CN110672323B - Bearing health state assessment method and device based on neural network - Google Patents
Bearing health state assessment method and device based on neural network Download PDFInfo
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- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
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Abstract
The invention relates to the technical field of fault diagnosis, in particular to a bearing health state assessment method and device based on a neural network.
Description
Technical Field
The invention relates to the technical field of fault diagnosis, in particular to a bearing health state assessment method and device based on a neural network.
Background
As an emerging comprehensive marginal discipline, the bearing fault diagnosis technology initially forms a relatively complete discipline system. As for the technical means, the vibration diagnosis technology has become the mainstream technology of bearing fault diagnosis. The rapid progress of the computer technology and the signal information processing technology greatly promotes the development of the bearing fault diagnosis and monitoring technology towards the direction of scientification and practicability.
However, in the field of current bearing fault diagnosis, large-scale data concurrency often exists, great challenges are brought to the real-time requirement of fault diagnosis, and the online prediction rate of bearing fault diagnosis needs to be improved urgently.
Disclosure of Invention
The invention aims to provide a bearing health state assessment method and device based on a neural network, and aims to improve the online prediction rate of bearing fault diagnosis.
In order to achieve the purpose, the invention provides the following technical scheme:
a bearing health state assessment method based on a neural network comprises the following steps:
acquiring training data, wherein the training data is historical data representing a bearing vibration signal;
extracting a characteristic value of the training data and a bearing health state corresponding to the characteristic value;
determining a classification model containing the corresponding relation between the characteristic value and the bearing health state;
responding to a bearing health state evaluation instruction, acquiring a bearing vibration signal in real time, processing the bearing vibration signal to generate sampling data, inputting the sampling data into the classification model for evaluation, and outputting a bearing health state evaluation result.
Further, the characteristic values include vibration displacement, vibration velocity, vibration acceleration and high-frequency acceleration, the bearing health state includes a normal state and an abnormal state, and the abnormal state includes three categories, which are respectively: light wear state, wear failure state, failure state.
Further, the determining a classification model containing the correspondence between the characteristic values and the bearing health states includes:
dividing the characteristic values into a plurality of characteristic sets through a self-organizing neural network algorithm;
calculating the variance value of each feature set and the average variance value of all feature sets;
generating a comparison result according to the variance value and the average variance value, and preliminarily judging the health state of the bearing according to the comparison result;
determining the category and the classification difference value of the bearing health state according to the variance value and the average variance value;
and taking the classification and the classification difference value as a classification model of the bearing health state.
Further, the preliminary judgment of the health state of the bearing according to the comparison result specifically comprises:
when the variance value of the feature sets is less than or equal to the average variance value of all the feature sets, the bearing health state is a normal state;
and when the variance value of the feature sets is larger than the average variance value of all the feature sets, the health state of the bearing is an abnormal state.
Further, the determining the category and the classification difference of the bearing health state according to the variance value and the average variance value includes:
obtaining the variance values of all the feature sets, comparing to obtain the minimum variance value and the maximum variance value in all the variance values, and recording the minimum variance value as SminAnd recording the maximum variance value as SmaxThe mean variance of all the feature sets is recorded as
Calculating a classification difference value of the feature set by the following formula:
wherein n is the general category of bearing health status;
calculating the bearing health state class corresponding to the characteristic set by the following formula:
wherein Si represents a variance value of the ith feature set, k is a class number of the bearing health state, and k is 1, 2.
A neural network-based bearing state of health assessment apparatus, the apparatus comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in modules of:
the extraction module is used for acquiring training data, wherein the training data are historical data representing bearing vibration signals, and extracting characteristic values of the training data and bearing health states corresponding to the characteristic values;
the determining module is used for determining a classification model containing the corresponding relation between the characteristic value and the bearing health state;
and the output module is used for responding to the bearing health state evaluation instruction, acquiring a bearing vibration signal in real time, processing the bearing vibration signal to generate sampling data, inputting the sampling data into the classification model for evaluation, and outputting a bearing health state evaluation result.
Further, in the extraction module, the characteristic value includes vibration displacement, vibration velocity, vibration acceleration, and high-frequency acceleration, the bearing health status includes a normal status and an abnormal status, and the abnormal status includes three categories, which are: light wear state, wear failure state, failure state.
The invention has the beneficial effects that: the invention discloses a bearing health state evaluation method and device based on a neural network, which comprises the steps of firstly obtaining training data, wherein the training data are historical data representing bearing vibration signals, extracting characteristic values of the training data and fault types corresponding to the characteristic values, then determining optimal dimension reduction training data of the training data, further calculating mean values and covariance matrixes corresponding to all the fault types in the optimal dimension reduction training data, obtaining dimension reduction test data by reducing dimensions of the test data received in real time, calculating probability values of the dimension reduction data under all the fault types according to the mean values and the covariance matrixes, and taking the fault type with the maximum probability value as a fault type of bearing fault diagnosis. The invention improves the online prediction rate of bearing fault diagnosis.
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 method for evaluating a health status of a bearing based on a neural network according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating step S300 according to an embodiment of the present invention;
fig. 3 is a block diagram of a bearing health status evaluation apparatus based on a neural network according to an embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. Based on the embodiments of the present invention, other embodiments obtained by a person of ordinary skill in the art without any creative effort belong to the protection scope of the present invention.
Referring to fig. 1, an embodiment of the present invention provides a bearing health state assessment method based on a neural network, including the following steps:
s100, acquiring training data, wherein the training data are historical data representing bearing vibration signals;
s200, extracting a characteristic value of the training data and a bearing health state corresponding to the characteristic value;
step S300, determining a classification model containing the corresponding relation between the characteristic value and the bearing health state;
and S400, responding to a bearing health state evaluation instruction, acquiring a bearing vibration signal in real time, processing the bearing vibration signal to generate sampling data, inputting the sampling data into the classification model for evaluation, and outputting a bearing health state evaluation result.
In the embodiment, the characteristic values of the training data and the bearing health states corresponding to the characteristic values are extracted, the characteristic values and the fault types are extracted, the training data are obtained, and the classification model containing the corresponding relation between the characteristic values and the bearing health states is determined, so that the classification model has good discrimination.
In a specific embodiment, the characteristic values include vibration displacement, vibration velocity, vibration acceleration, and high-frequency acceleration, the bearing health status includes a normal status and an abnormal status, and the abnormal status includes three categories, which are: light wear state, wear failure state, failure state.
Referring to fig. 2, as a preference of this embodiment, the step S300 includes:
step S310, dividing the characteristic values into a plurality of characteristic sets through a self-organizing neural network algorithm;
step S320, calculating the variance value of each feature set and the average variance value of all feature sets;
s330, generating a comparison result according to the variance value and the average variance value, and preliminarily judging the health state of the bearing according to the comparison result;
step S340, determining the category and the classification difference value of the bearing health state according to the variance value and the average variance value;
and step S350, taking the classification and the classification difference value as a classification model of the bearing health state.
The self-organizing neural network self-organizes and self-adaptively changes network parameters and structures by automatically searching the intrinsic rules and essential attributes in a sample. The learning and classification of the multi-layer perceptron are conditioned by knowing a certain priori knowledge, namely the adjustment of the network weight is carried out under supervision.
In such a network, the output nodes are widely connected to other nodes in their neighborhood and mutually stimulate each other. The input nodes and the output nodes are connected through the strength values. By setting rules, the intensity values are constantly adjusted so that, when stable, all nodes of each neighborhood have similar outputs for some input and the probability distribution of the cluster is close to that of the input pattern. The self-organizing neural network has the greatest advantage of self-adapting weight, and the optimal solution is greatly and conveniently found.
In a specific embodiment, in step S330, the bearing health status is preliminarily determined according to the comparison result, specifically:
when the variance value of the feature sets is less than or equal to the average variance value of all the feature sets, the bearing health state is a normal state;
and when the variance value of the feature sets is larger than the average variance value of all the feature sets, the health state of the bearing is an abnormal state.
Preferably, in this embodiment, the step S340 includes:
obtaining the variance values of all the feature sets, comparing to obtain the minimum variance value and the maximum variance value in all the variance values, and recording the minimum variance value as SminAnd recording the maximum variance value as SmaxThe mean variance of all the feature sets is recorded as
Calculating a classification difference value of the feature set by the following formula:
wherein n is the general category of bearing health status;
calculating the bearing health state class corresponding to the characteristic set by the following formula:
wherein Si represents a variance value of the ith feature set, k is a class number of the bearing health state, and k is 1, 2.
Referring to fig. 3, the present embodiment further provides a neural network-based bearing health status assessment apparatus, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in modules of:
an obtaining module 100, configured to obtain training data, where the training data is historical data representing a bearing vibration signal;
an extracting module 200, configured to extract a feature value of the training data and a bearing health state corresponding to the feature value;
a determining module 300, configured to determine a classification model including a correspondence between the feature values and the bearing health states;
and the output module 400 is used for responding to a bearing health state evaluation instruction, acquiring a bearing vibration signal in real time, processing the bearing vibration signal to generate sampling data, inputting the sampling data into the classification model for evaluation, and outputting a bearing health state evaluation result.
Preferably, in the extraction module 200, the characteristic values include vibration displacement, vibration velocity, vibration acceleration, and high-frequency acceleration, the bearing health status includes a normal status and an abnormal status, and the abnormal status includes three categories, which are: light wear state, wear failure state, failure state.
The bearing health state evaluation device based on the neural network can be operated in computing equipment such as desktop computers, mobile phones, notebooks, tablet computers and cloud servers. The system which can be operated by the neural network-based bearing health state evaluation device can comprise, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the example is merely an example of a neural network-based bearing health status assessment apparatus, and does not constitute a limitation of a neural network-based bearing health status assessment apparatus, and may include more or less components than the neural network-based bearing health status assessment apparatus, or may combine some components, or different components, for example, the neural network-based bearing health status assessment apparatus may further include an input-output device, a network access device, a bus, and the like.
The Processor may be a Central-Processing Unit (CPU), other general-purpose Processor, a Digital Signal Processor (DSP), an Application-Specific-Integrated-Circuit (ASIC), an off-the-shelf 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, the processor is a control center of the operational system of the neural network based bearing health status assessment device, and various interfaces and lines are used to connect various parts of the operational system of the entire neural network based bearing health status assessment device.
The memory may be used for storing the computer programs and/or modules, and the processor may implement the various functions of the neural network-based bearing health status assessment apparatus by executing or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory 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 (5)
1. A bearing health state assessment method based on a neural network is characterized by comprising the following steps:
acquiring training data, wherein the training data is historical data representing a bearing vibration signal;
extracting a characteristic value of the training data and a bearing health state corresponding to the characteristic value;
determining a classification model containing the corresponding relation between the characteristic value and the bearing health state;
responding to a bearing health state evaluation instruction, acquiring a bearing vibration signal in real time, processing the bearing vibration signal to generate sampling data, inputting the sampling data into the classification model for evaluation, and outputting a bearing health state evaluation result;
wherein the determining a classification model containing the feature values and the bearing health state correspondence comprises:
dividing the characteristic values into a plurality of characteristic sets through a self-organizing neural network algorithm;
calculating the variance value of each feature set and the average variance value of all feature sets;
generating a comparison result according to the variance value and the average variance value, and preliminarily judging the health state of the bearing according to the comparison result;
determining the category and the classification difference value of the bearing health state according to the variance value and the average variance value;
using the classification and the classification difference value as a classification model of the bearing health state;
wherein the determining the category and the classification difference of the bearing health state according to the variance value and the average variance value comprises:
obtaining the variance values of all the feature sets, comparing to obtain the minimum variance value and the maximum variance value in all the variance values, and recording the minimum variance value as SminAnd recording the maximum variance value as SmaxThe mean variance of all the feature sets is recorded as
Calculating a classification difference value of the feature set by the following formula:
wherein n is the general category of bearing health status;
calculating the bearing health state class corresponding to the characteristic set by the following formula:
wherein Si represents a variance value of the ith feature set, k is a class number of the bearing health state, and k is 1, 2.
2. The neural network-based bearing health status assessment method according to claim 1, wherein the characteristic values comprise vibration displacement, vibration velocity, vibration acceleration, high frequency acceleration, the bearing health status comprises a normal status and an abnormal status, the abnormal status comprises three categories, which are respectively: light wear state, wear failure state, failure state.
3. The method for evaluating the health status of the bearing based on the neural network as claimed in claim 2, wherein the preliminary judgment of the health status of the bearing according to the comparison result is specifically as follows:
when the variance value of the feature sets is less than or equal to the average variance value of all the feature sets, the bearing health state is a normal state;
and when the variance value of the feature sets is larger than the average variance value of all the feature sets, the health state of the bearing is an abnormal state.
4. A neural network-based bearing state of health assessment apparatus, the apparatus comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to operate in modules of:
the extraction module is used for acquiring training data, wherein the training data are historical data representing bearing vibration signals, and extracting characteristic values of the training data and bearing health states corresponding to the characteristic values;
the determining module is used for determining a classification model containing the corresponding relation between the characteristic value and the bearing health state;
the output module is used for responding to a bearing health state evaluation instruction, acquiring a bearing vibration signal in real time, processing the bearing vibration signal to generate sampling data, inputting the sampling data into the classification model for evaluation, and outputting a bearing health state evaluation result;
wherein the determination module is to:
dividing the characteristic values into a plurality of characteristic sets through a self-organizing neural network algorithm;
calculating the variance value of each feature set and the average variance value of all feature sets;
generating a comparison result according to the variance value and the average variance value, and preliminarily judging the health state of the bearing according to the comparison result;
determining the category and the classification difference value of the bearing health state according to the variance value and the average variance value;
using the classification and the classification difference value as a classification model of the bearing health state;
wherein the determining module is further specifically configured to:
obtaining the variance values of all the feature sets, comparing to obtain the minimum variance value and the maximum variance value in all the variance values, and recording the minimum variance value as SminAnd recording the maximum variance value as SmaxThe mean variance of all the feature sets is recorded as
Calculating a classification difference value of the feature set by the following formula:
wherein n is the general category of bearing health status;
calculating the bearing health state class corresponding to the characteristic set by the following formula:
wherein Si represents a variance value of the ith feature set, k is a class number of the bearing health state, and k is 1, 2.
5. The neural network-based bearing health state assessment device according to claim 4, wherein in the extraction module, the characteristic values comprise vibration displacement, vibration velocity, vibration acceleration and high frequency acceleration, the bearing health state comprises a normal state and an abnormal state, and the abnormal state comprises three categories, which are respectively: light wear state, wear failure state, failure state.
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| CN111638028B (en) * | 2020-05-20 | 2022-05-10 | 国网河北省电力有限公司电力科学研究院 | High-voltage parallel reactor mechanical state evaluation method based on vibration characteristics |
| CN112347898B (en) * | 2020-11-03 | 2024-04-09 | 重庆大学 | Rolling bearing health index construction method based on DCAE neural network |
| CN113486586B (en) * | 2021-07-06 | 2023-09-05 | 新奥新智科技有限公司 | Device health state evaluation method and device, computer device and storage medium |
| CN113537373A (en) * | 2021-07-23 | 2021-10-22 | 中国人民解放军陆军工程大学 | Diesel engine health state assessment method, device and equipment based on digital twinning |
| CN113687972B (en) * | 2021-08-30 | 2023-07-25 | 中国平安人寿保险股份有限公司 | Processing method, device, equipment and storage medium for abnormal data of business system |
| CN115840133B (en) * | 2023-02-24 | 2023-05-05 | 湖南遥光科技有限公司 | Circuit health grading evaluation method and electronic product health grading evaluation method |
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| CN102589884A (en) * | 2012-03-01 | 2012-07-18 | 西北工业大学 | Method for diagnosing failure of airplane generator bearing based on GentleBoost |
| CN103900816A (en) * | 2014-04-14 | 2014-07-02 | 上海电机学院 | Method for diagnosing bearing breakdown of wind generating set |
| CN104931263B (en) * | 2015-06-18 | 2016-10-12 | 东南大学 | A kind of Method for Bearing Fault Diagnosis based on symbolization probabilistic finite state machine |
| CN111094927A (en) * | 2017-09-26 | 2020-05-01 | 舍弗勒技术股份两合公司 | Bearing fault diagnosis method and device, readable storage medium and electronic equipment |
| CN107886091A (en) * | 2017-12-19 | 2018-04-06 | 南京航空航天大学 | A kind of mechanical breakdown fast diagnosis method based on deep neural network |
| CN109781411B (en) * | 2019-01-28 | 2020-05-19 | 西安交通大学 | Bearing fault diagnosis method combining improved sparse filter and KELM |
| CN110031226A (en) * | 2019-04-12 | 2019-07-19 | 佛山科学技术学院 | A kind of diagnostic method and device of bearing fault |
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