Electrical equipment condition monitoring and dynamic test evaluation system

Abstract

The present invention relates to the technical field of electrical equipment monitoring, and in particular to an electrical equipment state monitoring and dynamic test evaluation system, comprising a data acquisition module, a data processing module, a feature extraction module, a dynamic test evaluation module and a state evaluation and prediction module; wherein: the data acquisition module is used for real-time acquisition of raw data during the operation of the electrical equipment; the data processing module is used for processing the raw data collected by the data acquisition module to generate a standardized data set; the feature extraction module is used for extracting relevant feature parameters reflecting fatigue damage accumulation from the raw data; the dynamic test evaluation module is used for simulating the fatigue damage evolution process of the electrical equipment under complex working conditions; the present invention, by real-time monitoring of fatigue damage accumulation of the electrical equipment, combines dynamic test evaluation with finite element simulation technology, accurately predicts the remaining life of the equipment and the time of occurrence of a fault, and improves the accuracy and early warning capability of the equipment health state evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment monitoring, and in particular to an electrical equipment state monitoring and dynamic test evaluation system. Background Art

[0002] With the continuous improvement of industrialization and electrification, electrical equipment is increasingly used in energy, transportation, manufacturing and other fields. The operating status and life of electrical equipment are directly related to the safety and economic benefits of the entire system, so real-time monitoring and status evaluation of the equipment are particularly important. Traditional condition monitoring technology mainly relies on the collection and analysis of static data. It usually obtains equipment operating parameters (such as voltage, current, temperature, etc.) through a single sensor and combines statistical or empirical models for evaluation. However, these methods are difficult to fully reflect the true state of electrical equipment under complex working conditions, especially the fatigue damage characteristics accumulated during the long-term operation of the equipment cannot be effectively captured, resulting in difficulty in timely warning of potential equipment failures, which seriously affects the reliability and safety of the equipment.

[0003] The existing technology has significant deficiencies in the following aspects: First, there is a lack of real-time monitoring of the cumulative effect of fatigue damage on electrical equipment. Existing monitoring methods focus more on short-term failures and ignore long-term damage characteristics; second, there is a lack of dynamic test simulation conditions, which makes it difficult to reproduce the operating behavior of equipment under complex working conditions, resulting in insufficient understanding of the evolution law of fatigue damage; third, the prediction model is usually based on static analysis of fixed parameters, and it is difficult to update the health status of the equipment through real-time operation data, resulting in insufficient prediction accuracy and adaptability. Therefore, there is an urgent need for an electrical equipment status monitoring and dynamic test evaluation system to address the shortcomings of the existing technology. Summary of the invention

[0004] Based on the above objectives, the present invention provides an electrical equipment state monitoring and dynamic test evaluation system.

[0005] The electrical equipment condition monitoring and dynamic test evaluation system includes a data acquisition module, a data processing module, a feature extraction module, a dynamic test evaluation module, and a condition evaluation and prediction module; wherein:

[0006] Data acquisition module: used to collect raw data of electrical equipment in real time during operation, including stress change signals, vibration signals, temperature changes and long-term operation data;

[0007] Data processing module: used to filter noise, remove outliers and normalize the raw data collected by the data acquisition module to generate a standardized data set;

[0008] Feature extraction module: Based on the standardized data set output by the data processing module, relevant characteristic parameters reflecting fatigue damage accumulation are extracted from it, including fatigue crack growth rate, stress concentration factor and long-period damage accumulation energy;

[0009] Dynamic test evaluation module: Based on the characteristic parameters of the feature extraction module, by setting the dynamic test conditions of loading stress amplitude, frequency and environmental factors, combined with finite element dynamic simulation technology, it simulates the fatigue damage evolution process of electrical equipment under complex working conditions and generates equipment fatigue life curve and damage node prediction value;

[0010] Condition assessment and prediction module: The equipment fatigue life curve and damage node prediction value generated by the dynamic test assessment module are used in combination with the Bayesian update algorithm to build an equipment health assessment model. The remaining life of the equipment, the time of potential failure and the fatigue degree of the specified parts are predicted in real time through long-term operation data.

[0011] Optionally, the data acquisition module includes a stress sensing unit, a vibration sensing unit, a temperature sensing unit and an operation data recording unit; wherein:

[0012] Stress sensing unit: used to collect stress change signals of electrical equipment in real time during operation through distributed optical fiber stress sensing technology;

[0013] Vibration sensing unit: Based on low-frequency vibration monitoring technology, it is used to capture the mechanical vibration signals generated during the operation of electrical equipment and record the vibration amplitude and frequency in real time;

[0014] Temperature sensing unit: uses precision thermocouple sensors to collect temperature change data inside and outside the equipment and record real-time temperature change data;

[0015] Operation data recording unit: used to record the operation time and cycle information of the equipment and obtain long-term operation data.

[0016] Optionally, the data processing module includes a noise filtering unit, an outlier elimination unit, a normalization processing unit and a standardized data generation unit; wherein:

[0017] The noise filtering unit is used to suppress the noise of the raw data transmitted by the data acquisition module, using a bandpass filter to remove interference signals outside the predetermined frequency range, and combining a filtering algorithm to eliminate environmental noise and electromagnetic interference noise;

[0018] The outlier elimination unit is used to identify and eliminate outliers in the original data, screen the data distribution using statistical analysis methods, exclude values ​​that exceed the normal range by setting upper and lower thresholds, and use interpolation algorithms to complete the missing data after elimination;

[0019] A normalization processing unit is used to normalize the data after removing outliers, and map the data to a standard interval between 0 and 1 by setting a fixed minimum and maximum range;

[0020] The standardized data generation unit is used to integrate various types of data after normalization, synchronize the signal data according to a unified timestamp and generate a standardized data set for use by the feature extraction module.

[0021] Optionally, the feature extraction module includes a crack extension analysis unit, a stress concentration analysis unit, a damage accumulation energy calculation unit and a feature integration unit; wherein:

[0022] Crack growth analysis unit: used to extract fatigue crack growth rate from standardized data sets, and calculate crack growth rate based on the changes in collected stress signals using a cyclic load analysis model;

[0023] The stress concentration analysis unit is used to extract the stress concentration factors in the equipment operation from the standardized data set, analyze the distribution differences of stress in the predetermined parts of the equipment by using the multi-point stress distribution fitting method, and calculate the characteristic values ​​of the stress concentration area by combining finite element simulation;

[0024] Damage cumulative energy calculation unit: used to extract the damage cumulative energy of the equipment in the long-term operation data, decompose the periodic characteristics of the vibration signal based on the rain flow counting method, calculate the cumulative value of the energy dissipation of the equipment during long-term operation, and output the long-term damage cumulative energy parameter;

[0025] Feature integration unit: It is used to integrate the fatigue crack growth rate, stress concentration factor and long-period damage accumulation energy extracted by the above units to generate a comprehensive feature vector.

[0026] Optionally, the damage cumulative energy calculation unit specifically includes:

[0027] Signal decomposition: The vibration signal is first decomposed into multiple amplitude and frequency components, and the signal is cycle counted using the rain flow counting method; the rain flow counting method divides the vibration signal into periodic waveforms by identifying the rising and falling peaks in the signal to obtain the amplitude and cycle number of each cycle;

[0028] Energy dissipation calculation: Calculate the energy dissipation of each cycle based on the amplitude of each cycle after decomposition;

[0029] Long-term damage cumulative energy calculation: Based on the energy dissipation of each cycle, the total damage cumulative energy value of the equipment is calculated by accumulating the energy dissipation values ​​of all cycles;

[0030] Output long-period damage cumulative energy parameters: Output the long-period damage cumulative energy parameters of the device based on the total damage cumulative energy value.

[0031] Optionally, the dynamic test evaluation module includes a dynamic loading condition generation unit, a finite element model construction unit, a fatigue life calculation unit and a damage node prediction unit; wherein:

[0032] Dynamic loading condition generation unit: used to set the loading conditions of the dynamic test according to the fatigue crack growth rate, stress concentration factor and long-period damage accumulation energy extracted by the feature extraction module; the specific loading conditions include determining the loading stress amplitude according to the stress concentration factor, setting the loading frequency according to the fatigue crack growth rate, and setting the temperature and humidity in combination with the long-period damage accumulation energy and the equipment operating environment;

[0033] Finite element model building unit: used to build a finite element analysis model based on the physical structure and material properties of the equipment. The input of the finite element analysis model includes the characteristic parameters provided by the characteristic extraction module and the dynamic loading conditions output by the dynamic loading condition generation unit, so as to generate a simulation model that can simulate the evolution of fatigue damage of the equipment under complex working conditions;

[0034] Fatigue life calculation unit: used to simulate the fatigue damage evolution process of equipment under complex working conditions based on the simulation model generated by the finite element model building unit; specifically, by dynamically applying loading conditions, combining stress distribution characteristics and crack growth rate, the fatigue damage accumulation rate of the specified part of the equipment is calculated, and the fatigue life curve of the equipment is generated by the cyclic superposition method;

[0035] Damage node prediction unit: Identifies nodes that will be damaged during equipment operation based on fatigue life curves, and outputs damage node prediction results, including time, location, and stress level, by analyzing the time point and location when fatigue damage reaches the critical value.

[0036] Optionally, the fatigue life calculation unit specifically includes:

[0037] Dynamic loading condition application: According to the loading condition generation unit of the dynamic test evaluation module, the stress amplitude, loading frequency and environmental factor changes are dynamically applied to simulate the dynamic load of the equipment in actual operation;

[0038] Calculation of stress distribution characteristics: Combined with the finite element model of the equipment, calculate the stress distribution characteristics of the equipment under different loading conditions;

[0039] Crack growth rate calculation: In each loading cycle, the crack growth rate is calculated through the relationship between stress intensity factor and crack growth rate;

[0040] Fatigue damage accumulation rate calculation: Calculate the fatigue damage accumulation rate of the specified part of the equipment based on the crack growth rate and stress distribution characteristics;

[0041] Fatigue life calculation and fatigue life curve generation: Through the cyclic superposition method, the fatigue damage accumulation rate of each loading cycle is accumulated, the total damage of the equipment is calculated, and the fatigue life of the equipment is predicted based on the total damage; then, the fatigue life prediction curve is drawn through the relationship between the accumulated fatigue damage of the equipment and the remaining life.

[0042] Optionally, the damaged node prediction unit specifically includes:

[0043] Fatigue damage accumulation analysis: Based on the above fatigue damage accumulation calculation method, the cumulative damage is calculated by combining dynamic loading conditions, stress distribution characteristics, crack growth rate and damage accumulation rate. The formula is: Among them, D total is the total fatigue damage of the equipment, is the crack growth rate in the i-th loading cycle, ΔN i is the number of loading times in the i-th cycle;

[0044] Determination of critical damage value: According to the fatigue characteristics of the equipment, set the critical threshold D of fatigue damage cr , when the damage amount D of a certain part of the equipment total When this critical value is reached, it means that the part has reached the critical damage point and entered the early warning state of failure or damage;

[0045] Damage node time prediction: By real-time tracking of the fatigue damage accumulation process, it is predicted which parts of the equipment will reach the critical damage value first at a certain point in the future. At this time, the time point when the damage node reaches the critical damage value can be calculated by comparing the damage accumulation rate with the accumulated damage amount.

[0046] Prediction of the location of damaged nodes: By analyzing the stress distribution and crack propagation of the equipment, determine the location that will first reach critical damage; and predict the location of damaged nodes based on the stress distribution characteristics and damage accumulation rate;

[0047] Prediction of stress level of damaged nodes: Determine the stress level of damaged nodes by combining the damaged node location and loading conditions.

[0048] Optionally, the state assessment and prediction module includes a model building unit and a prediction unit; wherein:

[0049] Model building unit: used to build an equipment health assessment model based on the equipment fatigue life curve and damage node prediction value combined with the improved Bayesian update algorithm;

[0050] The construction of the equipment health assessment model specifically includes:

[0051] Initial setting of the model: The equipment fatigue life curve and damage node prediction value are taken as the prior distribution π(θ), where θ represents the unknown parameters of the equipment health status, including the fatigue damage degree or the remaining life. The initial distribution is assumed to be a normal distribution, expressed as: Among them, μ0 and are the mean and variance of the initial distribution respectively;

[0052] Real-time data update: Long-term operation data is used as observation data y, and the model is updated using the Bayesian theorem to calculate the posterior distribution. The formula is: π(θ|y)∝π(θ)·L(y|θ), where θ represents the equipment health status parameter, π(θ) is the prior distribution, L(y|θ) is the likelihood function of the observation data, and π(θ|y) is the posterior distribution.

[0053] Prediction unit: used to predict the remaining life of the equipment, the time of potential failure and the fatigue degree of the specified parts based on the posterior distribution of the health assessment model and combined with long-term operation data.

[0054] Optionally, the prediction unit specifically includes:

[0055] Remaining life prediction: Calculate the remaining life through the posterior distribution parameters, the formula is: R s =∫0 ∞ t·π(t|y i )dt, where R s represents the remaining life, t represents the time variable, π(t|y i ) represents the posterior probability distribution corresponding to time t;

[0056] Prediction of potential failure occurrence time: Combined with the predicted value of the damaged node, calculate the time when the equipment damage reaches the critical value. The formula is: Among them, T f Indicates the potential fault occurrence time, T0 indicates the current time, D c represents the damage critical value, D(T0) represents the current damage accumulation value, Indicates the current damage accumulation rate;

[0057] Prediction of fatigue degree of specified parts: By combining the real-time stress distribution and the posterior distribution parameters, the fatigue degree of the specified parts is calculated. The formula is: Among them, F x Indicates the fatigue degree of part x, D x Indicates the current cumulative damage value of part x, D c Indicates the damage threshold.

[0058] Beneficial effects of the present invention:

[0059] The present invention, through the synergistic effect of multiple sensors, collects the original data of the equipment, such as stress signals, vibration signals, temperature changes, etc., and combines advanced data processing and feature extraction technology. The system can accurately extract key characteristic parameters reflecting the fatigue damage of the equipment, such as fatigue crack growth rate, stress concentration factor and long-period damage cumulative energy. These characteristics provide an important basis for the health status assessment and life prediction of the equipment, and effectively make up for the deficiency that static analysis in the prior art cannot fully reflect the long-term operation status of the equipment.

[0060] The present invention simulates the fatigue damage evolution process of equipment under complex working conditions through a dynamic test evaluation module, and combines finite element dynamic simulation technology to generate the fatigue life curve of the equipment and the prediction value of key damage nodes. Combined with the improved Bayesian update algorithm, the system can update the equipment health assessment model in real time, and predict the remaining life of the equipment, the time of potential failure and the fatigue degree of the specified part through long-term operation data, thereby realizing accurate prediction and early warning of the equipment operation status, which not only improves the reliability of equipment operation, but also provides a scientific basis for equipment maintenance and management. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0062] Figure 1 A schematic diagram of an electrical equipment state monitoring and dynamic test evaluation system according to an embodiment of the present invention;

[0063] Figure 2 Schematic diagram of a dynamic test evaluation module according to an embodiment of the present invention. DETAILED DESCRIPTION

[0064] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0065] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0066] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0067] like Figure 1-Figure 2 As shown, the electrical equipment condition monitoring and dynamic test evaluation system includes a data acquisition module, a data processing module, a feature extraction module, a dynamic test evaluation module and a condition evaluation and prediction module; wherein:

[0068] Data acquisition module: used to collect raw data of electrical equipment in real time during operation, including stress change signals, vibration signals, temperature changes and long-term operation data;

[0069] Data processing module: used to filter noise, remove outliers and normalize the raw data collected by the data acquisition module to generate a standardized data set;

[0070] Feature extraction module: Based on the standardized data set output by the data processing module, relevant characteristic parameters reflecting fatigue damage accumulation are extracted from it, including fatigue crack growth rate, stress concentration factor and long-period damage accumulation energy;

[0071] Dynamic test evaluation module: Based on the characteristic parameters of the feature extraction module, by setting the dynamic test conditions of loading stress amplitude, frequency and environmental factors, combined with finite element dynamic simulation technology, it simulates the fatigue damage evolution process of electrical equipment under complex working conditions and generates equipment fatigue life curve and damage node prediction value;

[0072] Condition assessment and prediction module: The equipment fatigue life curve and damage node prediction value generated by the dynamic test assessment module are used in combination with the Bayesian update algorithm to build an equipment health assessment model. The remaining life of the equipment, the time of potential failure and the fatigue degree of the specified parts are predicted in real time through long-term operation data.

[0073] The data acquisition module includes a stress sensing unit, a vibration sensing unit, a temperature sensing unit and an operation data recording unit; wherein:

[0074] Stress sensing unit: used to collect stress change signals of electrical equipment in real time during operation through distributed optical fiber stress sensing technology, and transmit the collected signals to the data processing module in digital form;

[0075] Vibration sensing unit: Based on low-frequency vibration monitoring technology, it is used to capture the mechanical vibration signals generated during the operation of electrical equipment and record the vibration amplitude and frequency in real time;

[0076] Temperature sensing unit: uses precision thermocouple sensors to collect temperature change data inside and outside the equipment and record real-time temperature change data;

[0077] Operation data recording unit: used to record the operation time and cycle information of the equipment and obtain long-term operation data; through the coordinated work of the above units, the real-time and integrity of stress, vibration, temperature and operation cycle data are ensured, which can fully reflect the physical and environmental status of electrical equipment during operation, and provide reliable data support for accurately evaluating the health status and fatigue damage of equipment.

[0078] The data processing module includes a noise filtering unit, an outlier elimination unit, a normalization processing unit, and a standardized data generation unit; wherein:

[0079] The noise filtering unit is used to suppress the noise of the raw data transmitted by the data acquisition module, using a bandpass filter to remove interference signals outside the predetermined frequency range, and combining a filtering algorithm to eliminate environmental noise and electromagnetic interference noise to retain effective signal components;

[0080] The outlier elimination unit is used to identify and eliminate outliers in the original data, screen the data distribution using statistical analysis methods, exclude values ​​that exceed the normal range by setting upper and lower thresholds, and use interpolation algorithms to complete the missing data after elimination;

[0081] The normalization processing unit is used to normalize the data after removing outliers. By setting a fixed minimum and maximum range, the data is mapped to a standard interval between 0 and 1 to ensure the comparability and consistency of feature data of different dimensions.

[0082] The standardized data generation unit is used to integrate various types of data after normalization, synchronize the signal data according to a unified timestamp and generate a standardized data set. The standardized data set includes the normalized results of stress signals, vibration signals, temperature changes and long-period operation data for use by the feature extraction module. Through the synergistic effect of the above units, the data processing module realizes noise filtering, outlier removal and normalization of the original data, ensuring that the generated standardized data set has high accuracy, consistency and integrity, providing a high-quality data input basis for subsequent feature extraction and dynamic test evaluation.

[0083] The feature extraction module includes a crack extension analysis unit, a stress concentration analysis unit, a damage accumulation energy calculation unit and a feature integration unit; among which:

[0084] Crack growth analysis unit: used to extract fatigue crack growth rate from standardized data sets. Based on the changes in the collected stress signals, the crack growth rate is calculated using a cyclic load analysis model. The specific quantitative calculation is based on the following formula: da / dN = C (ΔK) m , where da / dN is the crack growth rate per cycle, C and m are material related parameters, ΔK = K max -K min is the stress intensity factor range, K max and K min denote the maximum and minimum stress intensity factors, respectively;

[0085] The stress concentration analysis unit is used to extract the stress concentration factors in the equipment operation from the standardized data set, analyze the distribution differences of stress in the predetermined parts of the equipment by using the multi-point stress distribution fitting method, and calculate the characteristic values ​​of the stress concentration area in combination with finite element simulation; the specific calculation formula is: Among them, K t is the stress concentration factor, σ max is the maximum local stress in the material or structure, σ nominal is the corresponding average stress;

[0086] Damage cumulative energy calculation unit: used to extract the damage cumulative energy of the equipment in the long-term operation data, decompose the periodic characteristics of the vibration signal based on the rain flow counting method, calculate the cumulative value of the energy dissipation of the equipment during long-term operation, and output the long-term damage cumulative energy parameter;

[0087] Feature integration unit: It is used to integrate the fatigue crack growth rate, stress concentration factor and long-period damage accumulation energy extracted by the above-mentioned units to generate a comprehensive feature vector, which is used as the input of the dynamic test evaluation module. Through the synergistic effect of the crack growth analysis unit, the stress concentration analysis unit and the damage accumulation energy calculation unit, the characteristic parameters reflecting the fatigue damage accumulation are accurately extracted, and a comprehensive feature vector is generated through the feature integration unit, which provides a feature data input basis with high correlation and high reliability for dynamic test evaluation.

[0088] The damage cumulative energy calculation unit specifically includes:

[0089] Signal decomposition: First, the vibration signal is decomposed into multiple amplitude and frequency components, and the signal cycle is counted using the rain flow counting method; the rain flow counting method divides the vibration signal into periodic waveforms by identifying the rising and falling peaks in the signal, and obtains the amplitude and cycle number of each cycle; this method specifically calculates the periodic characteristics through the following formula: Δx i =x(t i )-x(t i-1 ), where Δx i is the amplitude change of the ith period, x(t i ) is the time point t i The vibration signal value at x(t i-1 ) is the vibration signal value of the previous cycle; through this method, the amplitude change of each cycle is calculated and a complete periodic characteristic table is constructed;

[0090] Energy dissipation calculation: Calculate the energy dissipation of each cycle based on the amplitude of each cycle after decomposition; the energy dissipation of the equipment is determined by the stress amplitude and the loading frequency, and is specifically calculated using the following formula: Among them, E i is the energy dissipation value of the i-th cycle, K is the stiffness coefficient of the equipment, Δx i is the vibration amplitude change of the ith cycle;

[0091] Long-term damage cumulative energy calculation: Based on the energy dissipation of each cycle, the total damage cumulative energy value of the device is calculated by accumulating the energy dissipation values ​​of all cycles; the process follows the following formula: E i , where E total is the total damage accumulated energy in long-term operation, N is the number of cycles, E i is the energy dissipation of the ith cycle;

[0092] Output long-period damage cumulative energy parameter: According to the total damage cumulative energy value E total, output the long-period damage accumulation energy parameters of the equipment for subsequent fatigue damage assessment; the damage accumulation energy calculation unit periodically decomposes the vibration signal through the rain flow counting method, and accurately calculates the energy dissipation of each cycle of the equipment, which can accurately evaluate the fatigue damage accumulation in long-term operation; by accumulating all periodic energies, the output long-period damage accumulation energy parameters provide accurate input for the dynamic test evaluation module, effectively supporting the health assessment and fatigue life prediction of the equipment.

[0093] The dynamic test evaluation module includes a dynamic loading condition generation unit, a finite element model construction unit, a fatigue life calculation unit, and a damage node prediction unit; among which:

[0094] Dynamic loading condition generation unit: used to set the loading conditions of the dynamic test according to the fatigue crack growth rate, stress concentration factor and long-period damage accumulation energy extracted by the feature extraction module; the specific loading conditions include determining the loading stress amplitude according to the stress concentration factor, setting the loading frequency according to the fatigue crack growth rate, and setting the temperature and humidity in combination with the long-period damage accumulation energy and the equipment operating environment;

[0095] The specific setting method of loading conditions is as follows:

[0096] Loading stress amplitude: Based on stress concentration factor K t and the average stress σ of the equipment nominal , stress amplitude σ of dynamic loading load Calculated according to the following formula: load =K t ·σ nominal , where σ nominal is the design average stress of the equipment, σ load represents the loading stress amplitude;

[0097] Loading frequency: Based on fatigue crack growth rate da / dN and crack growth threshold ΔK th , the frequency of dynamic loading f load Calculated using the following formula: Where ΔK = K max -K min is the stress intensity factor range, C and m are material characteristic parameters, f load Indicates loading frequency;

[0098] Environmental factors change conditions: combined with long-term damage cumulative energy E total and the actual working conditions of the equipment operating environment, the temperature T is determined by the following linear mapping formula env and humidity H env :

[0099] T env =Tbase +α·E total ;

[0100] H env =H base +β·E total ; Among them, T base and H base are the reference temperature and humidity of the equipment design environment, α and β are the environmental parameter adjustment coefficients, and E total Accumulate energy for long-term damage.

[0101] Finite element model building unit: used to build a finite element analysis model based on the physical structure and material properties of the equipment. The input of the finite element analysis model includes the characteristic parameters provided by the characteristic extraction module and the dynamic loading conditions output by the dynamic loading condition generation unit, so as to generate a simulation model that can simulate the evolution of fatigue damage of the equipment under complex working conditions;

[0102] The specific steps to build a simulation model are as follows:

[0103] Step 1: According to the physical size and geometric structure of the equipment, establish a three-dimensional finite element geometric model G (x, y, z) of the equipment to accurately reflect the actual shape and characteristics of the equipment; the geometric model is generated by a three-dimensional CAD tool or a parametric modeling tool, and the formula is expressed as follows: Among them, V i represents the volume of the i-th structural unit of the equipment, and n is the total number of structural units;

[0104] Step 2: The material properties of the equipment, including elastic modulus E, Poisson's ratio v, yield strength σ y , fracture toughness K IC , assigned to each structural unit in the geometric model;

[0105] Step 3: Divide the geometric model into finite element units and generate a mesh model for numerical calculation. The mesh unit type can be tetrahedral or hexahedral. The number of mesh nodes N and the number of units M satisfy the following expressions: Among them, V is the geometric volume, and Mesh Density is the mesh density, which can be adjusted according to the complexity of the equipment to ensure the accuracy and efficiency of the model calculation;

[0106] Step 4: Based on the output of the dynamic loading condition generation unit, apply the following boundary conditions and dynamic loading;

[0107] Fixed boundary conditions, imposing zero displacement conditions u(x, y, z) = 0 on the constraint points of the equipment;

[0108] Dynamic loading conditions, applied stress amplitude σ load, Loading frequency f loadand ambient temperature change T env ; Dynamic loading is a periodic stress change, and its expression is: σ(t) = σ lood ·sin(2πf load t); the temperature load changes linearly, and its expression is: T(t) = T base +α·t;

[0109] Step 5: Based on the mesh model and loading conditions, the dynamic damage of the equipment is calculated using the cumulative method of fatigue damage evolution. The following expression is used to describe the cumulative damage: Where D is the fatigue damage factor, Δσ i is the stress amplitude of the ith cycle, σ y is the yield strength of the material, N1 is the total amount of material;

[0110] Step 6: Combining all the steps, the numerical solution of the dynamic simulation model is generated by the finite element solver. The expression of the simulation model is: Among them, M is the mass matrix, C is the damping matrix, K is the stiffness matrix; u is the displacement vector, and are velocity and acceleration vectors respectively; F(t) is the external load vector, including periodic stress and temperature field.

[0111] Fatigue life calculation unit: used to simulate the fatigue damage evolution process of equipment under complex working conditions based on the simulation model generated by the finite element model building unit; specifically, by dynamically applying loading conditions, combining stress distribution characteristics and crack growth rate, the fatigue damage accumulation rate of the specified part of the equipment is calculated, and the fatigue life curve of the equipment is generated by the cyclic superposition method;

[0112] Damage node prediction unit: Based on the fatigue life curve, nodes that will be damaged during the operation of the equipment are identified. By analyzing the time point and location when the fatigue damage reaches the critical value, the damage node prediction value results, including time, location and stress level, are output; through the synergistic effect of the dynamic loading condition generation unit, the finite element model construction unit, the fatigue life calculation unit and the damage node prediction unit, the dynamic test evaluation module can accurately simulate the fatigue damage evolution process of electrical equipment under complex working conditions, provide the fatigue life curve of the equipment and the damage node prediction value, and provide high-precision data support for subsequent status evaluation and prediction, thereby improving the reliability and scientificity of equipment operation status monitoring and life prediction.

[0113] The fatigue life calculation unit specifically includes:

[0114] Dynamic loading condition application: According to the loading condition generation unit of the dynamic test evaluation module, the stress amplitude, loading frequency and environmental factor changes are dynamically applied to simulate the dynamic load of the equipment in actual operation; the relationship between stress amplitude and loading frequency can be described by the following formula: σ(t) = σ peak ·sin(2πfl load t), where σ peak is the maximum stress amplitude under load, f load is the loading frequency, t is the time;

[0115] Calculation of stress distribution characteristics: Combined with the finite element model of the equipment, calculate the stress distribution characteristics of the equipment under different loading conditions; stress distribution is obtained through the stress-strain relationship, and the stress distribution formula of linear elastic materials is: Where σ(x) is the stress at position x, M(x) is the bending moment, I is the moment of inertia of the section, and y is the perpendicular distance from the neutral axis;

[0116] Crack growth rate calculation: In each loading cycle, the crack growth rate is calculated by the relationship between the stress intensity factor and the crack growth rate; the formula for the stress intensity factor is: Where Y is the geometric correction factor, σ is the applied stress, and a is the crack length; the crack growth rate is calculated using the Paris formula: Where ΔK is the change in stress intensity factor, C and m are material constants;

[0117] Fatigue damage accumulation rate calculation: Calculate the fatigue damage accumulation rate of the specified part of the equipment based on the crack growth rate and stress distribution characteristics; assume that the damage accumulation rate is proportional to the crack growth rate, and calculate it using the following formula: in, is the damage accumulation rate, is the crack growth rate, a thresh is the threshold of crack extension;

[0118] Fatigue life calculation and fatigue life curve generation: Through the cyclic superposition method, the fatigue damage accumulation rate of each loading cycle is accumulated, the total damage of the equipment is calculated, and the fatigue life of the equipment is predicted based on the total damage; the fatigue life calculation formula is: Among them, N f is fatigue life, D maxis the maximum damage of the equipment during the fatigue process; and then, through the relationship between the accumulated fatigue damage of the equipment and the remaining life, a fatigue life prediction curve is drawn to express the relationship between the fatigue life and damage of the equipment; the above steps combine dynamic loading conditions, stress distribution characteristics and crack growth rate, and the fatigue life calculation unit can accurately evaluate the fatigue damage accumulation rate of the equipment under complex working conditions, and generate the fatigue life curve of the equipment through the cyclic superposition method; this method enables the fatigue damage of the equipment in long-term operation to be effectively predicted, thereby providing a scientific basis for equipment health management and maintenance decisions.

[0119] The damaged node prediction unit specifically includes:

[0120] Fatigue damage accumulation analysis: Based on the aforementioned fatigue damage accumulation calculation method, the fatigue damage accumulation process of the equipment at different parts is analyzed by combining dynamic loading conditions, stress distribution characteristics, crack growth rate and damage accumulation rate; the cumulative damage amount is calculated using the formula: Among them, D total is the total fatigue damage of the equipment, is the crack growth rate in the i-th loading cycle, ΔN i is the number of loading times in the i-th cycle;

[0121] Determination of critical damage value: According to the fatigue characteristics of the equipment, set the critical threshold D of fatigue damage cr (usually 1, indicating that the crack is fully extended), when the damage amount D of a certain part of the equipment total When this critical value is reached, it means that the part has reached the critical damage point and entered the early warning state of failure or damage;

[0122] Damage node time prediction: By real-time tracking of fatigue damage accumulation process, it is predicted which parts of the equipment will reach the critical damage value first at a certain point in the future; at this time, the time point t when the damage node reaches the critical damage value can be calculated by comparing the damage accumulation rate with the accumulated damage amount. critical , the formula is: Among them, t0 is the current time, D total (t0) is the total damage at the current moment, is the damage accumulation rate at the current moment, t critical It is the predicted time point when the damage reaches the critical value;

[0123] Prediction of the location of the damage node: By analyzing the stress distribution and crack propagation of the equipment, determine the location that will first reach critical damage; and predict the location of the damage node by combining the stress distribution characteristics and damage accumulation rate critical , the formula is: Where σ(x) is the stress value at position x, is the crack growth rate at position x, critical Indicates the location of the damaged node;

[0124] Prediction of stress level of damaged node: The stress level of the damaged node is determined by combining the damaged node location and loading conditions; the stress level is calculated using the following formula:

[0125] σ critical =v peak ·sin(2πf load t critical ), where σ critical is the stress level at the damaged node, σ peak is the maximum stress amplitude under load, f load is the loading frequency, t critical The time when the damaged node reaches the critical damage point; the above steps predict the time, location and stress level of the damaged node by analyzing the fatigue damage accumulation process of the equipment, and can provide accurate early warning information for equipment maintenance and inspection; this method effectively identifies the parts of the equipment that may fail and takes precautions in advance, which helps to extend the service life of the equipment, reduce maintenance costs, and improve the reliability and safety of the equipment.

[0126] The state assessment and prediction module includes a model building unit and a prediction unit; wherein:

[0127] Model building unit: used to build an equipment health assessment model based on the equipment fatigue life curve and damage node prediction value combined with the improved Bayesian update algorithm;

[0128] Building a device health assessment model specifically includes:

[0129] Initial setting of the model: The equipment fatigue life curve and damage node prediction value are taken as the prior distribution π(θ), where θ represents the unknown parameters of the equipment health status, including the fatigue damage degree or the remaining life. The initial distribution is assumed to be a normal distribution, expressed as: Among them, μ0 and are the mean and variance of the initial distribution respectively;

[0130] Real-time data update: Long-term operation data is used as observation data y, and the model is updated using the Bayesian theorem to calculate the posterior distribution. The formula is: π(θ|y)∝π(θ)·L(y|θ), where θ represents the equipment health status parameter, π(θ) is the prior distribution, L(y|θ) is the likelihood function of the observation data, and π(θ|y) is the posterior distribution, which represents the health status probability distribution of the equipment under real-time operation data.

[0131] Prediction unit: used to predict the remaining life of the equipment, the time of potential failure and the fatigue degree of the specified parts based on the posterior distribution of the health assessment model and combined with long-term operation data.

[0132] The prediction unit specifically includes:

[0133] Remaining life prediction: Calculate the remaining life through the posterior distribution parameters, the formula is: R s =∫0 ∞ t·π(t|y i )dt, where R s represents the remaining life, t represents the time variable, π(t|y i ) represents the posterior probability distribution corresponding to time t;

[0134] Prediction of potential failure occurrence time: Combined with the predicted value of the damaged node, calculate the time when the equipment damage reaches the critical value. The formula is: Among them, T f Indicates the potential fault occurrence time, T0 indicates the current time, D c represents the damage critical value, D(T0) represents the current damage accumulation value, Indicates the current damage accumulation rate;

[0135] Prediction of fatigue degree of specified parts: By combining the real-time stress distribution and the posterior distribution parameters, the fatigue degree of the specified parts is calculated. The formula is: Among them, F x Indicates the fatigue degree of part x, D x Indicates the current cumulative damage value of part x, D c Represents the damage critical value; through the combination of the above units, the status assessment and prediction module can realize real-time assessment and prediction of the equipment operation status. This method provides scientific support for equipment maintenance and life management, and improves the accuracy of prediction and the reliability of equipment operation.

[0136] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0137] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. Electrical equipment condition monitoring and dynamic test evaluation system, characterized in that: It includes data acquisition module, data processing module, feature extraction module, dynamic test evaluation module and state evaluation and prediction module; among which: Data acquisition module: used to collect raw data of electrical equipment in real time during operation, including stress change signals, vibration signals, temperature changes and long-term operation data; Data processing module: used to filter noise, remove outliers and normalize the raw data collected by the data acquisition module to generate a standardized data set; Feature extraction module: Based on the standardized data set output by the data processing module, relevant characteristic parameters reflecting fatigue damage accumulation are extracted from it, including fatigue crack growth rate, stress concentration factor and long-period damage accumulation energy; Dynamic test evaluation module: Based on the characteristic parameters of the feature extraction module, by setting the dynamic test conditions of loading stress amplitude, frequency and environmental factors, combined with finite element dynamic simulation technology, the fatigue damage evolution process of electrical equipment under complex working conditions is simulated, and the equipment fatigue life curve and damage node prediction value are generated; Condition assessment and prediction module: The equipment fatigue life curve and damage node prediction value generated by the dynamic test assessment module are used in combination with the Bayesian update algorithm to build an equipment health assessment model. The remaining life of the equipment, the time of potential failure and the fatigue degree of the specified parts are predicted in real time through long-term operation data.

2. The electrical equipment condition monitoring and dynamic test evaluation system according to claim 1 is characterized in that: The data acquisition module includes a stress sensing unit, a vibration sensing unit, a temperature sensing unit and an operation data recording unit; wherein: Stress sensing unit: used to collect stress change signals of electrical equipment in real time during operation through distributed optical fiber stress sensing technology; Vibration sensing unit: Based on low-frequency vibration monitoring technology, it is used to capture the mechanical vibration signals generated during the operation of electrical equipment and record the vibration amplitude and frequency in real time; Temperature sensing unit: uses precision thermocouple sensors to collect temperature change data inside and outside the equipment and record real-time temperature change data; Operation data recording unit: used to record the operation time and cycle information of the equipment and obtain long-term operation data.

3. The electrical equipment condition monitoring and dynamic test evaluation system according to claim 1 is characterized in that: The data processing module includes a noise filtering unit, an outlier elimination unit, a normalization processing unit and a standardized data generation unit; wherein: The noise filtering unit is used to suppress the noise of the raw data transmitted by the data acquisition module, using a bandpass filter to remove interference signals outside the predetermined frequency range, and combining a filtering algorithm to eliminate environmental noise and electromagnetic interference noise; The outlier elimination unit is used to identify and eliminate outliers in the original data, screen the data distribution using statistical analysis methods, exclude values ​​that exceed the normal range by setting upper and lower thresholds, and use interpolation algorithms to complete the missing data after elimination; A normalization processing unit is used to normalize the data after removing outliers, and map the data to a standard interval between 0 and 1 by setting a fixed minimum and maximum range; The standardized data generation unit is used to integrate various types of data after normalization, synchronize the signal data according to a unified timestamp and generate a standardized data set for use by the feature extraction module.

4. The electrical equipment condition monitoring and dynamic test evaluation system according to claim 1, characterized in that: The feature extraction module includes a crack extension analysis unit, a stress concentration analysis unit, a damage accumulation energy calculation unit and a feature integration unit; wherein: Crack growth analysis unit: used to extract fatigue crack growth rate from standardized data sets, and calculate crack growth rate based on the changes in collected stress signals using a cyclic load analysis model; The stress concentration analysis unit is used to extract the stress concentration factors in the equipment operation from the standardized data set, analyze the distribution differences of stress in the predetermined parts of the equipment by using the multi-point stress distribution fitting method, and calculate the characteristic values ​​of the stress concentration area by combining finite element simulation; Damage cumulative energy calculation unit: used to extract the damage cumulative energy of the equipment in the long-term operation data, decompose the periodic characteristics of the vibration signal based on the rain flow counting method, calculate the cumulative value of the energy dissipation of the equipment during long-term operation, and output the long-term damage cumulative energy parameter; Feature integration unit: It is used to integrate the fatigue crack growth rate, stress concentration factor and long-period damage accumulation energy extracted by the above units to generate a comprehensive feature vector.

5. The electrical equipment condition monitoring and dynamic test evaluation system according to claim 4 is characterized in that: The damage accumulation energy calculation unit specifically includes: Signal decomposition: The vibration signal is first decomposed into multiple amplitude and frequency components, and the signal is cycle counted using the rain flow counting method; the rain flow counting method divides the vibration signal into periodic waveforms by identifying the rising and falling peaks in the signal to obtain the amplitude and cycle number of each cycle; Energy dissipation calculation: Calculate the energy dissipation of each cycle based on the amplitude of each cycle after decomposition; Long-term damage cumulative energy calculation: Based on the energy dissipation of each cycle, the total damage cumulative energy value of the equipment is calculated by accumulating the energy dissipation values ​​of all cycles; Output long-period damage cumulative energy parameters: Output the long-period damage cumulative energy parameters of the device based on the total damage cumulative energy value.

6. The electrical equipment condition monitoring and dynamic test evaluation system according to claim 1, characterized in that: The dynamic test evaluation module includes a dynamic loading condition generation unit, a finite element model construction unit, a fatigue life calculation unit and a damage node prediction unit; wherein: Dynamic loading condition generation unit: used to set the loading conditions of the dynamic test according to the fatigue crack growth rate, stress concentration factor and long-period damage accumulation energy extracted by the feature extraction module; the specific loading conditions include determining the loading stress amplitude according to the stress concentration factor, setting the loading frequency according to the fatigue crack growth rate, and setting the temperature and humidity in combination with the long-period damage accumulation energy and the equipment operating environment; Finite element model building unit: used to build a finite element analysis model based on the physical structure and material properties of the equipment. The input of the finite element analysis model includes the characteristic parameters provided by the characteristic extraction module and the dynamic loading conditions output by the dynamic loading condition generation unit, so as to generate a simulation model that can simulate the evolution of fatigue damage of the equipment under complex working conditions; Fatigue life calculation unit: used to simulate the fatigue damage evolution process of equipment under complex working conditions based on the simulation model generated by the finite element model building unit; specifically, by dynamically applying loading conditions, combining stress distribution characteristics and crack growth rate, the fatigue damage accumulation rate of the specified part of the equipment is calculated, and the fatigue life curve of the equipment is generated by the cyclic superposition method; Damage node prediction unit: Identifies nodes that will be damaged during equipment operation based on fatigue life curves, and outputs damage node prediction results, including time, location, and stress level, by analyzing the time point and location when fatigue damage reaches the critical value.

7. The electrical equipment condition monitoring and dynamic test evaluation system according to claim 6, characterized in that: The fatigue life calculation unit specifically includes: Dynamic loading condition application: According to the loading condition generation unit of the dynamic test evaluation module, the stress amplitude, loading frequency and environmental factor changes are dynamically applied to simulate the dynamic load of the equipment in actual operation; Calculation of stress distribution characteristics: Combined with the finite element model of the equipment, calculate the stress distribution characteristics of the equipment under different loading conditions; Crack growth rate calculation: In each loading cycle, the crack growth rate is calculated through the relationship between stress intensity factor and crack growth rate; Fatigue damage accumulation rate calculation: Calculate the fatigue damage accumulation rate of the specified part of the equipment based on the crack growth rate and stress distribution characteristics; Fatigue life calculation and fatigue life curve generation: Through the cyclic superposition method, the fatigue damage accumulation rate of each loading cycle is accumulated, the total damage of the equipment is calculated, and the fatigue life of the equipment is predicted based on the total damage; then, the fatigue life prediction curve is drawn through the relationship between the accumulated fatigue damage of the equipment and the remaining life.

8. The electrical equipment condition monitoring and dynamic test evaluation system according to claim 7, characterized in that: The damaged node prediction unit specifically includes: Fatigue damage accumulation analysis: Based on the above fatigue damage accumulation calculation method, the cumulative damage is calculated by combining dynamic loading conditions, stress distribution characteristics, crack growth rate and damage accumulation rate. The formula is: Among them, D total is the total fatigue damage of the equipment, is the crack growth rate in the i-th loading cycle, ΔN i is the number of loading times in the i-th cycle; Determination of critical damage value: According to the fatigue characteristics of the equipment, set the critical threshold D of fatigue damage cr , when the damage amount D of a certain part of the equipment total When this critical value is reached, it means that the part has reached the critical damage point and entered the early warning state of failure or damage; Damage node time prediction: By real-time tracking of the fatigue damage accumulation process, it is predicted which parts of the equipment will reach the critical damage value first at a certain point in the future. At this time, the time point when the damage node reaches the critical damage value can be calculated by comparing the damage accumulation rate with the accumulated damage amount. Prediction of the location of damaged nodes: By analyzing the stress distribution and crack propagation of the equipment, determine the location that will first reach critical damage; and predict the location of damaged nodes based on the stress distribution characteristics and damage accumulation rate; Prediction of stress level of damaged nodes: Determine the stress level of damaged nodes by combining the damaged node location and loading conditions.

9. The electrical equipment condition monitoring and dynamic test evaluation system according to claim 1, characterized in that: The state assessment and prediction module includes a model building unit and a prediction unit; wherein: Model building unit: used to build an equipment health assessment model based on the equipment fatigue life curve and damage node prediction value combined with the improved Bayesian update algorithm; The construction of the equipment health assessment model specifically includes: Initial setting of the model: The equipment fatigue life curve and damage node prediction value are taken as the prior distribution π(θ), where θ represents the unknown parameters of the equipment health status, including the fatigue damage degree or the remaining life. The initial distribution is assumed to be a normal distribution, expressed as: Among them, μ0 and are the mean and variance of the initial distribution respectively; Real-time data update: Long-term operation data is used as observation data y, and the model is updated using the Bayesian theorem to calculate the posterior distribution. The formula is: π(θ|y)∝π(θ)·L(y|θ), where θ represents the equipment health status parameter, π(θ) is the prior distribution, L(y|θ) is the likelihood function of the observation data, and π(θ|y) is the posterior distribution. Prediction unit: used to predict the remaining life of the equipment, the time of potential failure and the fatigue degree of the specified parts based on the posterior distribution of the health assessment model and combined with long-term operation data.

10. The electrical equipment condition monitoring and dynamic test evaluation system according to claim 9, characterized in that: The prediction unit specifically includes: Remaining life prediction: Calculate the remaining life through the posterior distribution parameters, the formula is: R s =∫0 ∞ t·π(t|y i )dt, where R s represents the remaining life, t represents the time variable, π(t|y i ) represents the posterior probability distribution corresponding to time t; Prediction of potential failure occurrence time: Combined with the predicted value of the damaged node, calculate the time when the equipment damage reaches the critical value. The formula is: Among them, T f Indicates the potential fault occurrence time, T0 indicates the current time, D c represents the damage critical value, D(T0) represents the current damage accumulation value, Indicates the current damage accumulation rate; Prediction of fatigue degree of specified parts: By combining the real-time stress distribution and the posterior distribution parameters, the fatigue degree of the specified parts is calculated. The formula is: Among them, F x Indicates the fatigue degree of part x, D x Indicates the current cumulative damage value of part x, D c Indicates the damage threshold.

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