relationship of these harmonics are affected by rotor current distribution, air gap nonuniformity, and even load fluctuations. Using the amplitude A_slot of the precise slot frequency f_slot as a reference, calculate the amplitudes A_2slot, A_3slot, and A_4slot of its first few major harmonics (for example, 2f_slot, 3f_slot, and 4f_slot). Calculate the ratio of each harmonic amplitude to the fundamental frequency amplitude, for example, A_2slot / A_slot, A_3slot / A_slot, etc. Simultaneously, extract the phase information φ_slot, φ_2slot, and φ_3slot of the precise slot frequency and its first few major harmonics, and calculate their phase differences relative to the fundamental frequency, for example, φ_2slot - 2φ_slot and φ_3slot - 3φ_slot. These amplitude ratios and phase differences constitute the harmonic structure characteristics and form the harmonic relationship parameters. For example, the harmonic relationship parameters can be a vector [A_2slot / A_slot,A_3slot / A_slot, A_4slot / A_slot,(φ_2slot-2φ_slot),(φ_3slot-3φ_slot),(φ_4slot-4φ_slot)].

[0075] In addition to the dominant slot-pass frequency, the air-gap capacitance signal is also modulated by the rotor current frequency (i.e., the slip frequency f_s = s × f_1, where s is the slip and f_1 is the power frequency). This modulation manifests as sidebands around the main frequency f_slot, i.e., f_slot ± k·f_s, where k is an integer. In the spectral data, identify the sideband lines on either side of the exact slot frequency f_slot. For example, look for lines at frequencies f_slot ± f_s and f_slot ± 2f_s. Measure the amplitudes of these sideband lines. The frequency modulation depth is typically related to the sideband amplitude and the main frequency amplitude. For example, calculate the ratio of the average amplitude of the primary sidebands (f_slot ± f_s) to the main frequency amplitude, A_slot, as a measure of the frequency modulation depth. This frequency modulation depth reflects the unevenness of the rotor current distribution and is, in turn, related to the slip. These parameters constitute the frequency modulation parameters. For example, the frequency modulation parameters include [(A_(f_slot+f_s)+A_(f_slot-f_s)) / (2A_slot)].

[0076] The rotor current frequency f_s also causes phase modulation of the air gap capacitance signal. The fused capacitance signal is synchronously demodulated based on the precise slot frequency f_slot to extract the instantaneous phase of the signal. The temporal variation of the instantaneous phase is analyzed. If phase modulation is present, the instantaneous phase will fluctuate periodically around an average value at the slip frequency f_s. The amplitude of this phase fluctuation, known as the phase modulation index, is calculated. For example, the signal's analytical signal is obtained through a Hilbert transform, its instantaneous phase is calculated, and the instantaneous phase is filtered to remove high-frequency noise and DC offset. The maximum amplitude of the phase fluctuation is then determined as the phase modulation index. The phase modulation index also reflects the rotor's electromagnetic state and is related to the slip rate. These parameters constitute the phase modulation signature.

[0077] The harmonic relationship parameters, frequency modulation parameters, and phase modulation characteristics obtained above are used as input to analyze their correlation with the motor load. These characteristic parameters are collected under different load conditions (e.g., no-load, half-load, and full-load), and the corresponding motor output power or torque data is recorded. Using machine learning algorithms such as support vector regression or neural networks, a mapping model is established between these characteristic parameters and the motor load. This model learns how the harmonic structure, frequency modulation, and phase modulation change under different loads. These variations indirectly reflect how the rotor current and magnetic field distribution vary with load, which is closely related to the slip. Using this correlation model, these modulation characteristics can be interpreted as characteristics reflecting the current load state, forming a load correlation feature. For example, the load correlation feature can be a vector whose values ​​are obtained by inputting the harmonic relationship parameters, frequency modulation parameters, and phase modulation characteristics into a pre-trained correlation model, representing the "load fingerprint" of the current state.

[0078] Based on the precise slot frequency f_slot and the known number of rotor slots N_r, the rotor mechanical speed f_r can be directly calculated as f_slot / N_r. For example, if the precise slot frequency is 679 Hz and the number of rotor slots is 28, the rotor speed f_r = 679 Hz / 28 = 24.25 revolutions per second. The rotor speed can be converted to common units, such as revolutions per minute (RPM): N = f_r × 60 = 24.25 × 60 = 1455 RPM. Furthermore, the amplitude of the power frequency f_1 in the spectrum data, as well as the relative amplitude relationship between f_slot and f_1, can be analyzed to provide auxiliary speed information or signal quality indicators. These calculated rotor speed values ​​and the associated spectrum amplitude information constitute the speed signature data. For example, the speed signature data consists of [f_r, A_slot / A_f1], where A_f1 is the amplitude of the power frequency in the spectrum.

[0079] The previously obtained speed characteristic data (core of which is the accurately calculated rotor speed) and load-related characteristics (modulation parameters reflecting the load state and rotor electromagnetic state) are integrated. A comprehensive feature vector or data structure, namely the rotor slot frequency characteristic spectrum, is constructed, which includes the rotor mechanical speed as well as its dynamic characteristics and load-related modulation characteristics. For example, the rotor slot frequency characteristic spectrum can be a vector [f_r, frequency modulation parameters, phase modulation characteristics, harmonic relationship parameters]. This characteristic spectrum directly provides high-precision rotor mechanical speed information and indirectly reflects the rotor current, magnetic field distribution, and load state through modulation characteristics, providing a key input based on the air gap capacitance signal for subsequent slip calculation.

[0080] Preferably, the thermal field-capacitive field characteristic correlation in step S3 is specifically:

[0081] Perform thermal field-capacitance field time series alignment on the thermal eigenvector and rotor slot frequency characteristic spectrum to obtain synchronized data pairs;

[0082] Extract key parameter features of synchronized data pairs;

[0083] Calculate the basic correlation coefficients of key parameter features to obtain the original correlation matrix;

[0084] Conduct lag correlation analysis on key parameter characteristics to obtain lag correlation data;

[0085] Perform nonlinear correlation mining on key parameter features to obtain nonlinear correlation data;

[0086] Perform correlation significance test on the original correlation matrix, lagged correlation data and nonlinear correlation data to obtain the significance test results;

[0087] According to the significance test results, key related features are screened to obtain key related feature pairs;

[0088] Construct a feature correlation matrix based on key correlated feature pairs.

[0089] In this embodiment of the present invention, the acquisition frequency of thermal feature vectors is relatively low (e.g., 5 Hz), while the calculation of the rotor slot frequency feature spectrum is based on high-speed acquisition of capacitance signals, which can have a relatively high effective update rate (e.g., 100 Hz). For joint analysis, these two types of data need to be aligned in time. The precise timestamps recorded by the data acquisition system are used as a basis. Because thermal features update slowly, the rotor slot frequency feature spectrum data is downsampled or aggregated according to the timestamps of the thermal features. For example, for each thermal feature vector timestamp t_i, all rotor slot frequency feature spectrum data within the time period [t_i-Δt / 2, t_i+Δt / 2] (where Δt is the thermal feature acquisition period, e.g., 0.2 seconds) is collected, and their average or median is calculated as the synchronous rotor slot frequency feature spectrum corresponding to the thermal feature vector. Another approach is to perform linear or spline interpolation on the thermal feature sequence to increase its temporal resolution to a level similar to that of the rotor slot frequency feature spectrum. For example, if the thermal signature is a point every 0.2 seconds and the rotor slot frequency signature is a point every 0.01 seconds, then the thermal signature sequence is interpolated 20 times. Regardless of the method used, the goal is to construct a series of data pairs with consistent timestamps, each of which contains a thermal signature vector and a corresponding rotor slot frequency signature spectrum to form a synchronized data pair. For example, a synchronized data pair can be represented as {(thermal signature vector 1, rotor slot frequency signature spectrum 1, ),(thermal eigenvector 2, rotor slot frequency eigenspectrum 2, ),...}.

[0090] From each thermal eigenvector and rotor slot frequency signature in the synchronized data pair, key parameters for correlation analysis are selected. For example, the relative temperature rise of the casing top area, the temperature rise ratio between the casing top and the end cover, and the temperature rise value of key hot spots are extracted from the thermal eigenvectors. The rotor mechanical speed (calculated from the precise slot frequency), the first-order sideband amplitude ratio (frequency modulation parameter), and the phase modulation index (phase modulation feature) are extracted from the rotor slot frequency signature. These selected parameters constitute the key parameter feature set for correlation analysis. For example, the key parameter feature can be a combined vector [ΔT_CasingTop, ΔT_CasingTop / ΔT_End Cover, f_r, frequency modulation depth, phase modulation index].

[0091] For the extracted key parameter feature set, calculate the Pearson correlation coefficient between any two parameters. The Pearson correlation coefficient r_xy is used to measure the linear correlation between two variables X and Y. Its calculation formula is: r_xy= ,in and is the parameter value of the ith sample in the synchronized data pair, and is the average of parameters X and Y. Calculate the correlation coefficients between all selected key parameters, forming a symmetric correlation coefficient matrix, the original correlation matrix. The element r_ij in the matrix represents the correlation coefficient between the i-th parameter and the j-th parameter. For example, the matrix includes the correlation coefficient between the casing top temperature rise and the rotor speed, the correlation coefficient between the temperature rise ratio and the frequency modulation depth, and so on.

[0092] Considering the inertia of the heat conduction process, the temperature change on the motor surface often lags behind the change of rotor speed and load. Therefore, it is necessary to analyze the lag correlation between the thermal characteristics and the slot passing characteristics. For each pair of key parameters (for example, the temperature rise of the top of the casing ΔT_top and the rotor speed f_r), the correlation coefficient ρ_xy(τ) between the ΔT_top series and the f_r series at different time lags τ is calculated. , where E[...] is the expected value operator, which represents the statistical average of the expression in the brackets; is the value of the X time series at time t; is the average value of the X time series; is the value of the Y time series at time t+τ; μ y is the average value of the Y time series; is the standard deviation of the X time series; is the standard deviation of the Y time series; τ is the time lag, representing the time offset of the Y series relative to the X series. τ can be positive (indicating that the thermal characteristics lag behind the capacitance characteristics) or negative (indicating that the capacitance characteristics lag behind the thermal characteristics, which is usually not significant). Calculate the correlation coefficient for a range of different lag values ​​and find the lag time τ_max that maximizes the correlation coefficient. For example, it was found that the temperature rise at the top of the casing has the highest correlation when it lags the rotor speed by approximately 10 seconds. Record the lag time that maximizes the correlation coefficient and the corresponding maximum correlation coefficient value to form the lag correlation data. This data reveals the time delay and the strongest correlation between different physical processes.

[0093] In addition to linear correlation, there is a nonlinear relationship between thermal characteristics and capacitance characteristics. For example, the rotor copper loss is proportional to the square of the slip rate, but the temperature rise and copper loss are not simply linearly related and are also affected by heat dissipation conditions. Nonlinear correlation measurement methods such as mutual information (Mutual Information) or maximum information coefficient (MIC) are used to quantify the strength of nonlinear correlation between key parameters. Mutual information I(X;Y) = The p(x,y)log2[p(x,y) / (p(x)p(y))] quantifies the degree of mutual dependence between two random variables X and Y, where p(x,y) is the joint probability distribution and p(x) and p(y) are marginal probability distributions. It measures the degree to which the uncertainty in one variable decreases when information about the other is known. The MIC value ranges between 0 and 1, capturing a wide range of functional relationships. Mutual information, or MIC, is calculated for all pairwise selected key parameters to generate nonlinear correlation data. These data complement the results of linear correlation analysis, revealing more complex correlation patterns.

[0094] Perform statistical significance tests on the calculated raw correlation coefficient, lagged correlation coefficient, and nonlinear association measures (such as MIC). For example, for the Pearson correlation coefficient, a t-test can be used to determine whether it is statistically significantly different from zero. For mutual information and MIC, non-parametric methods such as permutation tests can be used to assess their significance. Set a significance level (for example, α = 0.05). Only associations that pass the significance test are considered reliable and non-random associations. For example, if the Pearson correlation coefficient between the casing top temperature rise and the rotor speed is 0.85, and the t-test p value is less than 0.05, it is considered that there is a significant linear correlation between them. The significance test results mark which associations are statistically reliable.

[0095] Based on the results of the significance test, statistically significant and highly correlated feature pairs are selected from all calculated correlations. For example, linear correlation pairs with an absolute value of the Pearson correlation coefficient greater than 0.7 and passing the significance test are selected; hysteresis correlation pairs with a maximum lag correlation coefficient greater than 0.6 and a corresponding lag time within a reasonable range are selected; and nonlinear correlation pairs with a MIC value greater than 0.5 and passing the significance test are selected. These selected feature pairs are considered the most important correlations between thermal features and slot passing features and can effectively reflect the operating status of the motor. These selected parameters constitute key correlation feature pairs. For example, key correlation feature pairs include {(ΔT_Casing Top, f_r), (ΔT_Casing Top / ΔT_End Cover, Frequency Modulation Depth)}.

[0096] The key correlation feature pairs identified in the previous step and their corresponding correlation metrics (e.g., Pearson correlation coefficient, maximum lag correlation coefficient, MIC value) are organized into a streamlined feature correlation matrix. This matrix only includes relationships between key features deemed to be significantly correlated. For example, if the linear correlation between ΔT_CasingTop and f_r and the nonlinear correlation between ΔT_CasingTop / ΔT_EndCap and frequency modulation depth are identified, the matrix only displays the values ​​for these two correlations. For each pair of key correlation features, the correlation type (linear, lag, nonlinear) and correlation strength are recorded. This feature correlation matrix condenses the most valuable correlation information from the thermal and capacitance field data, providing a foundation for dynamically adjusting data fusion weights based on different operating conditions. For example, the element M_corr[i][j] in the matrix M_corr represents the correlation strength (e.g., the absolute value of the Pearson correlation coefficient or MIC value) between the i-th thermal feature parameter and the j-th capacitance feature parameter. If the correlation is not significant, it is 0.

[0097] Preferably, the dynamic allocation of the dual-field reliability weights in step S3 is specifically as follows:

[0098] Identify the motor operating condition based on the characteristic correlation matrix and obtain the operating condition type identification;

[0099] Perform dual-field signal quality assessment on the working condition type identification and feature correlation matrix to obtain a quality score table;

[0100] Dynamically allocate weights based on the quality score table, working condition type identification, and feature correlation matrix to obtain the fusion weight coefficient;

[0101] The overall credibility index of the slip rate under the current working condition is calculated based on the fusion weight coefficient and the quality score table to obtain the fusion credibility index.

[0102] In this embodiment of the present invention, a feature correlation matrix and the current synchronous data pair (thermal feature vectors and rotor slot frequency feature spectra) are used to identify the current operating condition of the motor. Several typical motor operating condition types are predefined, including: 1) steady-state light load (load less than 30% of rated load, stable temperature rise, and minimal speed fluctuation); 2) steady-state heavy load (load greater than 70% of rated load, high but stable temperature rise, and minimal speed fluctuation); 3) startup (rapid speed and temperature changes); 4) variable load (frequent load fluctuations, dynamic speed and temperature changes); 5) no-load; and 6) fault conditions (e.g., bearing faults causing abnormally high bearing seat temperature, stator winding faults causing local overheating). Using a rule-based approach or a trained classification model (e.g., a decision tree or support vector machine), the current motor state is classified into the most appropriate operating condition type based on key parameter features in the current synchronous data pair (e.g., mean and standard deviation of rotor speed, housing top temperature rise rate, temperature rise difference with ambient temperature, slot pass frequency stability index, etc.) and the strength of the inter-feature correlations reflected in the feature correlation matrix. For example, if the rotor speed is close to the synchronous speed and the temperature rise rate is close to zero, it is identified as a steady-state light load; if the speed is far below the synchronous speed and the temperature rise rate is large, it is identified as the startup process. The identified operating condition type and the duration of the operating condition are output as the operating condition type identifier.

[0103] After identifying the current motor operating conditions, the signal quality and reliability of the thermal signature vector and rotor slot frequency signature spectrum under these conditions are evaluated. A quality score is calculated for the thermal signature vector. For example, under steady-state conditions, temperature stability is a key indicator. The ratio of the standard deviation of the temperature rise at the top of the housing over the last minute to the average temperature rise is calculated. A smaller ratio indicates a higher quality score. The consistency of hotspot identification is also evaluated. If the location and number of characteristic hotspots change frequently within a short period of time, the thermal signature quality is considered low. A quality score is also calculated for the rotor slot frequency signature spectrum. For example, the ratio of the standard deviation of the precise slot frequency over the last second to the average value is calculated to reflect the stability of the speed measurement. A smaller ratio indicates a higher quality score. The ratio of the main frequency to harmonic energy in the spectrum is also evaluated. Abnormally high harmonic energy or a diffuse spectrum indicates strong signal interference, resulting in a lower quality score. The quality assessment also references historical data showing typical signal quality under different operating conditions. The thermal signature quality scores and slot signature quality scores are organized into a quality score table. For example, the quality score table contains [thermal feature quality score (range 0-1), slot pass feature quality score (range 0-1)].

[0104] Based on the quality score table, the current operating condition type identifier, and the strength of inter-feature correlations reflected in the feature correlation matrix, the weight coefficients for the fusion of the thermal feature vector and the rotor slot frequency signature in the subsequent slip calculation are dynamically calculated. An adaptive algorithm is used for weight assignment. For example, the following logic can be used: the thermal feature weight w_heat and the slot pass feature weight w_slot are initialized so that w_heat + w_slot = 1. According to the quality score table, if the thermal feature quality score is higher than the slot pass feature quality score, w_heat is initially increased; otherwise, w_slot is increased. Furthermore, the operating condition type is considered: during startup and high-speed load-variable conditions, where speed changes rapidly, the rotor slot frequency signature can better reflect the speed in real time, so w_slot is assigned a higher basic weight (e.g., 0.7). Under steady-state heavy-load conditions, where the temperature rise is more consistently correlated with copper loss (slip squared), w_heat is assigned a higher basic weight (e.g., 0.6). Combined with the feature correlation matrix: If under the current working conditions, a parameter in the thermal feature shows a strong correlation with a parameter in the slot pass feature (for example, the correlation coefficient is close to 1), this indicates that the two are highly consistent in the current state and can be weighted more directly according to the quality score. If the correlation is weak, it indicates that there is a problem with one of the signals or the correlation is not strong under the current working conditions, and the weights need to be allocated with caution. The formula for dynamic weight adjustment can be designed as: w_heat=f_heat(quality score_heat, quality score_slot, working condition type, feature correlation matrix), w_slot=1-w_heat. The function f_heat is a mapping function that dynamically calculates the weight of the thermal feature based on the input parameters. For example, simple linear weighting: w_heat= Quality score_heat+ Working condition basic weight_heat+ Average correlation strength_heat, where , , is the adjustment coefficient. The basic operating condition weight and average correlation strength are determined based on the operating condition type and the feature correlation matrix. More complex nonlinear mapping or fuzzy logic-based reasoning systems can also be used to calculate the weight. Ultimately, the current thermal feature fusion weight w_heat and the slot feature fusion weight w_slot are obtained to form the fusion weight coefficient.

[0105] According to the dynamically determined fusion weight coefficient and quality score table, the overall credibility index of the current slip estimation result is calculated. The credibility index reflects the reliability of the current measurement result. The calculation method comprehensively considers the intrinsic quality of the data source and the consistency after the fusion of the two. For example, the credibility index Confidence_Index can be calculated as: Confidence_Index=w_heat·quality score_heat+w_slot·quality score_slot+Consistency_Score. Consistency_Score is a score that measures the consistency between the slip results estimated independently by the thermal feature and the slot pass feature. If the slip estimation values ​​obtained by the two methods are very close in the preliminary slip estimation step, the Consistency_Score is high, otherwise it is low. Consistency_Score can be calculated as

[0106] 1-|s_heat_estimate-s_slot_estimate| / max(|s_heat_estimate|,|s_slot_estimate|,ε), where s_heat_estimate and s_slot_estimate are the independent slip estimates from the two methods, and ε is a small positive number to prevent division by zero. The weighted average quality score and consistency score are combined according to a preset ratio (for example, 60% quality score, 40% consistency score) to form the final fusion confidence index, which is typically normalized to a range between 0-100 or 0-1. For example, Confidence_Index = 0.6 × (w_heat · quality score_heat + w_slot · quality score_slot) + 0.4 × Consistency_Score. This fusion confidence index provides a reference for the reliability of subsequent slip results.

[0107] Preferably, the conductor temperature characteristic analysis in step S4 is specifically as follows:

[0108] Extract the key area temperature of the thermal feature vector to obtain the key component temperature set;

[0109] Conductor temperature distribution mapping is performed on the temperature set of key components to obtain a conductor temperature distribution diagram;

[0110] Calculate the copper resistance temperature coefficient according to the conductor temperature distribution diagram to obtain the resistance temperature correction value;

[0111] Analyze bearing lubrication characteristics based on key component temperature sets to obtain the lubrication viscosity change rate;

[0112] Perform mechanical loss temperature mapping based on the lubrication viscosity change rate and key component temperature set to obtain the mechanical loss correction factor;

[0113] Estimate the core loss temperature characteristics based on the key component temperature set and obtain the core loss temperature coefficient;

[0114] The temperature correction coefficient is obtained by integrating the resistance temperature correction value, the mechanical loss correction factor and the iron loss temperature coefficient according to the dual-source slip value.

[0115] In this embodiment of the present invention, surface temperature information highly correlated with the temperature of key internal motor components is extracted from the thermal feature vector. These key components include the stator winding, rotor winding (or squirrel cage bars), stator core, rotor core, and front and rear bearings. Although the infrared thermal imager measures the motor's surface temperature, pre-established thermal models or empirical relationships can be used to infer the approximate temperature of internal components from the surface temperature. For example, the average temperature rise in the top area of ​​the casing is correlated with the temperature rise of the stator core and stator winding; the temperature rise ratio in the area of ​​the casing directly facing the rotor is correlated with rotor copper loss, indirectly reflecting rotor temperature; and the temperature of the end cover and bearing seat areas is correlated with bearing temperature. Values ​​such as the maximum temperature at the top of the casing, the average temperature of the end cover, and the characteristic hot spot temperature of the bearing seat are extracted from the thermal feature vector. These extracted temperature values ​​are aggregated to form a key component temperature set. For example, the key component temperature set can be a vector [T_CasingTop_max, T_EndCover_avg, T_BearingSeat_Front, T_BearingSeat_Rear].

[0116] Using a set of key component temperatures, a pre-established motor thermal model or a data-driven mapping model is used to estimate the average operating temperatures of the stator windings and rotor conductors. The motor thermal model can be a lumped parameter thermal network model, simplifying the motor components into a network of thermal capacitances and thermal resistors. Internal temperatures are calculated using surface temperatures as boundary conditions. Data-driven models can establish a regression relationship between surface temperature and internal conductor temperature based on extensive experimental data. For example, the estimated stator winding temperature, T_stator, can be calculated as [α × T_case_top_max + β × T_end_cover_avg + γ × ambient temperature + δ], where α, β, γ, and δ are coefficients determined experimentally or through simulation. For squirrel-cage induction motors, the rotor conductor temperature, T_rotor, can be estimated indirectly by comparing the temperature rise ratio of specific housing regions with the slip, or using a more detailed thermal model. The resulting estimated average stator winding and rotor conductor temperatures constitute a conductor temperature profile (albeit a simplified average temperature profile). For example, a conductor temperature profile contains [T_stator, T_rotor].

[0117] Based on the estimated average stator winding temperature T_stator and the average rotor conductor temperature T_rotor, calculate the correction factor of the stator and rotor copper conductor resistance relative to the standard temperature (usually 25°C or 75°C). The resistivity of copper (or other conductor materials) varies approximately linearly with temperature: ,in is the reference temperature The resistivity under is the temperature coefficient of resistance (for copper, around 20°C).

[0118] Therefore, the resistance value also changes with temperature: Calculate the stator resistance correction factor Similarly, the rotor resistance correction factor is calculated as Here and is the stator and rotor resistance at reference temperature The nominal value under . Get the resistance temperature correction value, such as .

[0119] Estimate the bearing operating temperature using the bearing seat temperatures (T_bearing_seat_front, T_bearing_seat_rear), where the temperatures of key components are concentrated. Bearing temperature directly affects the viscosity of the grease or oil. The viscosity of most lubricants decreases with increasing temperature. Using the lubricant's viscosity-temperature curve or empirical formula, calculate the rate of change of the lubricant's viscosity at the bearing operating temperature relative to the viscosity at the standard operating temperature. For example, the viscosity change ratio η_ratio = viscosity(T_bearing) / viscosity(T_standard). Higher bearing temperature results in lower viscosity, and friction generally decreases with decreasing viscosity (within a certain range). This yields the lubricant viscosity change ratio.

[0120] The mechanical losses of a motor mainly include bearing friction loss and windage loss. Bearing friction loss is related to bearing type, load, speed, and lubrication status (viscosity). Windage loss is related to speed and duct structure, and is also affected by air density (which varies with temperature and altitude). Using the estimated bearing temperature and lubrication viscosity change rate, a mechanical loss model is used to calculate the change in bearing friction loss with temperature. For example, the friction torque M_friction ≈ C × η_ratio × , where C is a constant and β is typically close to 1. Windage loss also varies slightly as the air density changes due to rising internal motor temperature. Taking these factors into account, calculate the correction factor for total mechanical loss at the current temperature relative to the mechanical loss at the standard temperature: k_mech_loss = mechanical loss (T_bearing, T_internal air) / mechanical loss (T_standard). This yields the mechanical loss correction factor.

[0121] A motor's iron loss consists of hysteresis loss and eddy current loss, primarily occurring in the stator and rotor cores. Iron loss is affected by magnetic flux density, frequency, and core material temperature. Hysteresis loss decreases slightly with increasing temperature, while eddy current loss decreases due to the increased resistivity caused by increasing temperature. Using the estimated stator core temperature (which can be inferred from the temperature at the top of the casing), where key component temperatures are concentrated, and the estimated rotor core temperature, empirical formulas or lookup tables are used to estimate the change in iron loss at the current temperature relative to the standard temperature. For example, the iron loss temperature coefficient k_iron_loss = iron_loss(T_core) / iron_loss(T_standard) is calculated. This yields the iron loss temperature coefficient.

[0122] The resistance temperature correction values ​​(k_R_stator, k_R_rotor), the mechanical loss correction factor k_mech_loss, and the iron loss temperature coefficient k_iron_loss are integrated to form a set of temperature correction coefficients for subsequent slip temperature compensation. These correction coefficients are used to adjust the resistance, mechanical loss, and iron loss parameters in the motor equivalent circuit model to reflect the current actual temperature conditions. This integration also requires reference to the dual-source slip value, as slip itself affects the rotor current and frequency, which indirectly affects the iron loss and partial loss distribution. For example, the temperature correction coefficients can be a data structure containing all correction factors: {k_R_stator, k_R_rotor, k_mech_loss, k_iron_loss}. These coefficients are key inputs for temperature compensation in the subsequent slip calculation model, ensuring that the calculation results are not affected by motor temperature changes.

[0123] Preferably, the temperature characteristic compensation in step S4 is specifically as follows:

[0124] The motor equivalent parameters are constructed based on the temperature correction coefficient and the preliminary fusion slip rate to obtain the temperature-corrected motor parameters;

[0125] The rotor resistance main effect is calculated for the preliminary fusion slip according to the temperature correction coefficient to obtain the rotor main effect coefficient;

[0126] The stator-rotor interaction coefficient is obtained by analyzing the stator impedance interaction effect based on the temperature correction coefficient, temperature-corrected motor parameters and rotor main effect coefficient;

[0127] The mechanical loss impact of the initial fusion slip rate was evaluated to obtain the mechanical impact coefficient;

[0128] Perform temperature correction on magnetic characteristics according to temperature correction coefficient and temperature-corrected motor parameters to obtain magnetic characteristic coefficient;

[0129] Generate a comprehensive compensation factor based on the rotor main effect coefficient, fixed-rotor interaction coefficient, mechanical influence coefficient and magnetic characteristic coefficient;

[0130] The slip compensation is iteratively optimized based on the preliminary fusion slip, comprehensive compensation factor and temperature-corrected motor parameters to obtain the optimized slip.

[0131] In the embodiment of the present invention, the temperature correction coefficients (k_R_stator, k_R_rotor, k_mech_loss, k_iron_loss) and the preliminary fusion slip s_preliminary are used to construct the motor equivalent circuit parameters under the current temperature and slip. A commonly used single-phase equivalent circuit model of a squirrel cage asynchronous motor includes the stator resistance , stator leakage reactance , magnetizing reactance X_m, iron loss resistance R_c, rotor resistance (Converted to the stator side), rotor leakage reactance (converted to the stator side), and the resistance representing the mechanical load R_mech= (1-s) / s. Update temperature-related parameters according to the temperature correction coefficient: current stator resistance _temp=k_R_stator× _standard; current rotor resistance _temp=k_R_rotor× _standard; Current iron loss resistance R_c_temp ≈ R_c_standard / k_iron_loss (iron loss increases, equivalent resistance decreases). Leakage reactance and magnetizing reactance are less affected by temperature and are usually considered constants or slightly modified. Mechanical load equivalent resistance R_mech_temp _temp×(1-s_preliminary) / s_preliminary. These corrected parameters are combined to form the temperature-corrected motor parameters. For example, the temperature-corrected motor parameters can be a set containing all corrected equivalent circuit parameters { _temp, ,X_m,R_c_temp, _temp, ,R_mech_temp}.

[0132] Analyze the main effect of temperature increase on slip rate, namely rotor resistance According to the torque equation or power balance relationship of the asynchronous motor, when the output torque is constant, the rotor resistance The increase in s will lead to an increase in the slip rate s. This main effect can be approximated by a simple proportional relationship or equivalent circuit analysis. For example, when ignoring the stator impedance voltage drop and magnetic circuit saturation changes, the torque .when When the reactance changes are small, and , the torque is approximately equal to is proportional to, so at the same torque, That is, the slip rate is approximately linearly related to the rotor resistance. Calculate the main increment of slip rate caused by the increase of rotor resistance .For example, This increment represents the direct and main influence of the rotor resistance temperature change on the initial fusion slip rate, and the rotor main effect coefficient is obtained, for example, the rotor main effect coefficient = .

[0133] Considering stator resistance and leakage reactance The effect of temperature variations (although relatively small) and their interaction with rotor parameters on slip. The increase of will increase the stator voltage drop and reduce the air gap voltage, which will affect the magnetic flux and torque, and thus affect the slip rate. This effect is relatively complex and requires consideration of the entire equivalent circuit. And the preliminary fusion slip s_preliminary, the input impedance, input current, air gap voltage, air gap power and other parameters at the current temperature are calculated through the motor equivalent circuit model. For example, the input impedance Calculate the air gap voltage The torque is related to the air gap power. from _standard changes to _temp, or How small changes affect the air-gap voltage and torque, and thus the slip change required to achieve the same torque. This change, Δs_stator_interaction, is defined as the slip correction due to the stator-rotor interaction effect. The stator-rotor interaction coefficient is calculated, for example, as Δs_stator_interaction. This coefficient reflects the indirect effect of stator parameter temperature changes on slip through equivalent circuit coupling.

[0134] Mechanical losses (bearing friction and windage) generate a mechanical resistance torque that is dual to the speed. This resistance torque requires the motor's electromagnetic torque to overcome, consuming some air gap power and affecting the slip. The impact of mechanical losses on slip at the current temperature is assessed using the preliminary fused slip s_preliminary and the mechanical loss correction factor k_mech_loss. Mechanical loss power P_mech_loss = P_mech_loss_standard × k_mech_loss. Mechanical loss torque T_mech_loss = P_mech_loss / ω_r, where ω_r is the rotor angular velocity. Of the total electromagnetic torque T_em, a portion is used to overcome T_mech_loss, and the remainder is provided to the load torque T_load. T_em = T_load + T_mech_loss. Because torque is related to slip, changes in mechanical losses can cause a slight change in slip, Δs_mech. For example, greater mechanical losses require greater electromagnetic torque to achieve the same load torque, resulting in a slight increase in slip. The mechanical influence coefficient is calculated, for example, as Δs_mech.

[0135] The motor's magnetizing characteristics (magnetizing reactance X_m) and core loss (R_c) are also affected by temperature. Rising temperature typically causes a slight decrease in the magnetic permeability of the core material, which in turn affects the magnetizing reactance X_m. The core loss resistance R_c is related to eddy current loss, which is affected by the core resistivity. As the resistivity increases with temperature, this decreases eddy current loss and increases the equivalent core loss resistance R_c. The temperature correction factor k_iron_loss and the temperature-corrected motor parameters {..., X_m, R_c_temp,...} are used to evaluate the effect of temperature variations in magnetic characteristics (primarily X_m and R_c) on slip. While these effects are relatively minor, they should still be considered for improved accuracy. For example, the slip correction Δs_magnetic caused by a small change in magnetizing reactance ΔX_m and core loss resistance ΔR_c_temp is calculated. This yields the magnetic characteristic coefficient, for example, Δs_magnetic = Δs_magnetic.

[0136] The various temperature-induced slip corrections (Δs_R2 due to the rotor main effect, Δs_stator_interaction due to stator-rotor interaction, Δs_mech due to mechanical losses, and Δs_magnetic due to magnetic properties) are integrated. These corrections represent the temperature-induced slip compensation. The combined compensation factor, Δs_compensation = Δs_R2 + Δs_stator_interaction + Δs_mech + Δs_magnetic, converts the temperature effects of various motor components into an overall slip correction.

[0137] Use the preliminary fusion slip rate s_preliminary as the initial value and apply the comprehensive compensation factor to correct it to obtain the slip rate estimate after the first temperature compensation. =s_preliminary+Δs_compensation. However, the compensation factor Δs_compensation itself is calculated based on s_preliminary, and the temperature correction factor R_mech_temp also depends on the slip rate. Therefore, it is necessary to use an iterative optimization method to improve the accuracy of the compensation. Update R_mech_temp in the temperature-corrected motor parameters, recalculate the fixed-rotation interaction coefficient, mechanical influence coefficient and magnetic characteristic coefficient, and generate a new comprehensive compensation factor Calculate the slip rate after the second compensation

[0138] Repeat this process to calculate , ,...until the difference in slip rates calculated between two adjacent iterations is less than a preset convergence threshold (e.g., The final converged slip value is the optimized slip after temperature compensation. This iterative process ensures that the temperature compensation is self-consistent with the current slip estimate. The converged optimized slip is ultimately output.

[0139] Preferably, the stator impedance interaction effect analysis is specifically as follows:

[0140] Perform current distribution balance analysis based on the temperature correction coefficient to obtain the current distribution change rate;

[0141] Correct the air gap magnetic field intensity according to the current distribution change rate to obtain the magnetic field intensity correction coefficient;

[0142] Calculate the rotor induced electromotive force according to the magnetic field strength correction coefficient to obtain the induced electromotive force coefficient;

[0143] The torque balance coefficient is obtained by analyzing the torque balance characteristics according to the induced electromotive force coefficient;

[0144] Evaluate the stator leakage reactance temperature effect based on the torque balance coefficient to obtain the leakage reactance effect coefficient;

[0145] The fixed-rotation coupling factor is determined by taking the current distribution change rate, magnetic field strength correction coefficient, induced electromotive force coefficient, torque balance coefficient and leakage reactance effect coefficient into consideration to obtain the fixed-rotation coupling factor;

[0146] The interaction coefficient is comprehensively calculated for the stator-rotor coupling factor, the temperature-corrected motor parameters and the rotor main effect coefficient to obtain the stator-rotor interaction coefficient.

[0147] In the embodiment of the present invention, the temperature correction coefficient, especially the stator resistance correction coefficient k_R_stator, is used to analyze the effect of the increase in stator winding resistance on the balance of three-phase current distribution. Although the power supply is three-phase symmetrical, the temperature increase of the winding resistance (even if it is a uniform increase) will change the impedance of the equivalent circuit. Especially when the internal resistance of the power supply or the connection impedance is considered, this impedance change will cause a slight imbalance in the amplitude or phase of the three-phase current. _temp, ,X_m,R_c_temp, _temp, And the preliminary fusion slip rate s_preliminary is used to establish a complete three-phase equivalent circuit model including the power supply internal resistance R_s and X_s. Under this model, the currents I_a, I_b, and I_c flowing through the three-phase stator windings when a symmetrical three-phase voltage is applied are calculated. Analyze whether the amplitude and phase of these three-phase currents deviate from the ideal symmetry. Calculate the current imbalance, for example, using the imbalance defined in the IEC standard V_unbalance = (the maximum value of the difference between the highest phase current and the average current) / average current. This imbalance reflects the change in current distribution, forming a current distribution change rate. For example, the current distribution change rate can be a numerical value representing the imbalance.

[0148] Unbalanced current distribution causes the rotating magnetic field in the motor air gap to no longer be an ideal circle, but rather an elliptical shape or contain harmonic components. This magnetic field distortion affects the fundamental amplitude of the air gap magnetic field. Using the current distribution change rate calculated in the previous step, the motor magnetic circuit model or empirical formula is used to calculate the correction of the air gap magnetic field fundamental amplitude relative to the ideal symmetrical current case. For example, if the current imbalance is ε_I, the air gap magnetic field strength correction factor k_B_gap ≈ 1-c×ε_ , where c is a coefficient related to motor design. This correction factor k_B_gap reflects the impact of current imbalance on the air gap magnetic field strength, forming a magnetic field strength correction factor.

[0149] The fundamental amplitude of the air gap magnetic field directly determines the amplitude of the induced electromotive force in the rotor winding. ≈4.44× × _eff×Φ_gap, where is the rotor current frequency ( =s× ), _eff is the equivalent number of rotor turns, and Φ_gap is the air gap flux. The air gap flux Φ_gap is proportional to the air gap magnetic field strength B_gap. Using the magnetic field strength correction factor k_B_gap, the air gap flux can be corrected or the rotor induced electromotive force can be directly corrected. For example, the corrected rotor induced electromotive force is _temp≈ _standard × k_B_gap. This corrected electromotive force value reflects the effect of the air gap magnetic field change on the rotor induced electromotive force, forming the induced electromotive force coefficient, for example, the induced electromotive force coefficient = k_B_gap.

[0150] The change of rotor induced electromotive force directly affects the rotor current and electromagnetic torque. The electromagnetic torque T_em is approximately proportional to the product of the air gap flux and the rotor current, and is related to the rotor power factor. Using the induced electromotive force coefficient k_B_gap, the rotor induced electromotive force is updated to _temp, combined with the temperature of the motor parameters _temp and , calculate the corrected rotor current I2'_temp. Analyze the change in slip required to achieve the same load torque T_load under the new rotor current and air gap magnetic field conditions. For example, under constant load torque, T_load = T_em - T_mech_loss. Calculate the electromagnetic torque T_em(s, temperature correction parameter) using the corrected equivalent circuit parameters and slip s. Solve the equation T_em(s, temperature correction parameter) = T_load + T_mech_loss to obtain the new slip that satisfies the torque balance. The torque balance coefficient can be expressed as the difference between the new slip rate and the preliminary fusion slip rate Δs_torque= -s_preliminary. Get the torque balance coefficient.

[0151] Stator leakage reactance It is mainly caused by the end leakage and slot leakage of the stator winding. Its value is slightly affected by the winding size and shape, as well as the temperature on the conductor resistivity and the dielectric constant of the insulation material. Although it is generally believed that the leakage reactance is only slightly affected by temperature, it still needs to be evaluated in high-precision calculations. The torque balance coefficient reflects the change in slip required to achieve torque balance after considering factors such as resistance and magnetic field. This change Δs_torque includes the impact of small changes in stator leakage reactance. For example, through sensitivity analysis, the stator leakage reactance can be evaluated. Minor changes The effect on the torque-slip curve can be used to estimate the slip change Δs_X1 required to achieve the same torque. This Δs_X1 can be separated from Δs_torque or calculated using an independent model as a measure of the stator leakage reactance temperature effect. The leakage reactance effect coefficient is obtained, for example, leakage reactance effect coefficient = Δs_X1.

[0152] Intermediate quantities such as the current distribution change rate, magnetic field strength correction factor, induced electromotive force coefficient, torque balance coefficient, and leakage reactance effect coefficient, which reflect how stator-side temperature changes affect the air gap and rotor-side parameters, ultimately affecting torque and slip, are integrated. Together, these intermediate quantities describe the degree of "interaction" or "coupling" between stator parameter temperature changes and rotor behavior. For example, current imbalance causes magnetic field distortion (via k_B_gap), which in turn affects the rotor electromotive force (via the induced electromotive force coefficient), which in turn affects the rotor current and torque (via the torque balance coefficient). Changes in stator leakage reactance also directly affect the stator impedance and air gap voltage, indirectly affecting torque (via the leakage reactance effect coefficient). The combined impact of these factors can be quantified as one or more stator-rotor coupling factors. For example, the stator-rotor coupling factor can be a vector [current distribution change rate, k_B_gap, induced electromotive force coefficient, torque balance coefficient, leakage reactance effect coefficient].

[0153] Using the various interaction effect information contained in the fixed-rotor coupling factor, combined with the temperature-corrected motor parameters { , ,...} and the rotor main effect coefficient (k_R_rotor-1), calculate the final stator-rotor interaction coefficient Δs_stator_interaction. The stator-rotor interaction coefficient is the total additional effect of the stator side temperature change on the slip through complex electromagnetic coupling. This influence quantity Δs_stator_interaction is not simply linear, it depends on the current operating state (reflected by the temperature-corrected motor parameters and the preliminary slip). For example, a function f_interaction based on the equivalent circuit model can be used, whose input is the stator-rotor coupling factor, the temperature-corrected motor parameters and the rotor main effect coefficient, and the output is Δs_stator_interaction = f_interaction (stator-rotor coupling factor, temperature-corrected motor parameters, rotor main effect coefficient). This function calculates the impact on the slip by simulating the chain reaction caused by changes in parameters such as stator resistance and leakage reactance in the equivalent circuit. For example, by calculating = _temp, = _temp (assuming slight changes) isothermal parameters, the slip rate required to achieve the same load torque as at standard temperature , then Δs_stator_interaction≈ -s_preliminary - Δs_R2_simple, where Δs_R2_simple is an approximate increment of slip due to a simple change in rotor resistance. This calculation requires iteratively solving the equivalent circuit equation to ultimately obtain the precise stator-rotor interaction coefficient, Δs_stator_interaction.

[0154] 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.

[0155] 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 method for measuring the slip rate of a three-phase asynchronous motor, characterized in that: The following steps are involved: Step S1: performing temperature gradient analysis on different regions of the three-phase asynchronous motor to obtain regional hotspot data and a temperature gradient matrix; calculating the copper loss temperature rise ratio based on the regional hotspot data and the temperature gradient matrix to obtain a thermal characteristic vector; Step S2: measuring the capacitance of the motor stator in the air gap region to obtain a fused capacitance signal; extracting rotor period features from the fused capacitance signal to obtain a rotor slot frequency characteristic spectrum; Step S3: Correlate the thermal field-capacitance field characteristics of the thermal feature vector and the rotor slot frequency feature spectrum to obtain a feature correlation matrix; According to the characteristic correlation matrix, the dual-field reliability weights under different working conditions are dynamically allocated to obtain the fusion credibility index; Step S4: Calculate preliminary slip estimation values ​​based on the thermal eigenvector and the rotor slot frequency characteristic spectrum to obtain a dual-source slip value; analyze the conductor temperature characteristics according to the thermal eigenvector to obtain a temperature correction coefficient; The dual-source slip values ​​are weighted mapped and weighted fused with the dual-field slip according to the fusion credibility index to obtain the preliminary fused slip. The preliminary fused slip is temperature-corrected according to the temperature correction coefficient to obtain the optimized slip.

2. The slip measurement method of a three-phase asynchronous motor according to claim 1, characterized in that: The temperature gradient analysis in step S1 is specifically as follows: The running three-phase asynchronous motor is scanned and imaged in all directions by an infrared thermal imager at a frequency of more than 5 Hz to obtain the original thermal image sequence; Filter the thermal field noise of the original thermal map sequence to obtain the corrected thermal map; The corrected heat map is divided into functional areas, and then the hotspot area features are extracted to obtain regional hotspot data; The heat flux density gradient is extracted from the corrected thermal map according to the regional hotspot data to obtain the temperature gradient matrix.

3. The slip measurement method of a three-phase asynchronous motor according to claim 1, characterized in that: The calculation of the copper consumption temperature rise ratio in step S1 is specifically as follows: Establish the cold state benchmark temperature for the regional hot spot data and obtain the regional cold state temperature table; Calculate the relative temperature rise based on the regional cold temperature table and regional hot spot data to obtain the regional temperature rise data; Analyze the temperature rise time characteristics of regional temperature rise data to obtain the temperature rise dynamic characteristics; The regional temperature rise ratio is constructed based on the regional temperature rise data and the temperature gradient matrix to obtain the regional temperature rise ratio set; Extract slip rate thermal related parameters based on the regional temperature rise ratio set and temperature rise dynamic characteristics to obtain slip rate thermal characteristic parameters; Integrate the slip rate thermal characteristic parameters and the regional temperature rise ratio set to obtain temperature rise characteristic data; Multi-dimensional thermal features are constructed based on regional hotspot data, temperature gradient matrix and temperature rise characteristic data to obtain thermal feature vectors.

4. The slip measurement method of a three-phase asynchronous motor according to claim 1, characterized in that: The specific measurement of the air gap region capacitance in step S2 is: Obtaining the structural parameters of the inner surface of the motor stator; installing capacitive sensors along the A, B, and C phases of the three-phase winding of the motor stator according to the structural parameters of the inner surface of the motor stator, with each capacitive sensor having a size not exceeding 5 mm × 5 mm × 1 mm, to form a sensor position map; According to the sensor position diagram, each capacitance sensor is driven by a high-frequency oscillation circuit to capture the air gap characteristic changes caused by three-phase imbalance and obtain the original capacitance signal; Performing response signal noise reduction on the original capacitance signal to obtain a noise-reduced capacitance signal; The noise reduction capacitor signal is spatially fused according to the sensor position map to obtain a fused capacitor signal.

5. The slip measurement method of a three-phase asynchronous motor according to claim 1, characterized in that: The rotor period feature extraction in step S2 is specifically as follows: Performing slot-tooth modulation characteristic analysis on the fused capacitor signal to obtain spectrum data; Accurately locate the slot frequency of the spectrum data to obtain the precise slot frequency; The harmonic structure characteristics of the spectrum data are constructed according to the precise slot frequency to obtain the harmonic relationship parameters; Perform frequency modulation depth analysis on spectrum data based on precise slot frequency to obtain frequency modulation parameters; The phase modulation characteristics of the fused capacitor signal are extracted according to the precise slot frequency to obtain the phase modulation characteristics; Perform load characteristic correlation analysis on harmonic relationship parameters, frequency modulation parameters and phase modulation characteristics to obtain load correlation characteristics; Extract the speed information from the precise slot frequency and spectrum data to obtain the speed characteristic data; The rotor slot frequency characteristic spectrum is constructed based on the speed characteristic data and load correlation characteristics.

6. The slip measurement method of a three-phase asynchronous motor according to claim 1, characterized in that: The thermal field-capacitive field characteristic correlation in step S3 is specifically as follows: Perform thermal field-capacitance field time series alignment on the thermal eigenvector and rotor slot frequency characteristic spectrum to obtain synchronized data pairs; Extract key parameter features of synchronized data pairs; Calculate the basic correlation coefficients of key parameter features to obtain the original correlation matrix; Conduct lag correlation analysis on key parameter characteristics to obtain lag correlation data; Perform nonlinear correlation mining on key parameter features to obtain nonlinear correlation data; Perform correlation significance test on the original correlation matrix, lagged correlation data and nonlinear correlation data to obtain the significance test results; According to the significance test results, key related features are screened to obtain key related feature pairs; Construct a feature correlation matrix based on key correlated feature pairs.

7. The slip measurement method of a three-phase asynchronous motor according to claim 1, characterized in that: The dynamic allocation of dual-field reliability weights in step S3 is specifically as follows: Identify the motor operating condition based on the characteristic correlation matrix and obtain the operating condition type identification; Perform dual-field signal quality assessment on the working condition type identification and feature correlation matrix to obtain a quality score table; Dynamically allocate weights based on the quality score table, working condition type identification, and feature correlation matrix to obtain the fusion weight coefficient; The overall credibility index of the slip rate under the current working condition is calculated based on the fusion weight coefficient and the quality score table to obtain the fusion credibility index.

8. The slip measurement method of a three-phase asynchronous motor according to claim 1, characterized in that: The conductor temperature characteristic analysis in step S4 is specifically as follows: Extract the key area temperature of the thermal feature vector to obtain the key component temperature set; Conductor temperature distribution mapping is performed on the temperature set of key components to obtain a conductor temperature distribution diagram; Calculate the copper resistance temperature coefficient according to the conductor temperature distribution diagram to obtain the resistance temperature correction value; Analyze bearing lubrication characteristics based on key component temperature sets to obtain the lubrication viscosity change rate; Perform mechanical loss temperature mapping based on the lubrication viscosity change rate and key component temperature set to obtain the mechanical loss correction factor; Estimate the core loss temperature characteristics based on the key component temperature set and obtain the core loss temperature coefficient; The temperature correction coefficient is obtained by integrating the resistance temperature correction value, the mechanical loss correction factor and the iron loss temperature coefficient according to the dual-source slip value.

9. The slip measurement method of a three-phase asynchronous motor according to claim 1, characterized in that: The temperature characteristic compensation in step S4 is specifically as follows: The motor equivalent parameters are constructed based on the temperature correction coefficient and the preliminary fusion slip rate to obtain the temperature-corrected motor parameters; The rotor resistance main effect is calculated for the preliminary fusion slip according to the temperature correction coefficient to obtain the rotor main effect coefficient; The stator-rotor interaction coefficient is obtained by analyzing the stator impedance interaction effect based on the temperature correction coefficient, temperature-corrected motor parameters and rotor main effect coefficient; The mechanical loss impact of the initial fusion slip rate was evaluated to obtain the mechanical impact coefficient; Perform temperature correction on magnetic characteristics according to temperature correction coefficient and temperature-corrected motor parameters to obtain magnetic characteristic coefficient; Generate a comprehensive compensation factor based on the rotor main effect coefficient, fixed-rotor interaction coefficient, mechanical influence coefficient and magnetic characteristic coefficient; The slip compensation is iteratively optimized based on the preliminary fusion slip, comprehensive compensation factor and temperature-corrected motor parameters to obtain the optimized slip.

10. The slip measurement method of a three-phase asynchronous motor according to claim 9, characterized in that: The stator impedance interaction effect analysis is as follows: Perform current distribution balance analysis based on the temperature correction coefficient to obtain the current distribution change rate; Correct the air gap magnetic field intensity according to the current distribution change rate to obtain the magnetic field intensity correction coefficient; Calculate the rotor induced electromotive force according to the magnetic field strength correction coefficient to obtain the induced electromotive force coefficient; The torque balance coefficient is obtained by analyzing the torque balance characteristics according to the induced electromotive force coefficient; Evaluate the stator leakage reactance temperature effect based on the torque balance coefficient to obtain the leakage reactance effect coefficient; The fixed-rotation coupling factor is determined by taking the current distribution change rate, magnetic field strength correction coefficient, induced electromotive force coefficient, torque balance coefficient and leakage reactance effect coefficient into consideration to obtain the fixed-rotation coupling factor; The interaction coefficient is comprehensively calculated for the stator-rotor coupling factor, the temperature-corrected motor parameters and the rotor main effect coefficient to obtain the stator-rotor interaction coefficient.

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