A noise-resistant motor bearing fault adaptive detection method

Abstract

The present application belongs to the field of fault detection, and particularly relates to an anti-noise motor bearing fault adaptive detection method. The method of the present application firstly determines the installation positions of a vibration sensor and a rotating speed sensor, sets the initial value N0, the minimum value N min and the maximum value N max of the number N of synchronous sampling data points per rotation of the motor rotating shaft, the number L of rotations of the motor rotating shaft in a detection time window, the threshold values T1 and T2; secondly, acquires N groups of periodic synchronous single-point sampling data Y; then, calculates the correlation coefficient of the adjacent two groups of periodic synchronous single-point sampling data in the data Y to obtain data C, and calculates the average value M of the absolute value of the data C; finally, judges whether the motor bearing has a fault and realizes adaptive data acquisition. The method of the present application uses the correlation coefficient as the fault feature, suppresses the influence of the disturbance noise on the detection result, reliably depicts the degradation state of the bearing, reduces the misdiagnosis rate and the missed diagnosis rate of the fault detection, and further improves the reliability of the motor bearing fault adaptive detection; meanwhile, the adaptive data acquisition strategy is introduced, which can adaptively determine the number N of synchronous sampling data points per rotation of the motor rotating shaft, reduces the data acquisition, transmission and processing cost under the condition of ensuring the reliability of the fault detection.

Description

Technical Field

[0001] This invention belongs to the field of fault detection, specifically relating to an adaptive method for noise-resistant motor bearing fault detection technology. Background Technology

[0002] Electric motors include electric motors and generators. An electric motor is a common electrical device that converts electrical energy into mechanical energy, while a generator is a common electrical device that converts mechanical energy into electrical energy. Currently, electric motors are widely used in industry, energy, agriculture, transportation, and household appliances, and are an indispensable power source in modern industry and daily life. Motor bearings play a crucial role in electric motors, not only supporting the weight of the motor rotor and ensuring its smooth rotation, but also reducing friction and absorbing vibrations and shocks generated during operation. Long-term operation of an electric motor can lead to bearing degradation and a certain risk of fatigue failure. Studies have shown that fatigue failure typically occurs in the inner ring, outer ring, and rolling elements of the bearing. To improve the working efficiency of the motor and avoid damage caused by bearing failure, it is necessary to perform fault detection on the motor bearings, thereby improving the reliability of motor operation.

[0003] Currently, researchers have proposed various motor bearing fault detection technologies. Data acquisition and feature extraction are crucial steps in motor bearing fault detection. Using inappropriate data acquisition methods or extracting unsuitable fault features can not only reduce the reliability of fault detection but also produce erroneous results. Current bearing fault detection methods often employ fixed or variable detection interval sampling. Fixed detection interval sampling can lead to data redundancy or missing data. Variable detection interval sampling adaptively determines the detection interval based on the bearing's degradation state. This method can overcome the shortcomings of fixed detection interval sampling to some extent. However, bearing faults can occur at any stage of operation, not just during the fault detection phase. In this case, neither of the above sampling methods can solve the problem of missed fault detection. To overcome the shortcomings of the above two sampling methods and reduce data acquisition costs, invention patent application number 2024115572115 discloses a real-time motor bearing fault detection method based on adaptive data acquisition. While this method can address the issue of missed fault detection and reduce data acquisition, the fault characteristics it employs are significantly affected by disturbances and noise, indicating poor noise immunity and an inability to effectively characterize the bearing's degradation state. In practice, signal disturbances and noise are unavoidable and vary with the bearing's health condition. These changing disturbances and noise severely limit the reliability of the fault detection method.

[0004] To improve the noise resistance of adaptive fault detection methods, this invention discloses a noise-resistant adaptive fault detection method for motor bearings. The method disclosed in this invention introduces a correlation coefficient as a fault feature, suppressing the impact of disturbance noise on the reliability of motor fault detection. The fault features used in this invention are well applicable to various bearing health states and can reliably characterize bearing degradation states, thus providing a reference for adaptive data acquisition. Summary of the Invention

[0005] Purpose of the invention: To address the problems in the background art, this invention discloses a noise-resistant adaptive detection method for motor bearing faults. By introducing correlation coefficients and adaptive decision-making, the influence of disturbance noise on fault characteristics is reduced, and the number of data points synchronously collected per revolution of the motor shaft can be adaptively determined. This not only ensures the reliability of bearing fault detection but also reduces the amount of data collected, thereby effectively reducing the cost of data collection, transmission, and processing.

[0006] Technical solution: This invention provides an adaptive detection method for motor bearing faults with noise resistance, comprising the following steps:

[0007] Step 1: Determine the installation locations of the vibration sensor and speed sensor, and set the initial value N0 and minimum value N0 of the number of synchronous sampling data points N per revolution of the motor shaft. min and maximum value N max The motor shaft rotation speed L, thresholds T1 and T2, are measured within a detection time window.

[0008] A vibration sensor and a speed sensor are installed at the pre-test bearing and near the motor shaft, respectively, to measure the bearing vibration signal and the motor shaft speed signal. The initial value N0 of the number of synchronously sampled data per shaft revolution is set to... The `round()` function rounds the bearing to the nearest integer. Thresholds T1 and T2 are set based on statistical values ​​of fault characteristics under both fault-free and faulty bearing conditions.

[0009] Step 2: Obtain N sets of periodically synchronized single-point sampling data Y.

[0010] Based on the vibration sensor, the speed sensor, and the number N of synchronous sampling data points per revolution of the motor shaft, N sets of periodic synchronous single-point sampling data Y are obtained. This data can be expressed as:

[0011]

[0012] Among them, Y i =[y(i)y(N+i)y(2N+i)…y((L-1)N+i)] represents the i-th group of periodic synchronous single-point sampling data.

[0013] Step 3: Calculate the correlation coefficient of two adjacent sets of periodically synchronized single-point sampled data in data Y to obtain data C, and calculate the average value M of the absolute value of data C.

[0014] Based on the N sets of periodically synchronized single-point sampling data in data Y, N-1 correlation coefficients can be obtained, denoted as data C.

[0015] C=[r(1)r(2)r(3)…r(i)…r(N-1)],

[0016] Among them, the adjacent two sets of periodically synchronized single-point sampling data Y i and Y i+1 The correlation coefficient r(i) can be expressed as:

[0017]

[0018] In the formula, cov(Y) i ,Y i+1 Y represents two adjacent sets of periodically synchronized single-point sampling data. i And data Y i+1 covariance, and These represent two adjacent sets of periodically synchronized single-point sampling data Y. i and Y i+1 The standard deviation.

[0019] Taking the absolute value of the data C and then calculating the average M can be expressed as:

[0020]

[0021] Step 4: Determine if the motor bearing is faulty and implement adaptive data acquisition.

[0022] If M is less than or equal to T1, the motor bearing is determined to be in a fault-free state, and the number of synchronous sampling data points N per revolution of the motor shaft is adaptively adjusted to N0. min Then return to step 2 to continue fault detection; if M is greater than T1 and less than T2, it is impossible to reliably determine whether the bearing has failed. In this case, the number of synchronous sampling data points N per revolution of the motor shaft is set to Then return to step 2 to continue fault detection; if M is greater than or equal to T2, it is considered that the motor bearing has failed.

[0023] Beneficial effects:

[0024] 1. The present invention discloses a noise-resistant adaptive detection method for motor bearing faults, which overcomes the shortcomings of existing real-time detection methods for motor bearing faults that are affected by disturbance noise, and can be applied to fault detection at various health stages of motor bearings.

[0025] 2. The method of the present invention uses the correlation coefficient as a fault feature, which suppresses the influence of disturbance noise on the detection results, reduces the false diagnosis rate and false negative rate of fault detection, and improves the reliability of motor bearing fault detection.

[0026] 3. The method of this invention introduces an adaptive data acquisition strategy, which can adaptively determine the number N of synchronously sampled data points per revolution of the motor shaft. This reduces the costs of data acquisition, transmission, and processing while ensuring the reliability of fault detection.

[0027] 4. The method of the present invention has low computational cost and does not require a high-speed control unit, thereby reducing the cost of the fault detection system.

[0028] 5. The method of the present invention can be applied to the field of bearing fault detection in variable speed motors. Attached Figure Description

[0029] Figure 1 This is a flowchart of the adaptive fault detection process for motor bearings according to the method of the present invention;

[0030] Figure 2 The probability density function and threshold for fault characteristics under fault-free and fault conditions;

[0031] Figure 3 The test results are for when the motor bearings are fault-free.

[0032] Figure 4 N represents the number of synchronously sampled data points per revolution of the motor shaft when the motor bearings are fault-free.

[0033] Figure 5 The test results are for a motor bearing failure.

[0034] Figure 6 N represents the number of synchronously sampled data points per revolution of the motor shaft when the motor bearing fails.

[0035] Figure 7 This is a graph showing the variation of the mean of the standard deviation and the mean of the absolute value of the correlation coefficient with noise. Detailed Implementation

[0036] The implementation process of the present invention will be described below with reference to the accompanying drawings.

[0037] This invention discloses an adaptive detection method for motor bearing faults based on correlation coefficients, such as... Figure 1 As shown, it includes the following steps:

[0038] Step 1: Determine the installation locations of the vibration sensor and speed sensor, and set the initial value N0 = 51 and the minimum value N0 = 51 for the number of synchronous sampling data points N per revolution of the motor shaft. min =2, maximum value N max=100, the number of motor shaft rotations in a detection time window L=10, thresholds T1=0.287 and T2=0.320.

[0039] A vibration sensor and a speed sensor are installed at the pre-test bearing and near the motor shaft, respectively, to measure the bearing vibration signal and the motor shaft speed signal. The initial value N0 of the number of synchronously sampled data per shaft revolution is set to... Thresholds T1 and T2 are set based on the statistical values ​​of fault characteristics under both fault-free and faulty bearing conditions, such as... Figure 2 As shown.

[0040] Step 2: Obtain N sets of periodically synchronized single-point sampling data Y.

[0041] Based on the vibration sensor, the speed sensor, and the number N of synchronous sampling data points per revolution of the motor shaft, N sets of periodic synchronous single-point sampling data Y are obtained. This data can be expressed as:

[0042]

[0043] Among them, Y i =[y(i)y(N+i)y(2N+i)…y((L-1)N+i)] represents the i-th group of periodic synchronous single-point sampling data.

[0044] Step 3: Calculate the correlation coefficient of two adjacent sets of periodically synchronized single-point sampled data in data Y to obtain data C, and calculate the average value M of the absolute value of data C.

[0045] Based on the N sets of periodically synchronized single-point sampling data in data Y, N-1 correlation coefficients can be obtained, denoted as data C.

[0046] C=[r(1)r(2)r(3)…r(i)…r(50)],

[0047] Among them, the adjacent two sets of periodically synchronized single-point sampling data Y i And data Y i+1 The correlation coefficient r(i) can be expressed as:

[0048]

[0049] In the formula, cov(Y) i ,Y i+1 Y represents two adjacent sets of periodically synchronized single-point sampling data. i And data Y i+1 covariance, and These represent two adjacent sets of periodically synchronized single-point sampling data Y. i And data Y i+1 The standard deviation.

[0050] Taking the absolute value of the data C and then calculating the average M can be expressed as:

[0051]

[0052] Step 4: Determine if the motor bearing is faulty and implement adaptive data acquisition.

[0053] If the fault characteristic M is less than or equal to T1 = 0.287, the motor bearing is determined to be in a fault-free state, and the number of synchronous sampling data points N per revolution of the motor shaft is adaptively adjusted to N0.287. min =2, and return to step 2 to continue fault detection; if M is greater than T1 = 0.287 and less than T2 = 0.320, it is impossible to reliably determine whether a fault has occurred. In this case, the number of synchronous sampling data points N per revolution of the motor shaft is set to Then return to step 2 to continue fault detection; if M is greater than or equal to T2 = 0.320, the motor bearing is considered to have failed. Throughout the fault detection process, the number of synchronous sampling data points N per revolution of the motor shaft is adaptively adjusted in the same way to continuously detect whether a bearing fault has occurred until a fault is detected in the motor bearing.

[0054] Figure 3 The test results are shown when the motor bearing is fault-free. It can be seen that the fault feature M in all 30 time windows is less than the threshold T2. Fault judgment is performed according to step 4, and the process returns to step 2 for adaptive data acquisition, and then continues fault detection. Figure 4 The figure shows the number of synchronous sampling data points N per shaft revolution when the motor bearing is fault-free. As can be seen from the figure, since the bearing is in a fault-free state, the adaptive synchronous sampling data N per shaft revolution for each fault detection is less than the maximum value of 100, which reduces the number of synchronous sampling data points N within the shaft, thereby reducing the cost of data acquisition, processing and transmission.

[0055] Figure 5 This is the detection result when the motor bearing fails. As can be seen from the figure, the fault characteristic detected in the first test is greater than the threshold T2, which means that a fault has occurred. At this point, an alarm signal is issued, the motor operation is terminated, and the fault detection process is stopped. Figure 6 The number of synchronous sampling data points N per rotation of the shaft when the motor bearing fails is given. Since the bearing being tested is in a faulty state, the method of this invention can quickly detect the motor bearing fault.

[0056] The method of this invention introduces a correlation coefficient and uses it as a fault feature to suppress the influence of disturbance noise on the detection results. Figure 7The graph shows the average standard deviation and the curves of fault characteristics as a function of noise, as presented by the method of this invention. Compared to the average standard deviation, the fault characteristics of the method of this invention are less susceptible to disturbance noise, exhibiting better noise resistance. The fault characteristics of the method of this invention are well applicable to various health stages of bearings and can reliably reflect the bearing degradation state, thus providing a reference for adaptive data acquisition. Furthermore, the method of this invention performs adaptive data acquisition based on the bearing degradation state, reducing the amount of synchronously collected data per rotation of the shaft. This reduces the cost of data acquisition, transmission, and processing while ensuring the reliability of fault detection. Compared to traditional fault detection methods, this method determines the fault threshold based on the statistical results of fault characteristics when the bearing has and does not have a fault, and compares it with the current fault characteristics to determine the fault and achieve adaptive data acquisition. If the current fault characteristic M is less than or equal to T1, the bearing is determined to be in a fault-free state, and the number of synchronously sampled data points N per rotation of the motor shaft is set to the minimum value N. min Then return to step 2 to continue fault detection; when the fault characteristic M is greater than T1 and less than T2, it is impossible to determine whether the bearing has failed. In this case, N is set to Then return to step 2 to continue fault detection; when the fault characteristic M is greater than or equal to the threshold T2, the bearing is determined to be faulty.

[0057] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All equivalent transformations or modifications made in accordance with the spirit and essence of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A noise-resistant adaptive detection method for motor bearing faults, characterized in that, Includes the following steps: Step 1: Determine the installation locations of the vibration sensor and speed sensor, and set the initial value N0 and minimum value N0 of the number of synchronous sampling data points N per revolution of the motor shaft. min and maximum value N max The number of motor shaft revolutions L, thresholds T1 and T2 within a detection time window; Step 2: Obtain N sets of periodically synchronized single-point sampling data Y; Step 3: Calculate the correlation coefficient of two adjacent sets of periodically synchronized single-point sampled data in data Y to obtain data C, and calculate the average value M of the absolute value of data C; Step 4: Determine if the motor bearing is faulty and implement adaptive data acquisition.

2. The noise-resistant adaptive detection method for motor bearing faults according to claim 1, characterized in that, In step 1, the vibration sensor and the speed sensor are installed at the pre-detection bearing and near the shaft, respectively, to measure the bearing vibration signal and the motor shaft speed signal.

3. The noise-resistant adaptive detection method for motor bearing faults according to claim 1, characterized in that, In step 1, the initial value N0 of the number of synchronously sampled data per revolution of the shaft is set to... The `round()` function is used for rounding, and the result is an integer.

4. The noise-resistant adaptive detection method for motor bearing faults according to claim 1, characterized in that, In step 1, thresholds T1 and T2 are set based on the statistical values ​​of fault characteristics under no-fault and fault conditions of the bearing.

5. The noise-resistant adaptive detection method for motor bearing faults according to claim 1, characterized in that, In step 2, based on the vibration sensor, the speed sensor, and the number N of synchronous sampling data points per revolution of the motor shaft, N sets of periodic synchronous single-point sampling data Y are obtained. This data can be represented as: Among them, Y i =[y(i) y(i+N) y(i+2N) … y((L-1)N+i)] represents the i-th group of periodic synchronous single-point sampling data.

6. The noise-resistant adaptive detection method for motor bearing faults according to claim 1, characterized in that, In step 3, the correlation coefficient of two adjacent sets of periodically synchronized single-point sampled data in data Y is calculated to obtain data C = [r(1)r(2)r(3)...r(i)...r(N-1)], where the correlation coefficient of two adjacent sets of periodically synchronized single-point sampled data Y is calculated to obtain data C = [r(1)r(2)r(3)...r(i)...r(N-1)]. i and Y i+1 correlation coefficient In the formula, cov(Y) i ,Y i+1 Y represents two adjacent sets of periodically synchronized single-point sampling data. i And data Y i+1 covariance, and These represent two adjacent sets of periodically synchronized single-point sampling data Y. i and Y i+1 The standard deviation.

7. The noise-resistant adaptive detection method for motor bearing faults according to claim 1, characterized in that, In step 3, the average value of the absolute value of data C is calculated.

8. The noise-resistant adaptive detection method for motor bearing faults according to claim 1, characterized in that, In step 4, M is used as a fault feature and compared with thresholds T1 and T2. If the fault feature M is less than or equal to T1, the motor bearing is determined to be in a fault-free state, and the number of synchronous sampling data points N per revolution of the motor shaft is adaptively adjusted to N0. min Then return to step 2 to continue fault detection; if the fault characteristic M is greater than T1 and less than T2, it is impossible to reliably determine whether a fault has occurred. In this case, the number of synchronous sampling data points N per revolution of the motor shaft is set to Then return to step 2 to continue fault detection; if the fault characteristic M is greater than or equal to T2, it is considered that the motor bearing has failed.

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