CN110174281B - Electromechanical equipment fault diagnosis method and system - Google Patents
Electromechanical equipment fault diagnosis method and system Download PDFInfo
- Publication number
- CN110174281B CN110174281B CN201910487476.5A CN201910487476A CN110174281B CN 110174281 B CN110174281 B CN 110174281B CN 201910487476 A CN201910487476 A CN 201910487476A CN 110174281 B CN110174281 B CN 110174281B
- Authority
- CN
- China
- Prior art keywords
- signal
- cepstrum
- wavelet packet
- vibration
- noise
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M99/00—Subject matter not provided for in other groups of this subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
- Measurement Of Mechanical Vibrations Or Ultrasonic Waves (AREA)
Abstract
The invention discloses a fault diagnosis method and a fault diagnosis system for electromechanical equipment, wherein a vibration speed sensor and a vibration acceleration sensor are arranged at different measuring points in the vertical direction of a bearing of the electromechanical equipment, and are used for acquiring vibration signals, and the method comprises the following steps: the electromechanical device sends the vibration signal to a cloud; the cloud end carries out median filtering and wavelet packet noise reduction processing on the vibration signal to obtain a noise-reduced signal; the cloud end carries out cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal; and the cloud end carries out fault diagnosis according to the cepstrum signal. The method and the system for diagnosing the fault of the electromechanical equipment are used for solving the technical problem that the existing fault diagnosis method cannot detect the unbalance and the misalignment of the electromechanical equipment, and improve the accuracy of fault diagnosis.
Description
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a fault diagnosis method and system for electromechanical equipment.
Background
In modern devices of large and medium enterprises such as steel, coal mines, petrifaction enterprises and the like, various large and complicated mechanical equipment such as reducing and sizing machines, motors, pumps, fans, air compressors, gear boxes and the like play a very important role. With the development of the technology and the continuous modernization of equipment management, the analysis speed and performance of the instrument fault are greatly improved from the initial analog analysis to the current digital analysis technology.
The oil analysis technology is a technology for determining the cause, type and position of a fault by analyzing the performance change of a lubricant or a working medium used by the technology and the condition of carried wear particles so as to obtain the information of the lubrication and wear states of equipment, evaluate the working condition of the equipment and predict the fault. However, the oil analysis technology can only diagnose the problems of lubricating oil analysis, gear meshing and bearing faults by analyzing the wear of particles, and cannot diagnose the problems of unbalance and misalignment of electromechanical equipment.
The temperature diagnosis is to judge whether the electromechanical equipment has faults or not by monitoring the temperature of key parts of the equipment. However, the temperature diagnosis can only detect the problems of bearing faults and lubricating oil analysis, and cannot detect the problems of unbalance of electromechanical equipment, non-centering and gear meshing.
Disclosure of Invention
The invention provides a fault diagnosis method and a fault diagnosis system for electromechanical equipment, which are used for solving the technical problem that the existing fault diagnosis method cannot detect unbalance and misalignment of the electromechanical equipment and improving the accuracy of fault diagnosis.
In a first aspect, an embodiment of the present invention provides a method for diagnosing a fault of an electromechanical device, where a vibration speed sensor and a vibration acceleration sensor are installed at different measurement points in a vertical direction of a bearing of the electromechanical device, and the vibration speed sensor and the vibration acceleration sensor are used to acquire a vibration signal, where the method includes:
the electromechanical device sends the vibration signal to a cloud;
the cloud end carries out median filtering and wavelet packet noise reduction processing on the vibration signal to obtain a noise-reduced signal;
the cloud end carries out cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal;
and the cloud end carries out fault diagnosis according to the cepstrum signal.
Preferably, the cloud end performs median filtering on the vibration signal, specifically: and acquiring the sampling frequency of the vibration signal, calculating the window width of a median filter according to the sampling frequency, and filtering according to the window width.
Preferably, the cloud end performs median filtering and wavelet packet denoising on the vibration signal, specifically: and carrying out wavelet packet decomposition on the signals subjected to median filtering, determining a wavelet packet coefficient and an optimal wavelet packet basis, processing the wavelet packet coefficient according to a preset threshold value to obtain a new wavelet packet coefficient, and carrying out signal reconstruction according to the new wavelet packet coefficient and the optimal wavelet packet basis to obtain denoised signals.
Preferably, the cloud performs cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal, specifically: and carrying out Fourier transform on the noise-reduced signal to obtain a frequency spectrum function, and carrying out inverse Fourier transform on a logarithmic value of the frequency spectrum function to obtain a cepstrum signal.
Preferably, the method further comprises the steps of: and the cloud predicts the operation trend of the electromechanical equipment according to the cepstrum signal, wherein the operation trend comprises a loss level and a residual life.
In a second aspect, an embodiment of the present invention provides an electromechanical device fault diagnosis system, including an electromechanical device and a cloud, where a vibration speed sensor and a vibration acceleration sensor are installed at different measurement points in a vertical direction of a bearing of the electromechanical device, and the vibration speed sensor and the vibration acceleration sensor are used to acquire a vibration signal, and the system includes:
the electromechanical device is used for sending the vibration signal to a cloud end;
the cloud end is used for carrying out median filtering and wavelet packet noise reduction processing on the vibration signal to obtain a noise-reduced signal;
the cloud end is used for performing cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal;
and the cloud end is used for carrying out fault diagnosis according to the cepstrum signal.
Preferably, the cloud is configured to perform median filtering on the vibration signal, specifically: and acquiring the sampling frequency of the vibration signal, calculating the window width of a median filter according to the sampling frequency, and filtering according to the window width.
Preferably, the cloud is configured to perform median filtering and wavelet packet denoising on the vibration signal, specifically: and carrying out wavelet packet decomposition on the signals subjected to median filtering, determining a wavelet packet coefficient and an optimal wavelet packet basis, processing the wavelet packet coefficient according to a preset threshold value to obtain a new wavelet packet coefficient, and carrying out signal reconstruction according to the new wavelet packet coefficient and the optimal wavelet packet basis to obtain denoised signals.
Preferably, the cloud is configured to perform cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal, and specifically: and carrying out Fourier transform on the noise-reduced signal to obtain a frequency spectrum function, and carrying out inverse Fourier transform on a logarithmic value of the frequency spectrum function to obtain a cepstrum signal.
Preferably, the cloud is further configured to predict an operation trend of the electromechanical device according to the cepstrum signal, where the operation trend includes a loss level and a remaining life.
By adopting the technical scheme, the combination of median filtering and wavelet packet noise reduction is adopted, so that the interference of mixed complex noise on vibration signals is effectively eliminated, the detailed characteristics of fault signals are kept, sideband signals can be separated through cepstrum analysis, periodic components which are difficult to distinguish in a power spectrum are changed into discrete line spectrums in the cepstrum signals, the advantage of easily identifying frequency change is achieved, faults such as unbalance, misalignment, bearing looseness, gear meshing and the like can be well identified, and the accuracy of fault diagnosis is improved.
Drawings
FIG. 1 is a schematic diagram of a sensor mounting structure of an electromechanical device according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for diagnosing a fault of an electromechanical device according to an embodiment of the present invention;
FIG. 3 is a cepstrum signal diagram of an imbalance fault for an electromechanical device according to an embodiment of the present invention;
FIG. 4 is a cepstrum signal diagram of a fault in misalignment of an electromechanical device according to an embodiment of the present invention;
FIG. 5 is a cepstrum signal diagram of a bearing loosening fault for an electromechanical device provided in accordance with an embodiment of the present invention;
FIG. 6 is a cepstrum signal diagram of a gear mesh failure for an electromechanical device provided in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating a location of a bearing failure of an electromechanical device provided by an embodiment of the present invention;
fig. 8 is a block diagram of a fault diagnosis system for an electromechanical device according to an embodiment of the present invention.
Detailed Description
The following further describes embodiments of the present invention with reference to the drawings. It should be noted that the description of the embodiments is provided to help understanding of the present invention, but the present invention is not limited thereto. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
In the description of the embodiments herein, "/" means "or" unless otherwise specified, for example, a/B may mean a or B; "and/or" herein is merely an association describing an associated object, and means that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present application, "a plurality" means two or more than two.
In the following, the terms "first", "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of the present application, "a plurality" means two or more unless otherwise specified.
The electromechanical device fault diagnosis method is applied to an electromechanical device fault diagnosis system, and the electromechanical device fault diagnosis system comprises electromechanical devices and a cloud. Vibration speed sensors and vibration acceleration sensors are arranged at different measuring points in the vertical direction of a bearing of the electromechanical device, as shown in figure 1, wherein 1 and 3 in the figure are bearing supports; 2 is a main motor; 4 is a coupling; 5 is a gear box; 6. 8, 12, 14, 16 and 18 are vibration acceleration sensors; 7. 9, 11, 13, 15, 17 and 19 are communication controllers; 10 is a vibration speed sensor; 20. 21 is a gear; 22. 23, 24, 25 are bearings.
The method comprises the steps that when a vibration sensor is installed, the vibration sensor is close to a bearing area of a bearing as much as possible, rigid connection is adopted between the bearing and the sensor, the vibration sensor and a communication controller are installed in the vertical direction of the bearing at the driving end of a motor, a vibration speed sensor for measuring speed and a vibration acceleration sensor for measuring acceleration are installed according to signals needing to be detected at different positions, and vibration speed and acceleration information, namely vibration signals, of electromechanical equipment at different positions are obtained through the vibration speed sensor and the vibration acceleration sensor.
And transmitting the vibration signals acquired by the vibration sensor to a communication controller, and uploading data through the communication controller. The communication controller can be a wireless module, such as a wifi module or a narrowband internet of things module.
In a first aspect, as shown in fig. 2, an embodiment of the present invention provides a method for diagnosing a fault of an electromechanical device, where a vibration speed sensor and a vibration acceleration sensor are installed at different measurement points in a vertical direction of a bearing of the electromechanical device, and the vibration speed sensor and the vibration acceleration sensor are used to acquire a vibration signal, the method including:
step S201, the electromechanical device sends a vibration signal to a cloud;
specifically, the vibration signal is sent to the cloud end through the upper communication controller.
The cloud can be a server or an upper computer.
Step S202, the cloud carries out median filtering and wavelet packet noise reduction processing on the vibration signal to obtain a noise-reduced signal;
step S203, the cloud performs cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal;
and S204, the cloud end carries out fault diagnosis according to the cepstrum signal.
Because the frequency component in the cepstrum signal is different, the fault can be positioned according to the difference of the frequency component.
The cloud can display the fault after the fault is diagnosed, so that the user can watch the fault conveniently.
By adopting the technical scheme, the combination of median filtering and wavelet packet noise reduction is adopted, so that the interference of mixed complex noise on vibration signals is effectively eliminated, the detailed characteristics of fault signals are kept, sideband signals can be separated through cepstrum analysis, periodic components which are difficult to distinguish in a power spectrum are changed into discrete line spectrums in the cepstrum signals, the advantage of easily identifying frequency change is achieved, faults such as unbalance, misalignment, bearing looseness, gear meshing and the like can be well identified, and the accuracy of fault diagnosis is improved.
Preferably, in step S202: the cloud carries out median filtering on the vibration signals, and specifically comprises the following steps: and acquiring the sampling frequency of the vibration signal, calculating the window width of a median filter according to the sampling frequency, and filtering according to the window width.
The method comprises the following specific steps:
the sampling frequency Fs of the uploaded signals is obtained, and the window width Ld of the median filter is calculated according to the formula Ld-2 LsFs.
After obtaining the window width Ld, median filtering is performed on the noisy signal according to the equation y (n) Med [ x (n-d),.. times.x (n),. times.x (n + d) ]. Let the discrete sampling sequence of the signal x (t) be x (n), and Med [ ] denote the median of all numbers in the window.
Preferably, in step S202: the cloud carries out median filtering and wavelet packet noise reduction processing on the vibration signals, and specifically comprises the following steps: and carrying out wavelet packet decomposition on the signals subjected to median filtering, determining a wavelet packet coefficient and an optimal wavelet packet basis, processing the wavelet packet coefficient according to a preset threshold value to obtain a new wavelet packet coefficient, and carrying out signal reconstruction according to the new wavelet packet coefficient and the optimal wavelet packet basis to obtain the signals subjected to noise reduction.
The method comprises the following specific steps:
(1) performing wavelet packet decomposition on the signal after median filtering in the formula
And the expressed cost function M (x) is used as a judgment basis for judging whether the decomposition is continued or not, the optimal decomposition scale and the optimal wavelet packet basis are determined, and the wavelet packet coefficient is obtained. In the formula, j represents the decomposition scale.
(2) Using a predetermined threshold Tj,kSum threshold function expression processing each wavelet packet coefficient Wj,kTo obtain estimated new coefficients
Wherein
σj,kIs the standard deviation of noise, Wj,kIs the wavelet packet coefficient of the kth sub-band of the jth layer, and N is the signal length.
(3) By new coefficients thresholded at each scale
And reconstructing the signal by the optimal wavelet packet basis to obtain the signal after noise elimination.
Preferably, in step S203: the high in the clouds carries out cepstrum analysis to the signal after making an uproar, obtains the cepstrum signal, specifically is: and performing Fourier transform on the noise-reduced signal to obtain a frequency spectrum function, and performing inverse Fourier transform on a logarithm value of the frequency spectrum function to obtain a cepstrum signal.
The method specifically comprises the following steps: firstly, Fourier transform is carried out on the obtained time domain signals to convert the time domain signals into frequency spectrum functions, absolute values and logarithms of the obtained signals are taken, phase expansion is carried out on the logarithm values, then inverse Fourier transform is carried out on the logarithm values, and finally cepstrum is obtained.
C＝IFT(log(|FT(x(t))|)+j2πm)
Where m is a real number, x (t) is the received time domain signal, and c is the resulting cepstrum.
Preferably, the method further comprises the steps of: and the cloud end predicts the operation trend of the electromechanical equipment according to the cepstrum signal, wherein the operation trend comprises a loss level and a residual life. Thereby providing reference for related technicians and helping to make a maintenance and repair plan of the equipment.
Step S204, the cloud carries out fault diagnosis according to the cepstrum signal, and the diagnosis specifically comprises the following steps:
different faults may result in corresponding amplitudes at different frequencies.
If a significant fluctuation is detected in the frequency spectrum diagram at the frequency doubled by the motor speed, the problem of equipment failure is imbalance, as shown in fig. 3.
Secondly, if obvious fluctuation is detected on the first frequency multiplication and the second frequency multiplication of the motor rotation speed on the spectrogram, the fault problem of the equipment is misalignment, as shown in fig. 4.
Thirdly, if the vibration peak value is detected on the frequency spectrum diagram on the multiple frequency of the motor rotation frequency, the problem that the bearing is loosened due to the fault of the equipment is shown in fig. 5.
If a vibration peak value appears near the meshing frequency in the meshing vibration waveform of the motor on the spectrogram, and the vibration peak value is accompanied by multiple frequency conversion sidebands, the fact that the fault of the equipment is a missing tooth or meshing problem is shown in fig. 6.
For bearing fault, if fault signal frequency is found on frequency spectrum, comparing received fault signal frequency with preset fault characteristic frequency to determine the fault position of bearing as shown in fig. 7. Wherein: 71 is an outer ring defect; 72 is inner ring defect; 73 is a rolling element defect; and 74 is a cage defect.
Specific failure frequency:
for various faults of the bearing, corresponding fault analysis frequencies exist.
Bearing retainer touch outer ring
Bearing retainer inner collision ring
Bearing outer ring failure
Bearing inner ring failure
Single fault of bearing rolling body
Double failure of bearing rolling body
Transmission gear fault and oil seal ring collision grinding f7＝fn
D0 is the middle diameter (rolling body pitch circle diameter) of the bearing, mm; d is the diameter of the rolling body, mm; z is the number of rolling bodies; fn is the rotational frequency of the rotating shaft where the diagnostic component is located, and for a complex transmission system, the rotational frequency of the shaft where the bearing, the gear and the tread are located.
The invention can detect the problems of unbalance, misalignment, lubricating oil analysis and gear meshing faults of electromechanical equipment through different frequency analysis. And the specific bearing fault position can be determined through the cepstrum signal, and the diagnosis result is very accurate. Compared with a temperature diagnosis technology and an oil analysis technology, the method can comprehensively detect the invisible faults of the equipment. Compared with the use of a handheld vibration meter, the intelligent industrial equipment has the advantages that the consumption of manpower and material resources and the workload of regular on-site inspection are reduced by utilizing the vibration sensor and the communication controller. According to different fault conditions, remote fault diagnosis is carried out by using cloud computing, and the health condition of a complex system is monitored, so that fault repair is more timely, and unnecessary loss is reduced.
In a second aspect, as shown in fig. 8, an embodiment of the present invention provides an electromechanical device fault diagnosis system, including an electromechanical device 81 and a cloud 82, where a vibration speed sensor and a vibration acceleration sensor are installed at different measurement points in a vertical direction of a bearing of the electromechanical device, and the vibration speed sensor and the vibration acceleration sensor are used to acquire a vibration signal, and the system includes:
the electromechanical device 81 is used for sending a vibration signal to the cloud 82;
the cloud 82 is used for performing median filtering and wavelet packet noise reduction processing on the vibration signal to obtain a noise-reduced signal;
the cloud 82 is configured to perform cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal;
and the cloud end 82 is used for carrying out fault diagnosis according to the cepstrum signal.
Preferably, the cloud is configured to perform median filtering on the vibration signal, specifically: and acquiring the sampling frequency of the vibration signal, calculating the window width of the median filter according to the sampling frequency, and filtering according to the window width.
Preferably, the cloud is used for performing median filtering and wavelet packet denoising processing on the vibration signal, and specifically comprises: and performing wavelet packet decomposition on the signals subjected to median filtering, determining wavelet packet coefficients and an optimal wavelet packet basis, processing the wavelet packet coefficients according to a preset threshold value to obtain new wavelet packet coefficients, and performing signal reconstruction according to the new wavelet packet coefficients and the optimal wavelet packet basis to obtain denoised signals.
Preferably, the cloud is configured to perform cepstrum analysis on the noise-reduced signal, and the obtained cepstrum signal is specifically: and performing Fourier transform on the noise-reduced signal to obtain a frequency spectrum function, and performing inverse Fourier transform on a logarithm value of the frequency spectrum function to obtain a cepstrum signal.
Preferably, the cloud is further configured to predict an operation trend of the electromechanical device according to the cepstrum signal, where the operation trend includes a loss level and a remaining life.
The embodiments of the present invention have been described in detail with reference to the accompanying drawings, but the present invention is not limited to the described embodiments. It will be apparent to those skilled in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, and the scope of protection is still within the scope of the invention.
Claims (6)
1. A fault diagnosis method for electromechanical equipment is characterized in that vibration speed sensors and vibration acceleration sensors are mounted at different measuring points in the vertical direction of a bearing of the electromechanical equipment, and are used for acquiring vibration signals, and the method comprises the following steps:
the electromechanical device sends the vibration signal to a cloud;
the cloud end carries out median filtering and wavelet packet noise reduction processing on the vibration signal to obtain a noise-reduced signal;
the cloud end carries out cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal;
the cloud end carries out fault diagnosis according to the cepstrum signal;
the cloud end carries out median filtering and wavelet packet noise reduction processing on the vibration signal, and the method specifically comprises the following steps:
acquiring the sampling frequency Fs of the uploaded signals, and calculating the window width Ld of the median filter according to the formula Ld-2 LsFs;
after obtaining the window width Ld, median filtering the noisy signal according to the formula y (n) Med [ x (n-d),. once, x (n) + d) ], where x (t) is a discrete sampling sequence x (n), and Med [ ] represents the median of all numbers in the window;
performing wavelet packet decomposition on the signal after median filtering in the formula
The expressed cost function M (x) is used as a judgment basis for judging whether the decomposition is continued or not, the optimal decomposition scale and the optimal wavelet packet basis are determined, the wavelet packet coefficient is obtained, and j in the formula represents the decomposition scale;
using a predetermined threshold Tj,kSum threshold function expression processing each wavelet packet coefficient Wj,kTo obtain estimated new coefficients
Wherein
σj,kIs the standard deviation of noise, Wj,kThe wavelet packet coefficient of the kth sub-band of the jth layer is obtained, and N is the signal length;
by new coefficients thresholded at each scaleAnd reconstructing the signal by the optimal wavelet packet basis to obtain the signal after noise elimination.
2. The method for fault diagnosis of an electromechanical device according to claim 1, wherein the cloud performs cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal, and specifically comprises: and carrying out Fourier transform on the noise-reduced signal to obtain a frequency spectrum function, and carrying out inverse Fourier transform on a logarithmic value of the frequency spectrum function to obtain a cepstrum signal.
3. The electromechanical device fault diagnostic method according to claim 1, further comprising the steps of: and the cloud predicts the operation trend of the electromechanical equipment according to the cepstrum signal, wherein the operation trend comprises a loss level and a residual life.
4. The electromechanical device fault diagnosis system is characterized by comprising electromechanical devices and a cloud end, wherein vibration speed sensors and vibration acceleration sensors are installed at different measuring points in the vertical direction of bearings of the electromechanical devices, and are used for acquiring vibration signals, and the system comprises:
the electromechanical device is used for sending the vibration signal to a cloud end;
the cloud end is used for carrying out median filtering and wavelet packet noise reduction processing on the vibration signal to obtain a noise-reduced signal;
the cloud end is used for performing cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal;
the cloud end is used for carrying out fault diagnosis according to the cepstrum signal;
the cloud end carries out median filtering and wavelet packet noise reduction processing on the vibration signal, and the method specifically comprises the following steps:
acquiring the sampling frequency Fs of the uploaded signals, and calculating the window width Ld of the median filter according to the formula Ld-2 LsFs;
after obtaining the window width Ld, median filtering the noisy signal according to the formula y (n) Med [ x (n-d),. once, x (n) + d) ], where x (t) is a discrete sampling sequence x (n), and Med [ ] represents the median of all numbers in the window;
performing wavelet packet decomposition on the signal after median filtering in the formula
The expressed cost function M (x) is used as a judgment basis for judging whether the decomposition is continued or not, the optimal decomposition scale and the optimal wavelet packet basis are determined, the wavelet packet coefficient is obtained, and j in the formula represents the decomposition scale;
using a predetermined threshold Tj,kSum threshold function expression processing each wavelet packet coefficient Wj,kTo obtain estimated new coefficientsWherein
σj,kIs the standard deviation of noise, Wj,kThe wavelet packet coefficient of the kth sub-band of the jth layer is obtained, and N is the signal length;
by new coefficients thresholded at each scale
And an optimal waveletAnd (5) packing the basis and reconstructing the signal to obtain the signal after noise elimination.
5. The electromechanical device fault diagnosis system according to claim 4, wherein the cloud is configured to perform cepstrum analysis on the noise-reduced signal to obtain a cepstrum signal, and specifically: and carrying out Fourier transform on the noise-reduced signal to obtain a frequency spectrum function, and carrying out inverse Fourier transform on a logarithmic value of the frequency spectrum function to obtain a cepstrum signal.
6. The electromechanical device fault diagnosis system according to claim 4, wherein the cloud is further configured to predict an operational trend of the electromechanical device according to the cepstrum signal, the operational trend including a loss level and a remaining life.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910487476.5A CN110174281B (en) | 2019-06-05 | 2019-06-05 | Electromechanical equipment fault diagnosis method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910487476.5A CN110174281B (en) | 2019-06-05 | 2019-06-05 | Electromechanical equipment fault diagnosis method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110174281A CN110174281A (en) | 2019-08-27 |
| CN110174281B true CN110174281B (en) | 2021-08-13 |
Family
ID=67696973
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910487476.5A Active CN110174281B (en) | 2019-06-05 | 2019-06-05 | Electromechanical equipment fault diagnosis method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110174281B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110589021A (en) * | 2019-09-25 | 2019-12-20 | 东方航空技术有限公司 | Airplane anti-shake fault pre-judging system |
| CN110985425A (en) * | 2019-11-29 | 2020-04-10 | 联想(北京)有限公司 | Information detection method, electronic equipment and computer readable storage medium |
| CN112461356B (en) * | 2020-11-17 | 2023-06-23 | 南京同尔电子科技有限公司 | Test method for detecting abnormal oil seal in motor operation process through noise |
| CN117740370B (en) * | 2023-12-14 | 2024-08-30 | 广东派勒智能纳米科技股份有限公司 | Rotor fault diagnosis method, device, system and medium of nano sand mill |
| CN117874471B (en) * | 2024-03-11 | 2024-05-14 | 四川能投云电科技有限公司 | Water and electricity safety early warning and fault diagnosis method and system |
Family Cites Families (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6513004B1 (en) * | 1999-11-24 | 2003-01-28 | Matsushita Electric Industrial Co., Ltd. | Optimized local feature extraction for automatic speech recognition |
| CN105181019B (en) * | 2015-09-15 | 2018-12-18 | 安徽精科检测技术有限公司 | Rotate class machinery initial failure early warning analysis method |
| CN106855468A (en) * | 2015-12-09 | 2017-06-16 | 重庆市涪陵区德翔机电设备有限公司 | A kind of embedded electromechanical device state monitoring and fault diagnosis system |
| CN108550121A (en) * | 2018-03-30 | 2018-09-18 | 哈尔滨工程大学 | A kind of sediment sonar image processing method based on medium filtering and wavelet transformation |
| CN108613737A (en) * | 2018-05-14 | 2018-10-02 | 南京理工大学 | The discrimination method of aircraft multifrequency vibration signal based on wavelet packet and STFT |
| CN109029993A (en) * | 2018-06-20 | 2018-12-18 | 中国计量大学 | In conjunction with the bearing fault detection algorithm of genetic algorithm optimization parameter and machine vision |
| CN109708877B (en) * | 2018-12-27 | 2020-11-24 | 昆明理工大学 | Mechanical fault analysis method based on wavelet fuzzy recognition and image analysis theory |
| CN111272428B (en) * | 2020-02-17 | 2022-03-15 | 济南大学 | Rolling bearing fault diagnosis method based on improved Chebyshev distance |
-
2019
- 2019-06-05 CN CN201910487476.5A patent/CN110174281B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN110174281A (en) | 2019-08-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110174281B (en) | Electromechanical equipment fault diagnosis method and system | |
| Al-Balushi et al. | Gear fault diagnosis using energy-based features of acoustic emission signals | |
| Zhan et al. | Adaptive state detection of gearboxes under varying load conditions based on parametric modelling | |
| Yoon et al. | On the use of a single piezoelectric strain sensor for wind turbine planetary gearbox fault diagnosis | |
| EP3049788B1 (en) | Gear fault detection | |
| Feng et al. | A novel order spectrum-based Vold-Kalman filter bandwidth selection scheme for fault diagnosis of gearbox in offshore wind turbines | |
| Shanbr et al. | Detection of natural crack in wind turbine gearbox | |
| Li et al. | Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network | |
| CN109596349B (en) | Reducer fault diagnosis method based on VMD and PCT | |
| JP2003528292A (en) | State-based monitoring of bearings by vibration analysis | |
| Feng et al. | A novel cyclic-correntropy based indicator for gear wear monitoring | |
| CN110779724B (en) | Bearing fault diagnosis method based on frequency domain group sparse noise reduction | |
| CN109883691A (en) | The gear method for predicting residual useful life that kernel estimates and stochastic filtering integrate | |
| Singh et al. | A review of vibration analysis techniques for rotating machines | |
| CN110398362B (en) | Robot RV reducer fault diagnosis and positioning method | |
| Vogl et al. | A defect-driven diagnostic method for machine tool spindles | |
| CN114942139A (en) | Gear residual life prediction method considering bearing degradation influence in gear box | |
| CN111060317A (en) | Method for judging fault signal of rolling bearing of mining fan motor | |
| Pawlik | The use of the acoustic signal to diagnose machines operated under variable load | |
| Qu et al. | Gearbox Fault Diagnostics using AE Sensors with Low Sampling Rate. | |
| CN112154314B (en) | Signal acquisition module for rotating mechanism, monitoring system, aircraft and method for monitoring rotating mechanism | |
| Nacib et al. | A comparative study of various methods of gear faults diagnosis | |
| Zhan et al. | Adaptive autoregressive modeling of non-stationary vibration signals under distinct gear states. Part 2: experimental analysis | |
| Ghasemloonia et al. | Gear tooth failure detection by the resonance demodulation technique and the instantaneous power spectrum method–a comparative study | |
| CN109877647B (en) | Machine tool servo shaft performance degradation evaluation system based on built-in encoder |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |