CN111811819A - Bearing fault diagnosis method and device based on machine learning - Google Patents
Bearing fault diagnosis method and device based on machine learning Download PDFInfo
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Abstract
The invention relates to the technical field of fault diagnosis, in particular to a bearing fault diagnosis method and a bearing fault diagnosis device based on machine learning, wherein the method comprises the following steps: respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing device in a normal working state and a fault state, filtering the normal data signals and the fault data signals, and dividing the fault signals into a plurality of fault levels according to the difference value of the amplitude of the fault signals and the amplitude of the standard signals; decomposing the energy value of each frequency band of the fault signal through a wavelet packet to obtain parameter characteristics, dividing the parameter characteristics into a plurality of data sets according to the fault level, and taking the plurality of data sets as sample data; training the sample data by adopting a K mean value clustering algorithm to obtain a bearing fault diagnosis model; the invention obtains the fault result of the bearing equipment through the diagnosis of the bearing fault diagnosis model, and improves the diagnosis precision and the diagnosis efficiency of the bearing fault by removing the redundant characteristics in the original characteristics.
Description
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a bearing fault diagnosis method and device based on machine learning.
Background
Rolling bearings are an important component of rotary machines and are among the most prone to failure in rotary machines. The lubricating oil cooling device has the advantages of low manufacturing cost, convenience in lubricating and cooling, flexibility in operation, high use efficiency, convenience in maintenance and the like, and is widely applied to the mechanical industry. According to relevant statistics, the failure rate of about 30% is caused by the failure of the bearing, so whether the rolling bearing normally operates or not has great influence on the reliability, precision, service life and other performances of the whole machine. It is important to study condition monitoring and fault diagnosis of bearings.
The bearing state monitoring technology can be used for knowing the service performance of the bearing, carrying out early detection on possible faults, analyzing and predicting the possible faults, and processing state signals of bearing equipment to obtain a fault feature set in mechanical fault diagnosis, but the feature set usually contains redundant features, which can seriously affect the diagnosis precision and the diagnosis efficiency of the bearing faults.
Disclosure of Invention
The present invention is directed to a bearing fault diagnosis method and apparatus based on machine learning, so as to solve one or more technical problems in the prior art, and provide at least one useful choice or creation condition.
In order to achieve the purpose, the invention provides the following technical scheme:
a method of machine learning based bearing fault diagnosis, the method comprising:
respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a normal working state to obtain normal data signals; respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a fault state to obtain fault data signals;
filtering the normal data signal to obtain a standard signal, and extracting the amplitude of the standard signal; filtering the fault data signal to obtain a fault signal, and extracting the amplitude of the fault signal;
dividing the fault signal into a plurality of fault levels according to the difference value between the amplitude of the fault signal and the amplitude of the standard signal;
decomposing the energy value of each frequency band of the fault signal through a wavelet packet to obtain parameter characteristics, dividing the parameter characteristics into a plurality of data sets according to the fault level, and taking the plurality of data sets as sample data;
training the sample data by adopting a K mean value clustering algorithm to obtain a bearing fault diagnosis model;
the method comprises the steps of collecting vibration signals of the bearing equipment in real time, extracting frequency domain characteristic values of the vibration signals to form characteristic vectors, and inputting the characteristic vectors into a bearing fault diagnosis model to obtain a fault diagnosis result of the bearing equipment.
Further, the vibration signals are acquired by acceleration sensors respectively arranged on an inner ring, an outer ring and a rolling body of the bearing device.
Further, the vibration signal in the fault state is formed by a single point fault arranged at the inner ring, the outer ring and the rolling bodies of the bearing device, respectively.
Further, the dividing the fault signal into a plurality of fault levels according to the difference between the amplitude of the fault signal and the amplitude of the standard signal specifically includes:
calculating the maximum difference value of the amplitude of the fault signal and the amplitude of the standard signal;
equally dividing the maximum difference value into a plurality of average difference values to obtain a plurality of difference value intervals, and sequentially dividing a plurality of fault grades according to the difference value intervals;
and obtaining the fault grade of the fault signal according to the difference interval where the fault signal is located.
A bearing fault diagnosis apparatus based on machine learning, the apparatus comprising:
the data signal acquisition module is used for respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a normal working state to obtain normal data signals; respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a fault state to obtain fault data signals;
the filtering module is used for filtering the normal data signal to obtain a standard signal and extracting the amplitude of the standard signal; filtering the fault data signal to obtain a fault signal, and extracting the amplitude of the fault signal;
the fault grade dividing module is used for dividing the fault signal into a plurality of fault grades according to the difference value between the amplitude of the fault signal and the amplitude of the standard signal;
the sample data generation module is used for decomposing the energy value of each frequency band of the fault signal through a wavelet packet to obtain parameter characteristics, dividing the parameter characteristics into a plurality of data sets according to the fault level and taking the plurality of data sets as sample data;
the bearing fault diagnosis model generation module is used for training the sample data by adopting a K mean value clustering algorithm to obtain a bearing fault diagnosis model;
and the fault diagnosis module is used for acquiring vibration signals of the bearing equipment in real time, extracting frequency domain characteristic values of the vibration signals to form characteristic vectors, and inputting the characteristic vectors into a bearing fault diagnosis model to obtain a fault diagnosis result of the bearing equipment.
Further, the fault ranking module comprises:
the maximum difference value calculation module is used for calculating the maximum difference value between the amplitude of the fault signal and the amplitude of the standard signal;
the difference value dividing module is used for equally dividing the maximum difference value into a plurality of average difference values so as to obtain a plurality of difference value intervals, and sequentially dividing a plurality of fault levels according to the difference value intervals;
and the fault grade acquisition module is used for acquiring the fault grade of the fault signal according to the difference interval where the fault signal is located.
A bearing fault diagnosis apparatus based on machine learning, the apparatus comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of any of the above-described machine learning based bearing fault diagnosis methods.
The invention has the beneficial effects that: the invention discloses a bearing fault diagnosis method and a bearing fault diagnosis device based on machine learning, wherein redundant characteristics are removed by filtering a normal data signal and a fault data signal, the fault signal is divided into a plurality of fault grades according to the difference value of the amplitude of the fault signal and the amplitude of a standard signal, the energy value of each frequency band of the fault signal is decomposed by a wavelet packet to obtain parameter characteristics, and the parameter characteristics are divided into a plurality of data sets according to the fault grades, so that the subsequent fault classification training is facilitated; taking the plurality of data sets as sample data, and training the sample data by adopting a K mean value clustering algorithm to obtain a bearing fault diagnosis model; and diagnosing the fault result of the bearing equipment through the bearing fault diagnosis model. The invention improves the diagnosis precision and the diagnosis efficiency of the bearing fault by removing the redundant features in the original features.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a bearing fault diagnosis method based on machine learning according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a bearing fault diagnosis device based on machine learning according to an embodiment of the present invention.
Detailed Description
The conception, specific structure and technical effects of the present disclosure will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings to fully understand the objects, aspects and effects of the present disclosure. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, as shown in fig. 1, a bearing fault diagnosis method based on machine learning according to an embodiment of the present invention includes:
s100, respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a normal working state and a fault state;
respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a normal working state to obtain normal data signals; respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a fault state to obtain fault data signals;
step S200, extracting the amplitudes of the normal data signals and the fault data signals respectively;
filtering the normal data signal to obtain a standard signal, and extracting the amplitude of the standard signal; filtering the fault data signal to obtain a fault signal, and extracting the amplitude of the fault signal;
step S300, dividing the fault signal into a plurality of fault levels according to the difference value of the amplitude of the fault signal and the amplitude of the standard signal;
s400, decomposing the energy value of each frequency band of the fault signal through a wavelet packet to obtain parameter characteristics, dividing the parameter characteristics into a plurality of data sets according to the fault level, and taking the plurality of data sets as sample data;
s500, training sample data by adopting a K-means clustering algorithm to obtain a bearing fault diagnosis model;
the input of the bearing fault diagnosis model is sample data, and the output is the fault grade of the inner ring, the outer ring or the rolling body;
step S600, collecting vibration signals of the bearing equipment in real time, extracting frequency domain characteristic values of the vibration signals to form characteristic vectors, and inputting the characteristic vectors into a bearing fault diagnosis model to obtain a fault diagnosis result of the bearing equipment.
In a preferred embodiment, the vibration signals are detected by acceleration sensors which are arranged in the inner ring, the outer ring and the rolling bodies of the bearing device, respectively.
In a preferred embodiment, the vibration signal in the fault state is formed by a single point fault in the inner ring, the outer ring and the rolling body arrangement of the bearing device, respectively.
In a preferred embodiment, the dividing the fault signal into a plurality of fault levels according to the difference between the amplitude of the fault signal and the amplitude of the standard signal includes:
calculating the maximum difference value of the amplitude of the fault signal and the amplitude of the standard signal;
equally dividing the maximum difference value into a plurality of average difference values to obtain a plurality of difference value intervals, and sequentially dividing a plurality of fault grades according to the difference value intervals;
and obtaining the fault grade of the fault signal according to the difference interval where the fault signal is located.
Referring to fig. 2, an embodiment of the present invention further provides a bearing fault diagnosis apparatus based on machine learning, where the apparatus includes:
a data signal obtaining module 100, configured to obtain vibration signals of an inner ring, an outer ring, and a rolling element of the bearing device in a normal working state, respectively, to obtain normal data signals; respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a fault state to obtain fault data signals;
a filtering module 200, configured to filter the normal data signal to obtain a standard signal, and extract an amplitude of the standard signal; filtering the fault data signal to obtain a fault signal, and extracting the amplitude of the fault signal;
a fault level dividing module 300 for dividing the fault signal into a plurality of fault levels according to a difference between the amplitude of the fault signal and the amplitude of the standard signal;
a sample data generating module 400, configured to perform wavelet packet decomposition on the energy value of each frequency band of the fault signal to obtain parameter characteristics, divide the parameter characteristics into multiple data sets according to a fault level, and use the multiple data sets as sample data;
the bearing fault diagnosis model generation module 500 is used for training sample data by adopting a K-means clustering algorithm to obtain a bearing fault diagnosis model;
the fault diagnosis module 600 is configured to collect a vibration signal of the bearing device in real time, extract a frequency domain feature value of the vibration signal, form a feature vector, and input the feature vector into a bearing fault diagnosis model to obtain a fault diagnosis result of the bearing device.
In a preferred embodiment, the fault ranking module 300 further comprises:
the maximum difference value calculation module is used for calculating the maximum difference value between the amplitude of the fault signal and the amplitude of the standard signal;
the difference value dividing module is used for equally dividing the maximum difference value into a plurality of average difference values so as to obtain a plurality of difference value intervals, and sequentially dividing a plurality of fault levels according to the difference value intervals;
and the fault grade acquisition module is used for acquiring the fault grade of the fault signal according to the difference interval where the fault signal is located.
The embodiment of the invention also provides a bearing fault diagnosis device based on machine learning, which comprises: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of any of the above-described machine learning based bearing fault diagnosis methods.
From the above description of the embodiments, it is clear for those skilled in the art that the above embodiments and methods can be implemented by software plus necessary general hardware platform, and based on such understanding, the technical solution of the present invention or portions contributing to the prior art can be embodied in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, and including several instructions for causing a terminal device (computer, server, etc.) to execute the methods described in the embodiments of the present invention.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, and the processor is a control center of the bearing fault diagnosis apparatus based on machine learning, and various interfaces and lines are used to connect various parts of the whole bearing fault diagnosis apparatus based on machine learning.
The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the machine-learning-based bearing failure diagnosis apparatus by executing or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may primarily include a program storage area and a data storage area, which may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart-Media-Card (SMC), a Secure-Digital (SD) Card, a Flash-memory Card (Flash-Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
While the present disclosure has been described in considerable detail and with particular reference to a few illustrative embodiments thereof, it is not intended to be limited to any such details or embodiments or any particular embodiments, but it is to be construed with references to the appended claims so as to provide a broad, possibly open interpretation of such claims in view of the prior art, and to effectively encompass the intended scope of the disclosure. Furthermore, the foregoing describes the disclosure in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the disclosure, not presently foreseen, may nonetheless represent equivalent modifications thereto.
Claims (7)
1. A bearing fault diagnosis method based on machine learning, the method comprising:
respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a normal working state to obtain normal data signals; respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a fault state to obtain fault data signals;
filtering the normal data signal to obtain a standard signal, and extracting the amplitude of the standard signal; filtering the fault data signal to obtain a fault signal, and extracting the amplitude of the fault signal;
dividing the fault signal into a plurality of fault levels according to the difference value between the amplitude of the fault signal and the amplitude of the standard signal;
decomposing the energy value of each frequency band of the fault signal through a wavelet packet to obtain parameter characteristics, dividing the parameter characteristics into a plurality of data sets according to the fault level, and taking the plurality of data sets as sample data;
training the sample data by adopting a K mean value clustering algorithm to obtain a bearing fault diagnosis model;
the method comprises the steps of collecting vibration signals of the bearing equipment in real time, extracting frequency domain characteristic values of the vibration signals to form characteristic vectors, and inputting the characteristic vectors into a bearing fault diagnosis model to obtain a fault diagnosis result of the bearing equipment.
2. The bearing fault diagnosis method based on machine learning of claim 1, wherein the vibration signals are acquired by acceleration sensors respectively provided at an inner ring, an outer ring and a rolling body of the bearing device.
3. The bearing fault diagnosis method based on machine learning according to claim 2, characterized in that the vibration signal in the fault state is formed by single point faults arranged at the inner ring, the outer ring and the rolling bodies of the bearing device, respectively.
4. The bearing fault diagnosis method based on machine learning according to claim 1, wherein the fault signal is divided into a plurality of fault levels according to the difference between the amplitude of the fault signal and the amplitude of the standard signal, specifically:
calculating the maximum difference value of the amplitude of the fault signal and the amplitude of the standard signal;
equally dividing the maximum difference value into a plurality of average difference values to obtain a plurality of difference value intervals, and sequentially dividing a plurality of fault grades according to the difference value intervals;
and obtaining the fault grade of the fault signal according to the difference interval where the fault signal is located.
5. A bearing fault diagnosis apparatus based on machine learning, characterized in that the apparatus comprises:
the data signal acquisition module is used for respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a normal working state to obtain normal data signals; respectively acquiring vibration signals of an inner ring, an outer ring and a rolling body of the bearing equipment in a fault state to obtain fault data signals;
the filtering module is used for filtering the normal data signal to obtain a standard signal and extracting the amplitude of the standard signal; filtering the fault data signal to obtain a fault signal, and extracting the amplitude of the fault signal;
the fault grade dividing module is used for dividing the fault signal into a plurality of fault grades according to the difference value between the amplitude of the fault signal and the amplitude of the standard signal;
the sample data generation module is used for decomposing the energy value of each frequency band of the fault signal through a wavelet packet to obtain parameter characteristics, dividing the parameter characteristics into a plurality of data sets according to the fault level and taking the plurality of data sets as sample data;
the bearing fault diagnosis model generation module is used for training the sample data by adopting a K mean value clustering algorithm to obtain a bearing fault diagnosis model;
and the fault diagnosis module is used for acquiring vibration signals of the bearing equipment in real time, extracting frequency domain characteristic values of the vibration signals to form characteristic vectors, and inputting the characteristic vectors into a bearing fault diagnosis model to obtain a fault diagnosis result of the bearing equipment.
6. The machine-learning-based bearing fault diagnosis device according to claim 5, wherein the fault ranking module comprises:
the maximum difference value calculation module is used for calculating the maximum difference value between the amplitude of the fault signal and the amplitude of the standard signal;
the difference value dividing module is used for equally dividing the maximum difference value into a plurality of average difference values so as to obtain a plurality of difference value intervals, and sequentially dividing a plurality of fault levels according to the difference value intervals;
and the fault grade acquisition module is used for acquiring the fault grade of the fault signal according to the difference interval where the fault signal is located.
7. A bearing fault diagnosis apparatus based on machine learning, characterized in that the apparatus comprises: memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the machine learning based bearing fault diagnosis method according to any one of claims 1 to 4.
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