Electromechanical system performance degradation uncertainty evaluation method based on dynamic comprehensive index

Abstract

The invention relates to the technical field of intelligent operation and maintenance of mechanical equipment, in particular to an electromechanical system performance degradation uncertainty evaluation method based on a dynamic comprehensive index, which comprises the following steps: acquiring a real-time vibration signal of an electromechanical system, performing feature extraction, and constructing a real-time similarity feature index; constructing a model selection library containing a plurality of performance evaluation fitting models; fitting each model based on a real-time similarity characteristic index; scoring the fitting effect of each model based on a PE criterion, and screening out an optimal model; predicting the characteristic state of the electromechanical system based on the optimal model screened at the current moment, introducing process noise and performance monitoring noise, and calculating uncertain evaluation results of the electromechanical system at different moments; and performing performance prediction on the electromechanical system based on the uncertainty evaluation result. According to the method, uncertainty evaluation can be performed on the performance of the electromechanical system based on the dynamic iteration model of the actual operation state, and the precision and reliability of performance evaluation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of mechanical equipment, and more particularly to a method for evaluating uncertainty in performance degradation of an electromechanical system based on dynamic comprehensive indicators. Background Art

[0002] In recent years, electromechanical systems have been widely used in modern mechanical equipment due to their strong load-bearing capacity and high efficiency. The performance of electromechanical systems directly impacts the reliability and efficiency of the entire equipment. However, electromechanical systems often operate in harsh environments such as high temperatures and high loads. These harsh environments can easily cause component failures within the electromechanical system, leading to failure of the entire system. System failures can delay work progress at best, or even result in personal injury or death. Repairing an electromechanical system after failure significantly increases operational costs, driving increasing interest in performance evaluation. Performance evaluation of electromechanical systems not only reduces the incidence of accidents but also significantly lowers maintenance costs. Therefore, performance evaluation of electromechanical systems is of great significance.

[0003] Among existing mechatronic system performance evaluation methods, model-based approaches have received increasing attention. However, due to the complex and volatile operating environments of mechatronic systems, using only a single model makes it difficult to accurately evaluate the performance of mechatronic systems. To address this issue, a growing number of studies have focused on adaptive evaluation methods, which are mainly divided into multi-stage evaluation methods and dynamic evaluation methods. Multi-stage evaluation methods divide the mechatronic system performance degradation process into three stages: operation, slow degradation, and accelerated degradation. However, in actual operation, not all mechatronic system degradation processes can be divided into these three stages. For example, sudden and self-healing degradation processes, for example, can significantly reduce the evaluation effect when using multi-stage methods to evaluate these two types of degradation processes. Furthermore, the evaluation models used in existing dynamic evaluation methods are relatively simple, and the evaluation model is only selected at the initial stage of operation. The selection criteria used only consider the impact of fitting errors, making it difficult to accurately fit the mechatronic system degradation process. Furthermore, it is difficult to assess the uncertainty of the degradation process during the evaluation process. These shortcomings can lead to poor performance evaluation results, which in turn affects subsequent maintenance decisions. Therefore, how to improve the fitting effect of the degradation process and assess the uncertainty of the degradation process has become a technical problem that technicians in this field urgently need to solve. Summary of the Invention

[0004] In view of this, the present invention provides a method for evaluating the uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators, which solves the problems of poor fitting effect of the degradation process and low evaluation accuracy in existing methods.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators includes the following steps:

[0007] Obtain real-time vibration signals of electromechanical systems and perform feature extraction;

[0008] Based on the feature distribution extracted in real time from the electromechanical system and the feature distribution extracted in normal state, a real-time similarity feature index is constructed;

[0009] Build a model selection library containing a variety of performance evaluation fitting models;

[0010] Based on the real-time similarity feature index, a known data set is constructed, and each model in the model selection library is fitted based on the known data set;

[0011] The fitting effect of each model is scored based on the PE criterion, and the model with the lowest score is selected as the optimal model at the current moment;

[0012] Predict the instantaneous characteristic state of the electromechanical system at different future moments based on the optimal model selected at the current moment;

[0013] Introducing process noise and performance monitoring noise into the prediction results, and computing uncertain evaluation results of the electrical system at different times;

[0014] Performance prediction of electromechanical systems based on uncertainty assessment results.

[0015] Furthermore, the real-time vibration signal of the electromechanical system is expressed as ,in, is the running time, for The vibration signal value monitored at all times, is the signal sampling point, ;

[0016] The formula for feature extraction of real-time vibration signals of electromechanical systems is:

[0017]

[0018] in, is the extracted features.

[0019] Furthermore, the calculation formula of the feature similarity index is:

[0020]

[0021] in, Features extracted under normal conditions The distribution of Features extracted from vibration signal data at several consecutive moments updated in real time Distribution, through this calculation formula the characteristics Normalization reflects the similarity between the real-time update features and the features under normal operating conditions. The larger the value, the smaller the similarity between the two, and vice versa.

[0022] Furthermore, the model selection library includes basic models and complex models. The basic model is represented as:

[0023]

[0024] The complex model is expressed as:

[0025]

[0026] in, is the characteristic index value obtained by model fitting, is the characteristic index calculated at the initial moment, are the parameters that need to be fitted, is the running time, represents a standard Brownian motion process.

[0027] Furthermore, the process of fitting each model in the model selection library includes:

[0028] Based on real-time monitoring and calculation of similarity feature indicators , build a known data set ,in is the running time, for The feature values ​​extracted at runtime, is the signal sampling point, ;

[0029] Based on known datasets , and fit simple and complex models respectively.

[0030] Furthermore, the calculation formula for scoring the fitting effect of each model based on the PE criterion is:

[0031]

[0032] in, The number of unknown parameters in the fitted model for performance evaluation; is the number of known data involved in model fitting; For in time The true value obtained at To use the model at time The fitted value obtained at Represents the average trend value of the characteristic indicator, The weight for considering the effect of model degradation trend in the dynamic iteration process; Used to balance the situation where different models contain different numbers of unknown parameters;

[0033] Whenever the vibration signal data of the electromechanical system is updated, the performance evaluation fitting models are calculated. Value, select The model with the lowest score evaluates the performance of the electromechanical system at that moment.

[0034] Furthermore, process noise and performance monitoring noise are introduced into the prediction results, and the uncertain evaluation results of the computer electrical system at different times include:

[0035] Introduce process noise into the prediction results at a certain moment and construct the characteristic change equation;

[0036] Introduce performance monitoring noise and build performance monitoring functions;

[0037] The covariance matrix is ​​introduced, and the best characteristic approximation of the electromechanical system at the next moment is estimated by knowing the best characteristic approximation of the electromechanical system at the previous moment, so as to obtain the uncertainty evaluation results of the electromechanical system at different future moments.

[0038] Furthermore, the characteristic change equation is expressed as:

[0039]

[0040] in, Indicates The instantaneous characteristic state of the electromechanical system at time is The instantaneous characteristic state of the electromechanical system at time Indicates the slave state To status The transfer matrix, is the process noise, is the control matrix, For electromechanical systems Gain variable at time;

[0041] The expression of the performance monitoring function is:

[0042]

[0043] in, For The characteristic value of the electromechanical system at time For The moment feature change matrix, Monitor noise for features.

[0044] Furthermore, the calculation formula for the uncertainty assessment results of the electromechanical system at different moments in the future is:

[0045]

[0046] in, For The best characteristic approximation of the electromechanical system at time, For The prior error covariance matrix at time t, for The best characteristic approximation of the electromechanical system at time, Indicates the slave state To status The transfer matrix of For The characteristic change matrix at each moment, For The characteristic change matrix at each moment, For The transpose of the characteristic change matrix at time , Monitoring noise for features The covariance matrix of For Eigenvalues ​​of the electromechanical system at the moment;

[0047] For The covariance matrix of the prior error of the electromechanical system at time t is calculated as:

[0048]

[0049] in, is the transfer matrix The transposed matrix of for The covariance matrix of the electromechanical system at time t, Indicates The influencing variables of external factors during the operation of the electromechanical system at each moment.

[0050] Furthermore, the calculation formula for predicting the performance of the electromechanical system based on the uncertainty assessment results is:

[0051]

[0052] in, For The remaining running time predicted at each moment, is the eigenvalue The lower limit of is The eigenvalues ​​obtained after uncertainty evaluation are, is the set failure characteristic value of the electromechanical system, Expressed as from From the moment until the eigenvalue is evaluated Reach failure characteristic value The time interval.

[0053] It can be seen from the above technical solutions that compared with the prior art, the present invention has the following beneficial effects:

[0054] 1. The present invention can dynamically iterate the model as the electromechanical system continues to operate to adapt to different types of degradation processes, and combines the linear and nonlinear models commonly used in existing performance evaluation methods to fit the degradation process, thus adapting to various types of degradation processes.

[0055] 2. The present invention iteratively selects the model by using the comprehensive indicator PE criterion, while taking into account the fitting error and model degradation trend evaluation effect, better capturing the degradation process of the electromechanical system, reducing the impact of data mutation on performance evaluation, and improving the evaluation effect.

[0056] 3. The present invention takes into account uncertainty factors such as noise during operation and performs uncertainty assessment on the degradation process, thereby improving the accuracy and reliability of performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0058] Figure 1 A flowchart of the uncertainty assessment method for electromechanical system performance degradation based on dynamic comprehensive indicators provided by the present invention;

[0059] Figure 2 A framework diagram of the uncertainty assessment method for electromechanical system performance degradation based on dynamic comprehensive indicators provided by the present invention;

[0060] Figure 3 Schematic diagram showing the performance comparison between the method of the present invention and the existing prediction method. DETAILED DESCRIPTION

[0061] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0062] like Figure 1-Figure 2 As shown, an embodiment of the present invention discloses a method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators, comprising the following steps:

[0063] S1. Obtain the real-time vibration signal of the electromechanical system and perform feature extraction;

[0064] S2. Constructing a real-time similarity feature index based on the feature distribution extracted in real time from the electromechanical system and the feature distribution extracted in the normal state;

[0065] S3. Build a model selection library containing a variety of performance evaluation fitting models;

[0066] S4. Based on the real-time similarity feature index, a known data set is constructed, and each model in the model selection library is fitted based on the known data set;

[0067] S5. Score the fitting effect of each model based on the PE criterion, and use the model with the lowest score as the optimal model selected at the current moment;

[0068] S6. Predict the instantaneous characteristic states of the electromechanical system at different future moments based on the optimal model selected at the current moment; introduce process noise and performance monitoring noise into the prediction results, and calculate the uncertainty evaluation results of the electromechanical system at different moments; and predict the performance of the electromechanical system based on the uncertainty evaluation results.

[0069] The above steps are further explained below.

[0070] S1. Obtain the real-time vibration signal of the electromechanical system. The real-time vibration signal of the electromechanical system is expressed as ,in, is the running time, for The vibration signal value monitored at all times, is the signal sampling point,

[0071] The feature extraction of the real-time vibration signal of the electromechanical system is performed, and the extraction formula is:

[0072]

[0073] in, is the extracted features.

[0074] S2. Construction of similarity feature index.

[0075] After extracting the features of the vibration signal, the feature similarity index is further constructed, and the calculation formula is:

[0076]

[0077] in, Features extracted under normal conditions According to the monitored data, the electromechanical system is in the normal operation stage in the initial operation stage, and the fault is gradually evolved. The normal state here refers to the features extracted during the initial operation process. For example, the distribution of the first 10 data is taken as the distribution extracted under normal conditions. . Features extracted from vibration signal data at several consecutive moments updated in real time The distribution can be the distribution of the features extracted at the current moment and the features extracted several moments before. The specific number of features can be set by yourself. The features are calculated by this formula. Normalization reflects the similarity between the real-time update features and the features under normal operating conditions. Can measure the distribution of monitored data With normal data distribution The similarity, when hour, At this point the two are completely similar. The larger the value, the smaller the similarity between the two, and vice versa. By constructing a similarity feature index, we can measure whether the electromechanical system is in an abnormal state, that is, whether it has begun to degrade.

[0078] S3. Build a model selection library containing a variety of performance evaluation fitting models. The model selection library contains two categories, namely basic models and complex models. The basic model is represented as:

[0079]

[0080] The complex model is expressed as:

[0081]

[0082] in, is the characteristic index value obtained by model fitting, is the characteristic index calculated at the initial moment, are the parameters that need to be fitted, is the running time, represents a standard Brownian motion process.

[0083] S4. Fit each model in the model selection library. The specific fitting process includes:

[0084] Based on real-time monitoring and calculation of similarity feature indicators , build a known data set ,in is the running time, for The feature values ​​extracted at runtime, is the signal sampling point, ;

[0085] Based on known datasets , and fit simple and complex models respectively.

[0086] Taking the double exponential model as an example, the fitting curve calculation formula is: .

[0087] in, The calculated value using the fitted model The characteristic index value at the moment is used to establish the error function, and the calculation formula is:

[0088]

[0089] in, are the parameters obtained by fitting, To find Minimize the function of the parameter. By using the known data set The unknown parameters are estimated and finally a fitted model is obtained.

[0090] S5. As the electromechanical system is monitored in real time, dynamic model iteration is performed using known data sets. In view of the problem that existing methods usually only consider model fitting errors, a comprehensive indicator PE criterion is constructed for scoring. The PE criterion considers both fitting errors and model degradation trend effects. The PE criterion is used to score the evaluation model fitting effect. The calculation formula of the PE value is:

[0091]

[0092] in, The number of unknown parameters in the fitted model for performance evaluation; is the number of known data involved in model fitting; For in time The true value obtained at To use the model at time The fitted value obtained at Represents the average trend value of the characteristic indicator, In order to consider the weight of the model degradation trend effect in the dynamic iteration process, it is determined according to the degree to which the degradation trend effect needs to be considered in the actual situation. If you care more about the model degradation trend effect, It can be set to be greater than 0.5, if the fitting error and degradation trend are considered equally. . Used to balance the situation where different models contain different numbers of unknown parameters;

[0093] Whenever the vibration signal data of the electromechanical system is updated, the performance evaluation fitting models are calculated. Value, select The model with the lowest score is taken as the optimal model at the current moment, and the performance of the electromechanical system at that moment is evaluated to achieve the effect of dynamic model iterative update.

[0094] S6. After the optimal model is determined, the performance of the electromechanical system is evaluated based on the data obtained at this moment. Since the working environment of the electromechanical system is affected by various external environments, its performance needs to be evaluated with uncertainty degradation, including:

[0095] S61. Since process noise exists in the actual monitoring process, process noise is introduced to construct a characteristic change equation to correct the characteristic value predicted by the optimal model. The expression of the characteristic change equation is:

[0096]

[0097] in, Indicates The instantaneous characteristic state of the electromechanical system at time The feature value predicted at each moment, is The instantaneous characteristic state of the electromechanical system at time Indicates the slave state To status The transfer matrix, is the process noise, is the control matrix, For electromechanical systems Gain variable at time t. Control matrix It indicates the degree of influence of external factors on state transfer. The control matrix can be obtained by fitting the function If there is no external influence, is 0, the above function can be simplified to .

[0098] Introduce performance monitoring noise and construct a performance monitoring function. The expression of the performance monitoring function is:

[0099]

[0100] in, For The characteristic value of the electromechanical system at time For The moment feature change matrix, The monitoring noise is characterized by being solved based on the fluctuation of the forward known data. The monitoring noise is obtained by calculating the noise component of the measurement data, such as the covariance of the difference between the measured value and the true value, so as to fit the uncertainty of the actual operation of the system.

[0101] The uncertainty of mechatronic system performance evaluation is accommodated by introducing process noise and performance monitoring noise.

[0102] S62. Find the best characteristic approximation of the electromechanical system, introduce the covariance matrix, and estimate the best characteristic approximation of the electromechanical system at the next moment by knowing the best characteristic approximation of the electromechanical system at the previous moment, so as to obtain the uncertainty assessment results of the electromechanical system at different future moments. The calculation formula for the uncertainty assessment results of the electromechanical system at different future moments is:

[0103]

[0104] in, For The best characteristic approximation of the electromechanical system at the moment is Uncertainty assessment results of the electromechanical system at time For The prior error covariance matrix at time t, for The best characteristic approximation of the electromechanical system at time, Indicates the slave state To status The transfer matrix of For The characteristic change matrix at each moment, For The characteristic change matrix at each moment, For The transpose of the characteristic change matrix at time , Monitoring noise for features The covariance matrix of For The eigenvalues ​​of the electromechanical system at time t.

[0105] For The covariance matrix of the prior error of the electromechanical system at time t is calculated as:

[0106]

[0107] in, is the transfer matrix The transposed matrix of for The covariance matrix of the electromechanical system at time t, Indicates The influencing variables of external factors during the operation of the electromechanical system at each moment.

[0108] S63. Predict the performance of the electromechanical system based on the uncertainty assessment results. The specific calculation formula is:

[0109]

[0110] in, For The remaining running time predicted at each moment, is the eigenvalue The lower limit of is The eigenvalues ​​obtained after uncertainty evaluation are, is the set failure characteristic value of the electromechanical system, Expressed as from From the moment until the eigenvalue is evaluated Reach failure characteristic value The time interval.

[0111] Next, taking the performance evaluation of the gearbox in the electromechanical system of a rail train as an example, the operating data of the gearbox in the electromechanical system was collected through a test bench to verify the effectiveness of the method of the present invention.

[0112] The experimental platform is powered by a drive motor and features two gearboxes. One gearbox serves as a test unit for gearbox degradation, with a module of 1.5 and a width of 15 mm. The other gearbox serves as an auxiliary test unit, with gears of modules 2 and 3, respectively, and a width of 30 mm to enhance reliability. Both gearboxes are constructed of S45C, with a tooth surface hardness below 194HB.

[0113] This experiment collected data from 11 channels of five different signal types, including vibration, speed, torque, sound, and current, until failure. The sampling frequency was set at 12.8 kHz, the sampling interval was 2 minutes, and each sampling duration was 2.56 seconds. Two types of operating conditions were set: constant and time-varying. The operating condition settings are shown in Table 1.

[0114] Table 1 Working condition settings

[0115]

[0116] Table 2 gives the detailed information of each tested gearbox, including its corresponding operating conditions, lifespan and failure type.

[0117] Table 2 Gear life degradation data list

[0118]

[0119] The experiment uses the method proposed in the present invention to perform dynamic model iteration and uncertainty degradation evaluation on gear operation data. The experiment compares the method proposed in the present invention with three other evaluation methods. Method 1 uses an adaptive two-stage degradation framework based on Gaussian regression process for prediction; Method 2 establishes two models, a linear regression model and a power exponential model, to describe the slow degradation process and the accelerated degradation process respectively; Method 3 uses the BIC criterion for model selection, but does not perform dynamic model update. The four methods are compared using different gearboxes. Table 3 and Figure 3 The comparative effects of the root mean square error and cumulative relative accuracy of the operating data of the three gearboxes are listed.

[0120] Table 3 Comparison of root mean square error of four methods

[0121]

[0122] It can be seen from the experimental results that the uncertainty assessment method for electromechanical system performance degradation based on dynamic comprehensive indicators proposed by the method of the present invention has achieved good assessment results, and the assessment effect is significantly better than the other three existing methods.

[0123] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the devices disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0124] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one 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 limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators, characterized in that: The following steps are involved: Obtain real-time vibration signals of electromechanical systems and perform feature extraction; Based on the feature distribution extracted in real time from the electromechanical system and the feature distribution extracted in normal state, a real-time similarity feature index is constructed; Build a model selection library containing a variety of performance evaluation fitting models; Based on the real-time similarity feature index, a known data set is constructed, and each model in the model selection library is fitted based on the known data set; The fitting effect of each model is scored based on the PE criterion, and the model with the lowest score is selected as the optimal model at the current moment; Predict the instantaneous characteristic state of the electromechanical system at different future moments based on the optimal model selected at the current moment; Introducing process noise and performance monitoring noise into the prediction results, and computing uncertain evaluation results of the electrical system at different times; Performance prediction of electromechanical systems based on uncertainty assessment results.

2. The method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators according to claim 1 is characterized in that: The real-time vibration signal of the electromechanical system is expressed as ,in, is the running time, for The vibration signal value monitored at all times, is the signal sampling point, ; The formula for feature extraction of real-time vibration signals of electromechanical systems is: ； in, is the extracted features.

3. The method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators according to claim 1 is characterized in that: The calculation formula of feature similarity index is: ； in, Features extracted under normal conditions The distribution of Features extracted from vibration signal data at several consecutive moments updated in real time Distribution, through this calculation formula the characteristics Normalization reflects the similarity between the real-time update features and the features under normal operating conditions. The larger the value, the smaller the similarity between the two, and vice versa.

4. The method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators according to claim 1 is characterized in that: The model selection library includes basic models and complex models. The basic model is represented as: ； The complex model is expressed as: ； in, is the characteristic index value obtained by model fitting, is the characteristic index calculated at the initial moment, are the parameters that need to be fitted, is the running time, represents a standard Brownian motion process.

5. The method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators according to claim 1 is characterized in that: The process of fitting each model in the model selection library includes: Based on real-time monitoring and calculation of similarity feature indicators , build a known data set ,in is the running time, for The feature values ​​extracted at runtime, is the signal sampling point, ; Based on known datasets , and fit simple and complex models respectively.

6. The method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators according to claim 1 is characterized in that: The calculation formula for scoring the fitting effect of each model based on the PE criterion is: ； in, The number of unknown parameters in the fitted model for performance evaluation; is the number of known data involved in model fitting; For in time The true value obtained at To use the model at time The fitted value obtained at Represents the average trend value of the characteristic indicator, The weight for considering the effect of model degradation trend in the dynamic iteration process; Used to balance the situation where different models contain different numbers of unknown parameters; Whenever the vibration signal data of the electromechanical system is updated, the performance evaluation fitting models are calculated. Value, select The model with the lowest score evaluates the performance of the electromechanical system at that moment.

7. The method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators according to claim 1 is characterized in that: Introducing process noise and performance monitoring noise into the prediction results, the uncertain evaluation results of the computer electrical system at different times include: Introduce process noise into the prediction results at a certain moment and construct the characteristic change equation; Introduce performance monitoring noise and build performance monitoring functions; The covariance matrix is ​​introduced, and the best characteristic approximation of the electromechanical system at the next moment is estimated by knowing the best characteristic approximation of the electromechanical system at the previous moment, so as to obtain the uncertainty evaluation results of the electromechanical system at different future moments.

8. The method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators according to claim 7 is characterized in that: The expression of the characteristic change equation is: ； in, Indicates The instantaneous characteristic state of the electromechanical system at time is The instantaneous characteristic state of the electromechanical system at time Indicates the slave state To status The transfer matrix, is the process noise, is the control matrix, For electromechanical systems Gain variable at time; The expression of the performance monitoring function is: ； in, For The characteristic value of the electromechanical system at time For The moment feature change matrix, Monitor noise for features.

9. The method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators according to claim 8, characterized in that: The calculation formula for the uncertainty assessment results of the electromechanical system at different moments in the future is: ； in, For The best characteristic approximation of the electromechanical system at time, For The prior error covariance matrix at time t, for The best characteristic approximation of the electromechanical system at time, Indicates the slave state To status The transfer matrix of For The characteristic change matrix at each moment, For The characteristic change matrix at each moment, For The transpose of the characteristic change matrix at time , Monitoring noise for features The covariance matrix of For Eigenvalues ​​of the electromechanical system at the moment; For The covariance matrix of the prior error of the electromechanical system at time t is calculated as: ； in, is the transfer matrix The transposed matrix of for The covariance matrix of the electromechanical system at time t, Indicates The influencing variables of external factors during the operation of the electromechanical system at each moment.

10. The method for evaluating uncertainty of electromechanical system performance degradation based on dynamic comprehensive indicators according to claim 9, characterized in that: The calculation formula for predicting the performance of the electromechanical system based on the uncertainty assessment results is: ； in, For The remaining running time predicted at each moment, is the eigenvalue The lower limit of is The eigenvalues ​​obtained after uncertainty evaluation are, is the set failure characteristic value of the electromechanical system, Expressed as from From the moment until the eigenvalue is evaluated Reach failure characteristic value The time interval.

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