Method for evaluating influence of environmental stress on motor fault

Abstract

The invention relates to the technical field of motor faults, in particular to a method for evaluating the influence of environmental stress on motor faults. In order to solve the problem that a reliability assessment method for the influence of environmental stress on motor faults is lacked in the prior art, namely, the assessment method for the influence of environmental stress on motor faults comprises the following steps: 1) analyzing sensitive environmental stress; 2) environment stress influence analysis: acquiring operating environment stress A data when a fault product breaks down, taking the operating environment stress A data as a stress data set, acquiring operating environment stress A data during non-fault operation, taking the operating environment stress A data as an intensity data set, fitting stress data and intensity data distribution curves respectively, building an intensity-stress interference model, and calculating the influence of the intensity-stress interference model; finding a strength-stress intersection point and defining the point as a critical value t0; and 3) in the'strength-stress' interference model built in the step 2), taking the area of a region where the strength and the stress are overlapped as a measurement index for representing the environmental adaptability level of the product. The evaluation method is high in applicability.

Description

Technical Field

[0001] This invention relates to the field of motor fault technology, specifically to a method for assessing the impact of environmental stress on motor faults. Background Technology

[0002] Electric motors, as indispensable key equipment in people's daily lives, have a technological development dating back to the early 19th century, boasting a rich technological background and diverse types, such as synchronous motors, asynchronous motors, and permanent magnet motors. In modern society, electric motors are widely used. In the industrial sector, they drive various mechanical equipment and wind power generation equipment; in the transportation sector, motors are used in rail transit and electric vehicles; and in the household and commercial sectors, motors are used in home appliances and office equipment. With more and more electric motors being put into use, the number of malfunctions is also increasing, and users are paying more attention to product failures and maintenance. Based on experience and operational data, motor failures are strongly correlated with geographical location; however, most companies pay little attention to the environmental factors (high temperature, low temperature, humidity, altitude, air pressure, etc.) at the time of the failure when recording failure information. Statistics show that 34.7% of product failures are closely related to environmental stress (including natural environmental stress and induced environmental stress). Therefore, how can we determine the role of environmental factors in product failures? Which environmental factors is this product most sensitive to? How does its environmental adaptability perform? The above issues urgently require attention. Due to the lack of guidance from such analytical methods, the analysis of the impact of environmental factors on products is not in-depth enough, making it difficult to discover the impact of environmental stress on the reliability of motor operation.

[0003] Traditional motor fault analysis methods, such as Figure 1 As shown in flowchart 1, this method focuses on the fault itself of a specific product and finds the cause of the fault from the operating principle and structure of the motor (theoretically, environmental analysis is less considered in the man-machine-material-method-environmental testing). The conclusions are often reflected in the control logic, quality defects, etc., and rarely consider the impact of continuous long-term environmental stress on the product.

[0004] With the application of PHM technology to the motor industry, product fault analysis has made leaps and bounds. However, due to technological and cost limitations, current monitoring mainly focuses on specific fault modes in key components such as bearings, insulation, and permanent magnets. Data is acquired through sensors to monitor changes in characteristic parameters of key motor components, identify fault characteristics and parameter degradation trends, and implement health management. The fault analysis process follows... Figure 1The middle process 2 is executed. The PHM technology, although being a cutting-edge technology, can find faults in advance, but is only for key components, needs to find characteristic parameters and can monitor the parameters, has a narrow application range and is high in cost. The PHM technology currently comprises a big data analysis method based on a characteristic parameter change trend and a physical model-based analysis method, both of which are fault prediction methods, and does not analyze fault causes from the whole, so as to find the relationship between environmental stress and product faults. SUMMARY

[0005] The present application is to solve the problem that the prior art lacks a reliability evaluation method for the influence of environmental stress on motor faults, that is, an evaluation method for the influence of environmental stress on motor faults.

[0006] The present application is implemented by using the following technical solutions:

[0007] An evaluation method for the influence of environmental stress on motor faults comprises the following steps:

[0008] 1) Sensitivity analysis of sensitive environmental stress

[0009] a. Obtain operation fault data and operation environmental stress data of products of the same type;

[0010] b. Perform correlation analysis on the faults and the operation environmental stress based on a grey correlation degree method, calculate the sensitivity of various operation environmental stresses to the faults and compare them, and determine a type of operation environmental stress A with the highest sensitivity to the faults;

[0011] 2) Environmental stress influence analysis

[0012] Obtain operation environmental stress A data of a fault product when the fault occurs, take the data as a stress data set, obtain operation environmental stress A data when a non-fault operation is performed and take the data as a strength data set, respectively fit stress data and strength data distribution curves, build an "strength-stress" interference model, find an intersection point of the strength and the stress and define the point as a critical value t0, and when the operation environmental stress value exceeds t0, it indicates that the probability density of the type of motor to occur a fault is the largest;

[0013] 3) In the "strength-stress" interference model built in step 2), an area where the strength and the stress overlap is taken as a measurement index representing the environmental adaptability level of the product, and the area of the overlap is defined as an environmental adaptability e(t), The greater e(t) is, the greater the possibility of the product to occur a fault is, and the smaller e(t) is, the smaller the possibility of the product to occur a fault is.

[0014] Further, the specific steps of step b in step 1) are as follows: ① Perform dimensionless processing on all the data obtained in step a, and the processing formula is y n = x n / x0, where y n Here are dimensionless processed values ​​of a certain type of environmental stress data, where x n To obtain a certain type of environmental stress data, x0 is the baseline value in the certain type of environmental stress data; ② Calculate the absolute value of the difference between each dimensionless data point and the corresponding number of failures to obtain the absolute value δ corresponding to each environmental stress value. n (i); ③ Obtain the maximum value δ of all absolute values ​​in step ②. max and δ min ④ Calculate the corresponding relationship coefficient ε according to the formula. n (i)=(δ min +ρδ max ) / (δ n (i)+ρδ max ), where ρ is taken as 0.5; ⑤ Calculate the correlation degree of various environmental stress data according to the following formula,

[0015]

[0016] ⑥ Compare the correlation of various environmental stress data obtained in step ⑤. The one with the largest γ(i) value is the type of operating environment stress A with the highest sensitivity to faults.

[0017] The beneficial effects of this invention are as follows: The evaluation method described in this invention supports the analysis of sensitive environmental stress during online operation of products, supports the determination of the critical value of environmental stress that products can withstand during online operation, supports the evaluation of the environmental adaptability of products and the feasibility calculation of applying products to new environments, supports the analysis of the impact of environmental stress during fault analysis, and provides a convenient evaluation method for the environmental adaptability design of motor products, thus supporting the environmental adaptability design of motor products. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 A flowchart of the fault analysis method described in the background section;

[0021] Figure 2 This is a flowchart of the evaluation method described in this invention;

[0022] Figure 3 "strength-stress" interference model built in step 2) in the evaluation method of the present application;

[0023] Figure 4 environmental fitness representation in the evaluation method of the present application. DETAILED DESCRIPTION

[0024] In order to enable persons skilled in the art to more clearly understand the above-mentioned objects, features and advantages of the present application, the schemes of the present application will be further described below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0025] In the description, it should be noted that the terms "first", "second" are only for the purpose of description, and cannot be understood as indicating or implying relative importance. It should be noted that, unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood in a broad sense, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0026] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application, but the present application can also be implemented in other ways different from those described herein; obviously, the examples in the description are only some of the embodiments of the present application, not all the embodiments.

[0027] The specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0028] As Figure 2 shown, an evaluation method of environmental stress on motor failure, comprising the following steps:

[0029] 1) sensitive environmental stress analysis

[0030] a, obtain the running failure data and running environmental stress data of the same type of product:

[0031] The running regions of a certain type of wind turbine are Zhejiang Province, Jiangsu Province, Guangdong Province, Anhui Province, Hunan Province, Shandong Province and Heilongjiang Province. Extract the meteorological data (extreme value) and failure data of the wind turbine in the running time range, and organize them into a data table, as shown in Table 1:

[0032] Table 1

[0033] Region Temperature - high temperature Temperature - low temperature Atmospheric pressure Salt spray Pollution Humidity Number of failures Zhejiang Province 40 -9 42.6 33 1 73 6 Jiangsu Province 41 -10 171.2 1.6 1 77 13 Guangdong Province 39 0 122.3 195 1 79 6 Anhui Province 40 -15 33.9 8 0 82 5 Hunan Province 41 -4 201.2 1.6 0 79 10 Shandong Province 40 -20 171.2 1.6 1 57 10 Heilongjiang Province 35 -34 117.7 8 0 70 5

[0034] b. Based on the grey relational analysis method, a correlation analysis is performed between faults and operating environment stresses. The sensitivity of various operating environment stresses to faults is calculated and compared to determine the operating environment stress A with the highest sensitivity to faults. The specific steps are as follows:

[0035] ① Since the data in each column is of different types, it is impossible to calculate the correlation degree. Therefore, the data table is processed to be dimensionless, and the processing formula is y n =x n / x0, where y n Here are dimensionless processed values ​​of a certain type of environmental stress data, where x n To obtain a certain type of environmental stress data, x0 is the baseline value in the certain type of environmental stress data. The processing results are shown in Table 2 (e.g., the data for high temperature locations in Jiangsu Province in Table 2 is 41 / 40 = 1.025):

[0036] Table 2

[0037] Region Temperature - high temperature Temperature - low temperature Atmospheric pressure Salt spray Pollution Humidity Number of failures Zhejiang Province 1 1 1 1 1 1 1 Jiangsu Province 1.025 1.1111 4.0188 0.0485 1 1.0548 2.1667 Guangdong Province 0.975 0 2.8709 5.9091 1 1.0822 1 Anhui Province 1 1.6667 0.7958 0.2424 0 1.1233 0.8333 Hunan Province 1.025 0.4444 4.7230 0.0485 0 1.0822 1.6667 Shandong Province 1 2.2222 4.0188 0.0485 1 0.7808 1.6667 Heilongjiang Province 0.875 3.7778 2.7629 0.2424 0 0.9589 0.8333

[0038] ② Calculate the absolute value of the difference between each dimensionless data point and the corresponding number of failures to obtain the absolute value δ corresponding to each environmental stress value. n (i) The processing results are shown in Table 3 (the data for the high temperature area in Jiangsu Province in Table 3 is 2.1667-1.025=1.142);

[0039] Table 3

[0040] Region Temperature - high temperature Temperature - low temperature Atmospheric pressure Salt spray Pollution Humidity Zhejiang Province 0 0 0 0 0 0 Jiangsu Province 1.142 1.056 1.852 2.118 1.167 1.112 Guangdong Province 0.025 1.000 1.871 4.909 0.000 0.082 Anhui Province 0.167 0.833 0.038 0.591 0.833 0.290 Hunan Province 0.642 1.222 3.056 1.618 1.667 0.584 Shandong Province 0.667 0.556 2.352 1.618 0.667 0.886 Heilongjiang Province 0.042 2.944 1.930 0.591 0.833 0.126

[0041] ③ Obtain the maximum value of all absolute values ​​in step ②, δmax = 4.909 and δmin = 0;

[0042] ④ Calculate the corresponding relationship coefficient ε according to the formula. n (i)=(δ min +ρδ max ) / (δ n (i)+ρδ max ), where ρ is taken as 0.5, and the calculation results are shown in Table 4;

[0043] Table 4

[0044] Province Temperature - high temperature Temperature - low temperature Atmospheric pressure Salt spray Pollution Humidity Zhejiang Province 1 1 1 1 1 1 Jiangsu Province 0.683 0.699 0.570 0.537 0.678 0.688 Guangdong Province 0.990 0.711 0.567 0.333 1.000 0.968 Anhui Province 0.936 0.747 0.985 0.806 0.747 0.894 Hunan Province 0.793 0.668 0.445 0.603 0.596 0.808 Shandong Province 0.786 0.815 0.511 0.603 0.786 0.735 Heilongjiang Province 0.983 0.455 0.560 0.806 0.747 0.951 Correlation degree 6.171 5.094 4.638 4.687 5.553 6.044

[0045] ⑤ Calculate the correlation of various environmental stress data according to the following formulas. The calculation results are shown in the last column of Table 4.

[0046]

[0047] ⑥ Compare the correlation of various environmental stress data obtained in step ⑤. The one with the largest γ(i) value is the type of operating environment stress A with the highest sensitivity to faults, namely temperature-high temperature.

[0048] 2) Environmental stress influence analysis

[0049] Temperature-high temperature data at the time of the faulty product's failure was obtained and used as a stress dataset. Temperature-high temperature data during normal operation was obtained and used as a strength dataset. The distribution curves of the stress and strength data were then fitted separately. In other words, data analysis software was used to fit the strength and stress distribution types to show that they belong to a normal distribution. The distribution function is as follows:

[0050]

[0051] like Figure 3 As shown, a strength-stress interference model was built, the strength-stress intersection point was found and defined as the critical value t0. When the stress value of the operating environment exceeds t0 (t0 = 23.05℃), it indicates that the probability density of failure of this type of motor is the highest.

[0052] 3) In the "strength-stress" interference model built in step 2), the region where strength and stress overlap is used as a measure of the product's environmental adaptability level, such as... Figure 4 As shown, the area of ​​the overlapping region is defined as the environmental fitness e(t). The larger e(t) is, the greater the probability of product failure; the smaller e(t) is, the less likely product failure is.

[0053] Subsequent practical verification showed that the results obtained from the above evaluation method are reliable, and the main environmental factor affecting the failure of this series of motors is high temperature.

[0054] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the present invention. Although detailed descriptions have been provided with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments, and they should all be covered within the protection scope of the claims.

Claims

1. A method of assessing the impact of environmental stresses on motor faults, characterized in that, Comprising the following steps: 1) Sensitivity analysis of environmental stress a, Obtain the operation failure data and operation environmental stress data of the same type of product; b, Based on the grey correlation degree method, the correlation analysis of failure and operation environmental stress is carried out, the sensitivity of various operation environmental stresses to failure is calculated and compared, and the operation environmental stress A with the highest sensitivity to failure is determined; 2) Environmental stress impact analysis Obtain the operation environmental stress A data of the fault product when the fault occurs, and take it as the stress data set, obtain the operation environmental stress A data when the non-fault operation is carried out and take it as the strength data set, respectively fit the stress data and strength data distribution curve, build the "strength-stress" interference model, find the strength-stress intersection point and define the point as the critical value t0, when the operation environmental stress value exceeds t0, it indicates that the probability density of the failure of the motor is the largest; 3) In the "strength-stress" interference model built in step 2), the area of the overlap region of strength and stress is defined as the environmental adaptability e(t), the larger the e(t) is, the greater the possibility of product failure is, and the smaller the e(t) is, the smaller the possibility of product failure is.

2. A method of assessing the effect of environmental stresses on the failure of an electrical machine according to claim 1, characterized in that, The specific steps of step b in step 1) are as follows: ① Perform dimensionless processing on all the data obtained in step a, and the processing formula is y n =x n / x0, where y n Here are dimensionless processed values ​​of a certain type of environmental stress data, where x n To obtain a certain type of environmental stress data, x0 is the baseline value in the certain type of environmental stress data; ② Calculate the absolute value of the difference between each dimensionless data point and the corresponding number of failures to obtain the absolute value δ corresponding to each environmental stress value. n (i); ③ Obtain the maximum value δ of all absolute values ​​in step ②. max and δ min ④ Calculate the corresponding relationship coefficient ε according to the formula. n (i)=(δ min +ρδ max ) / (δ n (i)+ρδ max ), where ρ is taken as 0.5; ⑤ Calculate the correlation degree of various environmental stress data according to the following formula, ⑥ Compare the correlation of various environmental stress data obtained in step ⑤. The one with the largest γ(i) value is the type of operating environment stress A with the highest sensitivity to faults.

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