Adaptive msb planetary gear train fault diagnosis method based on meshing period characteristics

Abstract

The present application relates to wind turbine gearbox fault diagnosis, vibration signal processing technical field, specifically discloses a kind of based on meshing period characteristic adaptive MSB planetary gear train fault diagnosis method, comprising the following steps: S1 obtains the original vibration signal and rotational speed pulse signal in the operation process of planetary gearbox, the original vibration signal is resampled, and the vibration signal after resampling is obtained;S2, the reset period of planetary gearbox is calculated, and the resampling signal is segmented according to the reset period, to obtain several reset period signal segments;S3, the sparse feature change trend of the separated segment signal is calculated using GI index, and the signal segment with the maximum GI index is extracted as the fault signal segment;S4, MSB is applied to the fault signal segment matrix to extract fault characteristics.The present application can identify the early fault information contained in vibration signal, adaptively select MSB segment, avoid the weakening of weak fault characteristic signal to signal in the average process, and effectively extract fault characteristic frequency.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wind turbine gearbox fault diagnosis and vibration signal processing, and specifically provides a self-adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics. BACKGROUND

[0002] As a kind of high efficiency, compact, low noise, long life and high load capacity transmission device, planetary gearbox plays a crucial role in wind turbine with its unique structure and superior performance, and wind turbine gearbox needs to run for a long time under high wind speed, heavy load and complex working conditions, and the compact design and high efficiency transmission characteristics of planetary gearbox make it an indispensable core component in wind power generation system, however, during use, the planetary gearbox is prone to pitting and cracking and other faults, which will cause the performance of the gearbox to decline, and in severe cases, may cause equipment damage and even shutdown, causing huge economic losses, therefore, timely detection of the fault condition of the planetary gearbox is of great significance to improve the reliability, operation stability and economic benefit of the wind turbine.

[0003] Traditional fault diagnosis methods have obvious limitations when facing planetary gearboxes, unlike fixed shaft gear trains, the continuous relative motion between gears in planetary gear trains causes the meshing position and vibration signal transmission path to change constantly, which poses a great challenge to fault feature extraction and diagnosis, and the vibration signals collected by sensors are often modulated by complex transmission paths, which masks the true fault information, making it difficult to achieve accurate diagnosis.

[0004] In order to improve the accuracy of planetary gearbox fault diagnosis, it is necessary to weaken the influence of time-varying transmission path on fault vibration, therefore, a planetary gear train fault diagnosis method needs to be designed to avoid the additional modulation effect of time-varying transmission path on vibration signals, effectively extract fault feature frequencies and improve the accuracy of fault diagnosis. SUMMARY

[0005] To solve the above problems, the present application provides a self-adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics, which can adaptively select MSB segments, avoid weakening of weak fault characteristic signals in the averaging process, effectively extract fault feature frequencies and improve the accuracy of fault diagnosis.

[0006] The self-adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics provided by the present application comprises the following steps:

[0007] S1, obtaining original vibration signals and speed pulse signals during operation of the planetary gearbox, resampling the original vibration signals through angle resampling, and obtaining resampled vibration signals;

[0008] S2. Calculating the reset period of the planetary gearbox according to the meshing period characteristics of the planetary gear train, segmenting the resampled signal according to the reset period, and obtaining a plurality of reset period signal segments;

[0009] S3. Calculate the sparse feature change trend of the separated segment signals using the GI index, and extract the signal segment with the largest GI index as the fault signal segment;

[0010] S4. Apply MSB to the fault signal fragment matrix to suppress the participating noise and demodulate the coupled frequency in the signal and extract the fault features.

[0011] Furthermore, the calculation formula for the reset period of the planetary gear train in step S2 is as follows:

[0012]

[0013] Among them, LCM represents the least common multiple, Indicates the number of teeth on the ring gear. Indicates the number of teeth on the faulty planetary gear or sun gear.

[0014] Furthermore, in step S3, the fault signal segment is selected as follows:

[0015] S31, segmenting the separated signal segments with different reset periods through a time-varying sliding window;

[0016] S32. Calculating the sparse feature change trend of different signal segments by GI index;

[0017] S33. Select the signal segment with the largest sparse feature as the fault signal segment for separation, and construct a fault signal segment matrix.

[0018] Furthermore, the calculation formula of the GI index in step S32 is as follows:

[0019]

[0020] in, Yes Sort in ascending order, ; express of norm; N Indicates signal x The total length of n Indicates the index position of the current summation.

[0021] Furthermore, step S4 is implemented by the following formula:

[0022]

[0023] in, To obtain the ensemble average of the MSB matrix from multiple modulation signals; for The complex conjugate of is the carrier frequency; is the modulation frequency; Upper and lower sideband frequencies; is the bispectrum of the modulation signal.

[0024] Furthermore, in step S1, the original vibration signal is obtained by an acceleration sensor mounted on the planetary gearbox housing, and the speed pulse signal is obtained by a photoelectric sensor on an encoder mounted on the shaft.

[0025] Furthermore, in step S1 , when resampling is performed, the resampling frequency is set to the number of points per rotation of the planet carrier.

[0026] Furthermore, in step S1, a simulation signal model is used to simulate the vibration signal of the planetary gearbox when a fault actually occurs. Specifically, the gear meshing modulation signal is simulated by an amplitude-frequency modulation signal, the time-varying transmission path of the planetary gear train vibration is simulated by a Hanning window, and a Gaussian white noise signal with a signal-to-noise ratio of 10 dB is added together; the simulation signal model is:

[0027]

[0028] in, represents the original vibration signal, Indicates the meshing AM / FM signal, represents the Hanning window, represents a Gaussian white noise signal with a signal-to-noise ratio of 10dB.

[0029] Furthermore, the gear meshing AM / FM signal is expressed as:

[0030]

[0031] in, >0, >0 indicates the amplitude of amplitude modulation and frequency modulation respectively; represents the fault characteristic frequency of the gear, t represents time, represents the meshing frequency of the gears, 、 and They are the initial phase of amplitude modulation, frequency modulation and the phase offset of the carrier respectively.

[0032] Furthermore, the Hanning window signal is expressed as:

[0033]

[0034] in, Represents the length of a single window function, Represents the time domain position of the window function.

[0035] Compared with the prior art, the present invention can achieve the following beneficial effects:

[0036] 1. To identify early fault information contained in vibration signals, the present invention proposes an adaptive MSB planetary gear fault diagnosis method based on the meshing period characteristics according to the kinematic laws of planetary gear meshing. Compared with the existing technology, the present invention combines the meshing period characteristics of the planetary gear train, separates the resampled signal according to the reset period, and corresponds the vibration signals in different reset periods to the planetary gear train meshing timing relationship one by one, so that the vibration signal segment closest to the sensor is separated, avoiding the additional modulation effect of the time-varying transmission path on the vibration signal.

[0037] 2. The present invention uses the fault signal segment closest to the sensor to perform the MSB method, which solves the problem that the MSB does not consider the influence of the time-varying transmission path on the vibration signal during signal processing, avoids the reduction of the MSB feature enhancement effect in the averaging process, and improves the accuracy of the MSB method in fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is an overall flow chart of a planetary gear train fault diagnosis method according to an embodiment of the present invention;

[0039] Figure 2 is a time domain diagram of a vibration signal simulated in a planetary gear train fault diagnosis method provided by an embodiment of the present invention;

[0040] Figure 3 is a spectrum diagram of a vibration signal simulated in a planetary gear train fault diagnosis method provided by an embodiment of the present invention;

[0041] Figure 4 1. A graph showing the change regularity of the GI index characteristics of different reset period signals of the time domain vibration signal in the planetary gear train fault diagnosis method provided by an embodiment of the present invention;

[0042] Figure 5 It is an MSB diagram of the same area where the maximum value of the average GI index in different reset cycles is selected in the planetary gear train fault diagnosis method provided by an embodiment of the present invention;

[0043] Figure 6 This is an MSB diagram of the same area where the minimum value of the average GI index in different reset cycles is selected in the planetary gear train fault diagnosis method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] For the purpose, technical solutions and advantages of the present application to be more clearly and intelligibly understood, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present application, but not to limit the present application. Figures 1-6 The present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely intended to explain the present application, but not to limit the present application.

[0045] A self-adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics, as shown in the accompanying drawings, comprises the following steps: Figure 1

[0046] S1, obtaining original vibration signals and rotation speed pulse signals in the operation process of the planetary gear box, the original vibration signals being obtained by an acceleration sensor installed on the planetary gear box body, and the rotation speed pulse signals being obtained by an optical sensor on an encoder installed on the shaft, in the present example, the number of teeth of the sun gear in the planetary gear train is , the number of teeth of the planet gear is , and the number of teeth of the ring gear is .

[0047] In step S1, a simulation signal model is used to simulate the vibration signals when the planetary gear box actually fails, which is specifically composed of an amplitude frequency modulation signal simulating the gear meshing modulation signal, a Hanning window simulating the time-varying transmission path of the planetary gear train, and a Gaussian white noise signal with a signal-to-noise ratio of 10 dB; the simulation signal model is:

[0048]

[0049] wherein, represents the original vibration signal, represents the meshing amplitude frequency modulation signal, represents the Hanning window, and represents the Gaussian white noise signal with a signal-to-noise ratio of 10 dB.

[0050] The gear meshing amplitude frequency modulation signal is expressed as:

[0051]

[0052] wherein, >0, >0 respectively represent the amplitude value of the amplitude modulation and the frequency modulation; represents the fault characteristic frequency of the gear, t represents time, represents the meshing frequency of the gear, , and are the initial phase of the amplitude modulation, the phase offset of the carrier, and the phase offset of the carrier, respectively.

[0053] The Hanning window signal is expressed as:

[0054] ​

[0055] wherein, denotes the length of a single window function, denotes the time domain position of the window function.

[0056] The time domain graph of the simulated vibration signal in the planetary gear train fault diagnosis method is shown in Figure 2 The frequency spectrum graph of the simulated vibration signal in the planetary gear train fault diagnosis method is shown in Figure 3

[0057] In step S1, the original vibration signal needs to be resampled by angle resampling, and the resampled vibration signal is obtained. In this example, the tooth engagement refers to only considering the engagement process of a single planetary gear and the sun gear in the planetary gear train. When resampling, the resampling frequency is set to the ratio of the number of sampling points to the time used for one revolution of the planet carrier, so as to ensure that the amount of data collected by the sensor is consistent for each revolution of the planet carrier.

[0058] S2, calculate the reset period of the planetary gear box according to the engagement period characteristics of the planetary gear train. The reset period calculation formula of the planetary gear train is as follows:

[0059]

[0060] wherein, LCM represents the least common multiple, denotes the number of gear teeth, denotes the number of teeth of the fault planetary gear or sun gear.

[0061] that is, the reset period of the planetary gear box is. Wherein, represents the rotation period of a planet carrier.

[0062] Then the resampled signal is segmented according to the reset period to obtain a plurality of reset period signal segments.

[0063] S3, calculate the sparse feature change trend of the separated signal segment using the GI index, and extract the signal segment with the maximum GI index as the fault signal segment. The GI index feature change rule graph of the time domain vibration signal of different reset periods in the planetary gear train fault diagnosis method is shown in Figure 4

[0064] The selection method of the fault signal segment is as follows:

[0065] S31, segment the separated different reset period signal segments by time-varying sliding window.

[0066] S32, calculate the sparse feature change trend of different signal segments by GI index;

[0067] wherein, the calculation formula of the GI index is as follows: ​​

[0068]

[0069] wherein, is arranged in ascending order, ; denotes the norm of N denotes the total length of the signal x denotes the index position of the current summation. n

[0070] S33, the signal segment with the largest sparse feature is selected as the fault signal segment for separation, and a fault signal segment matrix is constructed.

[0071] S4, the MSB is applied to the fault signal segment matrix to suppress the participating noise and demodulation coupling frequency in the signal and extract the fault feature.

[0072] Step S4 is realized by the following formula:

[0073]

[0074] wherein, is the set average value of the MSB matrix obtained from the multi-segment modulation signal; is the complex conjugate of is the carrier frequency; is the modulation frequency; is the upper and lower sideband frequency; is the modulation signal bispectrum of the signal. The MSB graph of the same region with the maximum average GI index in different reset periods in the planetary gear train fault diagnosis method is shown in

[0075] The MSB graph of the same region with the minimum average GI index in different reset periods in the planetary gear train fault diagnosis method is shown in Figure 5 . Figure 6

[0076] Although the embodiments of the present application have been shown and described above, it should be understood that the above-described embodiments are exemplary and should not be construed as limiting the present application. Those skilled in the art can make changes, modifications, replacements and variations to the above-described embodiments within the scope of the present application.​​

Claims

1. An adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics, characterized in that: The steps include: S1. Obtaining an original vibration signal and a speed pulse signal during the operation of the planetary gearbox, resampling the original vibration signal through angle resampling, and obtaining a resampled vibration signal; in step S1, when resampling, the resampling frequency is set to the number of points per rotation of the planet carrier; In step S1, a simulation signal model is used to simulate the vibration signal of the planetary gearbox when a fault actually occurs. Specifically, the gear meshing modulation signal is simulated by an amplitude-frequency modulation signal, the time-varying transmission path of the planetary gear train vibration is simulated by a Hanning window, and a Gaussian white noise signal with a signal-to-noise ratio of 10 dB is added together. The simulation signal model is: ； in, represents the original vibration signal, Indicates the meshing AM / FM signal, represents the Hanning window, represents a Gaussian white noise signal with a signal-to-noise ratio of 10dB; S2. Calculating the reset period of the planetary gearbox according to the meshing period characteristics of the planetary gear train, segmenting the resampled signal according to the reset period, and obtaining a plurality of reset period signal segments; S3. Calculate the sparse feature change trend of the separated segment signals using the GI index, and extract the signal segment with the largest GI index as the fault signal segment; In step S3, the fault signal segment is selected as follows: S31, segmenting the separated signal segments with different reset periods through a time-varying sliding window; S32. Calculate the sparse feature change trend of different signal segments using the GI index. The calculation formula of the GI index in step S32 is as follows: ； in, Yes Sort in ascending order, ; express of norm; N represents the total length of the signal x, and n represents the index position of the current summation; S33, selecting the signal segment with the largest sparse feature as the fault signal segment for separation, and constructing a fault signal segment matrix; S4. Apply MSB to the fault signal fragment matrix to suppress the participating noise and demodulate the coupled frequency in the signal and extract the fault features.

2. The adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics according to claim 1 is characterized in that: The calculation formula for the reset period of the planetary gear train in step S2 is as follows: ； Among them, LCM represents the least common multiple, Indicates the number of teeth on the ring gear. Indicates the number of teeth on the faulty planetary gear or sun gear.

3. The adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics according to claim 1, characterized in that: Step S4 is implemented by the following formula: ； in, To obtain the ensemble average of the MSB matrix from multiple modulation signals; for The complex conjugate of is the carrier frequency; is the modulation frequency; are the upper and lower sideband frequencies; is the bispectrum of the modulation signal.

4. The adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics according to claim 1, characterized in that: In step S1, the original vibration signal is obtained by the acceleration sensor installed on the planetary gearbox housing, and the speed pulse signal is obtained by the photoelectric sensor on the encoder installed on the shaft.

5. The adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics according to claim 1, characterized in that: The gear meshing amplitude and frequency modulation signal is expressed as: ； in, >0, >0 indicates the amplitude of amplitude modulation and frequency modulation respectively; represents the fault characteristic frequency of the gear, t represents time, represents the meshing frequency of the gears, 、 and They are the initial phase of amplitude modulation, frequency modulation and the phase offset of the carrier respectively.

6. The adaptive MSB planetary gear train fault diagnosis method based on meshing period characteristics according to claim 5, characterized in that: The Hanning window signal is expressed as: ； in, Represents the length of a single window function, Represents the time domain position of the window function.

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