CN116202770A - Bearing fault diagnosis simulation experiment device - Google Patents
Bearing fault diagnosis simulation experiment device Download PDFInfo
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- G01M13/00—Testing of machine parts
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Abstract
The application discloses bearing fault diagnosis simulation experiment device includes: the device comprises a bearing system module, a signal acquisition module, a signal processing module, a fault detection module and a control module; the bearing system module is used for realizing bearing rotation; the acquisition signal module is connected with the bearing system module and is used for acquiring bearing rotation signals; the signal processing module is connected with the signal acquisition module and is used for carrying out noise reduction processing on the acquired bearing rotation signals and extracting characteristic signals; the fault detection module is connected with the signal processing module and is used for detecting the signals processed by the signal processing module. According to the method, the bearing rotation signal is subjected to noise reduction treatment of the wavelet function, so that the characteristic extraction of the signal is realized, the signal is more accurate, meanwhile, the fault cause is judged through an empirical mode decomposition method, and the method has timeliness and accuracy.
Description
Technical Field
The application belongs to the technical field of equipment manufacturing diagnosis, and particularly relates to a bearing fault diagnosis simulation experiment device.
Background
With the progress of science and technology and the development of productivity, mechanical equipment and production systems are increasingly developed towards the directions of maximization, precision, high speed and automation, so that on one hand, the production efficiency is improved, the production cost is reduced, and on the other hand, higher and stricter requirements are put on the design, manufacture, installation, use, maintenance and reliable operation of the machinery. A minor failure may affect the stability and safety of the overall system operation and even have catastrophic consequences. Bearings are the most widely used common mechanical parts in rotary machines. Due to the severe operating environment and the increased complexity of the mechanical structure, faults are easily caused by damage, performance failure is caused, and serious economic loss is caused. Therefore, the abnormal running of the bearing is found in time, the fault part is accurately diagnosed, and the method has important significance for good running of equipment and avoiding accidents.
Bearings are the most common components in mechanical devices, and their operating state directly affects the function of the whole machine. The same symptom domain in bearing fault diagnosis is difficult to distinguish multiple faults, and a single sensor has uncertainty in fault classification identification. In general, a fault source of a system may have a plurality of fault characterizations, and to perform accurate and reliable fault diagnosis, it is necessary to have enough fault characterizations, and only if the mapping relationship between the fault characterizations and the fault source is clear, the fault source can be found by the fault characterizations.
Disclosure of Invention
The application provides a bearing fault diagnosis simulation experiment device, through carrying out the noise reduction processing of wavelet function to the bearing rotation signal, realize the characteristic extraction of signal for the signal is more accurate, judges the fault cause through the method of empirical mode decomposition simultaneously, has timeliness and accuracy more.
To achieve the above object, the present application provides the following solutions:
a bearing fault diagnosis simulation experiment device, comprising: the device comprises a bearing system module, a signal acquisition module, a signal processing module, a fault detection module and a control module;
the bearing system module is used for realizing bearing rotation;
the acquisition signal module is connected with the bearing system module and is used for acquiring bearing rotation signals;
the signal processing module is connected with the signal acquisition module and is used for carrying out noise reduction processing on the acquired bearing rotation signals and extracting characteristic signals;
the fault detection module is connected with the signal processing module and is used for detecting the signals processed by the signal processing module;
the control module is connected with the bearing system module, the signal acquisition module, the signal processing module and the fault detection module.
Preferably, the bearing system module includes: the inner ring, the outer ring, the rolling bodies and the retainer;
the inner ring is connected with the shaft and rotates together with the shaft;
the outer ring is connected with the bearing seat and used for supporting;
the rolling bodies are connected with the inner ring and the outer ring;
the cage evenly separates the rolling bodies for improved bearing internal load distribution.
Preferably, the signal acquisition module includes: the device comprises an acquisition unit and an A/D conversion unit;
the acquisition unit acquires bearing rotation signals by using a sensor;
the A/D conversion unit is used for converting the signals acquired by the acquisition unit.
Preferably, the method for noise reduction processing of the collected bearing rotation signal by the signal processing module includes:
estimating a noise variance and calculating a threshold based on the bearing rotation signal;
a wavelet function is selected to decompose the bearing rotation signal, and a wavelet decomposition coefficient is calculated;
processing the wavelet decomposition coefficient to obtain a new wavelet coefficient;
and reconstructing the wavelet coefficient to obtain a denoised signal.
Preferably, the method of calculating the threshold λ includes:
the formula is:
where σ is the noise variance, j is the decomposition scale, and N is the signal length.
Preferably, the method for calculating the wavelet decomposition coefficients includes:
wherein m and n are adjustment coefficients, and n is a positive integer.
Preferably, the method for detecting the signal by the fault detection module comprises the following steps: an empirical mode decomposition method is adopted.
Preferably, the empirical mode decomposition method includes:
decomposing the data sequence;
obtaining a modal function component based on the data sequence;
and repeatedly screening the modal function components, and judging the fault signal.
The beneficial effects of this application are:
the utility model discloses a bearing fault diagnosis simulation experiment device, through carrying out the noise reduction processing of wavelet function to the bearing rotation signal, realize the characteristic extraction of signal for the signal is more accurate, judges the fault cause through empirical mode decomposition's method simultaneously, self-adaptation is the nonlinear, nonstationary signal is according to the frequency from high to low decomposition into the sum of finite IMF, carries out Hilbert spectral analysis to each IMF again, has more timeliness and accuracy, has improved signal to noise ratio greatly, has also improved the precision and the speed of decomposition simultaneously, and the fault characteristic of extracting is more obvious, can discern antifriction bearing's trouble type more effectively.
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For a clearer description of the technical solutions of the present application, the drawings that are required to be used in the embodiments are briefly described below, it being evident that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic structural diagram of a bearing fault diagnosis simulation experiment device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
In order that the above-recited objects, features and advantages of the present application will become more readily apparent, a more particular description of the invention briefly described above will be rendered by reference to specific embodiments that are illustrated in the appended drawings.
Referring to fig. 1, a schematic structural diagram of a bearing fault diagnosis simulation experiment device according to an embodiment of the present application includes: the device comprises a bearing system module, a signal acquisition module, a signal processing module, a fault detection module and a control module; the control module is connected with the bearing system module, the signal acquisition module, the signal processing module and the fault detection module.
The bearing system module is used for realizing bearing rotation; the bearing system module includes: the inner ring, the outer ring, the rolling bodies and the retainer; the inner ring is connected with the shaft and rotates together with the shaft; the outer ring is connected with the bearing seat and used for supporting; the rolling bodies are connected with the inner ring and the outer ring; the cage evenly spaces the rolling elements for improved bearing internal load distribution.
The signal acquisition module is connected with the bearing system module and is used for acquiring bearing rotation signals; the rotation signal of bearing system module sends to gathering signal module, gathers signal module and includes: the device comprises an acquisition unit and an A/D conversion unit; the acquisition unit acquires bearing rotation signals by using a sensor; the A/D conversion unit is used for converting the signals acquired by the acquisition unit. In this embodiment, the a/D conversion unit adopts ADS1256, collects a bearing rotation signal through a sensor ADXL001, transmits the signal to ADS1256 to convert the analog signal into a digital signal, and sends the digital signal to the signal processing module under the action of the control module.
The signal processing module is connected with the signal acquisition module and is used for carrying out noise reduction processing on the acquired bearing rotation signals and extracting characteristic signals; and after the signal processing module receives the signal, noise reduction processing is carried out. In this embodiment, a wavelet function method is used to perform denoising, where the method includes:
estimating a noise variance and calculating a threshold based on the bearing rotation signal; the method for calculating the threshold lambda comprises the following steps:
in equation 1, σ is the noise variance, j is the decomposition scale, and N is the signal length.
A wavelet function is selected to decompose the bearing rotation signal, and a wavelet decomposition coefficient is calculated; the method for calculating the wavelet decomposition coefficients comprises the following steps:
in the formula 2, m and n are adjustment coefficients, and n is a positive integer.
Processing the wavelet decomposition coefficient to obtain a new wavelet coefficient; when |w j，k When the I is more than or equal to lambda,
there is->
With w j，k Is increased and is compromised>
And->
Then it decreases. The signal-to-noise ratio of the signal is determined by the adjustment coefficient m, and the larger the value of m is, the more serious the distortion of the signal is, and the smaller the oscillation is; the smaller the value of m, the less the distortion of the signal and the greater the oscillation. The values of m and n can be dynamically adjusted to regulate and control the denoising result.
And reconstructing the wavelet coefficient, and performing three-layer wavelet packet decomposition by adopting coif3 wavelet to obtain the wavelet packet decomposition coefficients in 8 frequency bands of the third-layer decomposition signal. And reconstructing wavelet packet decomposition coefficients of all nodes of the third layer to obtain a denoised signal.
The fault detection module is connected with the signal processing module and is used for detecting the signals processed by the signal processing module; the signal processing module sends the denoising signal to the fault detection module, and the fault detection module adopts an empirical mode decomposition method for detection. The method adaptively decomposes complex data into high and low frequency natural mode function components (IMFs) of limited instantaneous frequencies that are either amplitude or frequency modulated. The IMFs are subjected to Hilbert transformation to obtain a time-frequency spectrogram of the signal, so that the original characteristics of the signal can be accurately reflected. The empirical mode decomposition method comprises the following steps:
decomposing the data sequence; a signal is smoothed, resulting in a series of data sequences with different feature scales, each sequence being referred to as an IMF. Finding out all maximum value points and minimum value points of the data sequence x (t), respectively fitting by using a cubic spline interpolation function to form upper and lower envelopes of the original data, and marking the average value of the upper and lower envelopes as m 1 Subtracting the average envelope m from the original data sequence x (t) 1 Obtaining a new data sequence h 1 ：
h 1 ＝x(t)-m 1 ； (3)
Judging h 1 Whether the IMF condition is satisfied, if not, h is needed to be added 1 Repeating the steps to obtain h as original data 1 Is m 11 H is then 11 ＝h 1 -m 11 ；
Obtaining a modal function component based on the data sequence; repeating the above steps for k times until h of the kth time 1k Is an intrinsic mode function, i.e. h 1(k-1) -m 1k ＝h 1k The method comprises the steps of carrying out a first treatment on the surface of the Thus, the first IMF is extracted from the original data and denoted as c 1 ＝hk 1 ；
c 1 Subtracting c from the original data x (t) representing the highest frequency component in the original signal 1 Then a new data sequence r with high frequency removed is obtained 1 ：
r 1 ＝x(t)-c 1 The method comprises the steps of carrying out a first treatment on the surface of the Repeating the above steps to obtain
When r is n When IMFs can no longer be decomposed for a monotonic function, the decomposition is stopped.
And repeatedly screening the modal function components, and judging the fault signal. The screening principle is adopted as follows:
the Hilbert transform is performed on each of the natural mode functions to obtain an instantaneous magnitude and an instantaneous phase:
wherein H [ c ] j (t)]Is c j (t) Hilbert transformed data sequence.
The further obtained instantaneous frequency is:
the amplitude and frequency obtained by Hilbert transform are both functions of time. The time-frequency distribution representation of the magnitudes is called the Hilbert magnitude spectrum, or Hilbert spectrum for short, and is written as:
the empirical mode decomposition method adaptively decomposes nonlinear and non-stationary signals into the sum of a limited number of IMFs from high frequency to low frequency, and performs Hilbert spectrum analysis on each IMF so as to judge faults.
The foregoing embodiments are merely illustrative of the preferred embodiments of the present application and are not intended to limit the scope of the present application, and various modifications and improvements made by those skilled in the art to the technical solutions of the present application should fall within the protection scope defined by the claims of the present application.
Claims (8)
1. The bearing fault diagnosis simulation experiment device is characterized by comprising: the device comprises a bearing system module, a signal acquisition module, a signal processing module, a fault detection module and a control module;
the bearing system module is used for realizing bearing rotation;
the acquisition signal module is connected with the bearing system module and is used for acquiring bearing rotation signals;
the signal processing module is connected with the signal acquisition module and is used for carrying out noise reduction processing on the acquired bearing rotation signals and extracting characteristic signals;
the fault detection module is connected with the signal processing module and is used for detecting the signals processed by the signal processing module;
the control module is connected with the bearing system module, the signal acquisition module, the signal processing module and the fault detection module.
2. The bearing fault diagnosis simulation experiment apparatus of claim 1, wherein the bearing system module comprises: the inner ring, the outer ring, the rolling bodies and the retainer;
the inner ring is connected with the shaft and rotates together with the shaft;
the outer ring is connected with the bearing seat and used for supporting;
the rolling bodies are connected with the inner ring and the outer ring;
the cage evenly separates the rolling bodies for improved bearing internal load distribution.
3. The bearing fault diagnosis simulation experiment apparatus of claim 1, wherein the signal acquisition module comprises: the device comprises an acquisition unit and an A/D conversion unit;
the acquisition unit acquires bearing rotation signals by using a sensor;
the A/D conversion unit is used for converting the signals acquired by the acquisition unit.
4. The bearing fault diagnosis simulation experiment apparatus according to claim 1, wherein the method for performing noise reduction processing on the collected bearing rotation signal by the signal processing module comprises:
estimating a noise variance and calculating a threshold based on the bearing rotation signal;
a wavelet function is selected to decompose the bearing rotation signal, and a wavelet decomposition coefficient is calculated;
processing the wavelet decomposition coefficient to obtain a new wavelet coefficient;
and reconstructing the wavelet coefficient to obtain a denoised signal.
5. The bearing fault diagnosis simulation experiment apparatus of claim 4, wherein the method of calculating the threshold λ comprises:
the formula is:
where σ is the noise variance, j is the decomposition scale, and N is the signal length.
6. The bearing fault diagnosis simulation experiment apparatus of claim 4, wherein the method of calculating the wavelet decomposition coefficients comprises:
wherein m and n are adjustment coefficients, and n is a positive integer.
7. The bearing fault diagnosis simulation experiment apparatus of claim 1, wherein the method for detecting the signal by the fault detection module comprises: an empirical mode decomposition method is adopted.
8. The bearing fault diagnosis simulation experiment apparatus of claim 7, wherein the empirical mode decomposition method comprises:
decomposing the data sequence;
obtaining a modal function component based on the data sequence;
and repeatedly screening the modal function components, and judging the fault signal.
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| CN110514441A (en) * | 2019-08-28 | 2019-11-29 | 湘潭大学 | A kind of Fault Diagnosis of Roller Bearings based on vibration signal denoising and Envelope Analysis |
| CN114112400A (en) * | 2021-12-01 | 2022-03-01 | 盐城工学院 | Mechanical bearing fault diagnosis method based on multi-angle information fusion |
| CN115456019A (en) * | 2022-09-07 | 2022-12-09 | 沈阳航空航天大学 | Rolling bearing fault diagnosis method based on wavelet packet transformation and CEEMDAN |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110514441A (en) * | 2019-08-28 | 2019-11-29 | 湘潭大学 | A kind of Fault Diagnosis of Roller Bearings based on vibration signal denoising and Envelope Analysis |
| CN114112400A (en) * | 2021-12-01 | 2022-03-01 | 盐城工学院 | Mechanical bearing fault diagnosis method based on multi-angle information fusion |
| CN115456019A (en) * | 2022-09-07 | 2022-12-09 | 沈阳航空航天大学 | Rolling bearing fault diagnosis method based on wavelet packet transformation and CEEMDAN |
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