CN116642696A - Railway bearing health monitoring method, device, system, terminal and storage medium - Google Patents
Railway bearing health monitoring method, device, system, terminal and storage medium Download PDFInfo
- Publication number
- CN116642696A CN116642696A CN202310399146.7A CN202310399146A CN116642696A CN 116642696 A CN116642696 A CN 116642696A CN 202310399146 A CN202310399146 A CN 202310399146A CN 116642696 A CN116642696 A CN 116642696A
- Authority
- CN
- China
- Prior art keywords
- rolling bearing
- monitored
- index data
- health
- health index
- 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.)
- Pending
Links
- 230000036541 health Effects 0.000 title claims abstract description 125
- 238000000034 method Methods 0.000 title claims abstract description 63
- 238000012544 monitoring process Methods 0.000 title claims abstract description 33
- 238000005096 rolling process Methods 0.000 claims abstract description 114
- 238000003745 diagnosis Methods 0.000 claims abstract description 32
- 125000004122 cyclic group Chemical group 0.000 claims abstract description 16
- 238000000354 decomposition reaction Methods 0.000 claims abstract description 12
- 230000015654 memory Effects 0.000 claims description 28
- 238000001228 spectrum Methods 0.000 claims description 24
- 238000004590 computer program Methods 0.000 claims description 19
- 238000012549 training Methods 0.000 claims description 16
- 238000012360 testing method Methods 0.000 claims description 15
- 238000011176 pooling Methods 0.000 claims description 12
- 230000011218 segmentation Effects 0.000 claims description 11
- 240000007651 Rubus glaucus Species 0.000 claims description 9
- 235000011034 Rubus glaucus Nutrition 0.000 claims description 9
- 235000009122 Rubus idaeus Nutrition 0.000 claims description 9
- 230000007246 mechanism Effects 0.000 claims description 8
- 238000013075 data extraction Methods 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 abstract description 10
- 238000011161 development Methods 0.000 abstract description 4
- 239000002699 waste material Substances 0.000 abstract description 4
- 230000006870 function Effects 0.000 description 8
- 238000005516 engineering process Methods 0.000 description 7
- 238000012545 processing Methods 0.000 description 7
- 230000002457 bidirectional effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
- 238000001514 detection method Methods 0.000 description 3
- 230000018109 developmental process Effects 0.000 description 3
- 238000012806 monitoring device Methods 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 230000006403 short-term memory Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 238000010219 correlation analysis Methods 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M13/00—Testing of machine parts
- G01M13/04—Bearings
- G01M13/045—Acoustic or vibration analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
- G06N3/0442—Recurrent networks, e.g. Hopfield networks characterised by memory or gating, e.g. long short-term memory [LSTM] or gated recurrent units [GRU]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Business, Economics & Management (AREA)
- General Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Computational Linguistics (AREA)
- Game Theory and Decision Science (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Biophysics (AREA)
- Development Economics (AREA)
- Biomedical Technology (AREA)
- General Health & Medical Sciences (AREA)
- Acoustics & Sound (AREA)
- Health & Medical Sciences (AREA)
- Entrepreneurship & Innovation (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
Abstract
The application provides a method, a device, a system, a terminal and a storage medium for monitoring the health of a railway bearing. The railway bearing health monitoring method comprises the following steps: acquiring an original vibration signal of a rolling bearing to be monitored; determining health index data of the rolling bearing to be monitored according to the original vibration signal by adopting a Fourier decomposition method based on a cyclic envelope experience; performing fault diagnosis on the rolling bearing to be monitored according to the health index data to obtain a fault diagnosis result of the rolling bearing to be monitored; and predicting the residual life of the rolling bearing to be monitored according to the health index data to obtain a residual life prediction result of the rolling bearing to be monitored. The application can accurately judge the health state and service life of the rolling bearing, promote the development of an intelligent operation and maintenance system and avoid a great amount of resource waste caused by regular overhaul.
Description
Technical Field
The application relates to the technical field of bearing health monitoring, in particular to a railway bearing health monitoring method, device, system, terminal and storage medium.
Background
The rolling bearing is used as one of the key rotating parts of the running part, and various faults are very easy to occur due to the severe and complex working environment, so that equipment is abnormal, even serious safety accidents are caused, and serious casualties and economic losses are caused.
The running maintenance of the rolling bearing is in an excessive maintenance state of regular overhauling and forced scrapping for a long time, so that a great deal of waste of resources is caused. Therefore, how to accurately judge the health state and service life of the rolling bearing and promote the development of an intelligent operation and maintenance system is a problem to be solved urgently at present.
Disclosure of Invention
The embodiment of the application provides a method, a device, a system, a terminal and a storage medium for monitoring the health of a railway bearing, which are used for solving the problem that a great amount of resources are wasted because the health state and the service life of the rolling bearing cannot be accurately judged at present.
In a first aspect, an embodiment of the present application provides a method for monitoring health of a railway bearing, including:
acquiring an original vibration signal of a rolling bearing to be monitored;
determining health index data of the rolling bearing to be monitored according to the original vibration signal by adopting a Fourier decomposition method based on a cyclic envelope experience;
performing fault diagnosis on the rolling bearing to be monitored according to the health index data to obtain a fault diagnosis result of the rolling bearing to be monitored;
and predicting the residual life of the rolling bearing to be monitored according to the health index data to obtain a residual life prediction result of the rolling bearing to be monitored.
In one possible implementation manner, a cyclic envelope-based empirical fourier decomposition method is adopted, and health index data of the rolling bearing to be monitored is determined according to an original vibration signal, and the method comprises the following steps:
performing fast Fourier transform on the original vibration signal to obtain a corresponding signal frequency spectrum;
carrying out cyclic envelope on the signal spectrum to obtain a spectrum envelope curve;
frequency band segmentation is carried out on the frequency spectrum envelope curves to obtain a plurality of frequency band envelope curves;
constructing a zero-phase filter, and carrying out signal reconstruction on each frequency band envelope curve by adopting inverse fast Fourier transform to obtain a plurality of single-component signals;
health index data of the rolling bearing to be monitored is determined based on the plurality of single component signals.
In one possible implementation, the single component signal includes a component time domain waveform and a component envelope curve;
accordingly, determining health indicator data of the rolling bearing to be monitored based on the plurality of single component signals comprises:
and determining the health index data of the rolling bearing to be monitored based on the time domain waveforms of the components and the envelope curves of the components.
In one possible implementation manner, predicting the remaining life of the rolling bearing to be monitored according to the health index data to obtain a remaining life prediction result of the rolling bearing to be monitored, including:
and inputting the health index data into a pre-trained two-way long-short-term memory DA-BLSTM network model based on an attention mechanism to obtain a residual life prediction result of the rolling bearing to be monitored.
In one possible implementation manner, before the health index data is input into the pre-trained bidirectional long-short term memory DA-BLSTM network model based on the attention mechanism to obtain the residual life prediction result of the rolling bearing to be monitored, the method further comprises:
acquiring a health index data sample set, wherein the health index data sample set comprises a plurality of health index sample data marked with residual life;
clustering the health index data sample set by adopting a gray correlation-based clustering and GRACC classification method to obtain a plurality of clusters;
and dividing each cluster into a training set and a testing set, independently training the pre-constructed DA-BLSTM network model based on the training set corresponding to the cluster, and independently testing the trained DA-BLSTM network model based on the testing set corresponding to the cluster to obtain the pre-trained DA-BLSTM network model.
In one possible implementation, the DA-BLSTM network model includes a first layer of convolution, a second layer of convolution, a first layer of maximum pooling, a third layer of convolution, a second layer of maximum pooling, an input attention layer, a bi-directional LSTM layer, a directional attention layer, and a fully connected layer that are connected in sequence.
In a second aspect, an embodiment of the present application provides a railway bearing health monitoring device, including:
the acquisition module is used for acquiring an original vibration signal of the rolling bearing to be monitored;
the health index data extraction module is used for determining health index data of the rolling bearing to be monitored according to the original vibration signal by adopting a cyclic envelope experience Fourier decomposition method;
the fault diagnosis module is used for carrying out fault diagnosis on the rolling bearing to be monitored according to the health index data to obtain a fault diagnosis result of the rolling bearing to be monitored;
and the life prediction module is used for predicting the residual life of the rolling bearing to be monitored according to the health index data to obtain a residual life prediction result of the rolling bearing to be monitored.
In a third aspect, an embodiment of the present application provides a terminal, including a processor and a memory, where the memory is configured to store a computer program, and the processor is configured to invoke and run the computer program stored in the memory, to perform the method for monitoring health of a railway bearing according to the first aspect or any possible implementation manner of the first aspect.
In a fourth aspect, an embodiment of the present application provides a railway bearing health monitoring system, including an accelerometer, a capture card, a power adapter, and a terminal according to the third aspect;
the accelerometer is connected with a GPIO port of the terminal through the acquisition card; the power adapter is connected with a power interface of the terminal;
wherein the terminal is raspberry pie.
In a fifth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method for monitoring health of a railway bearing as described above in the first aspect or any one of the possible implementations of the first aspect.
The embodiment of the application provides a method, a device, a system, a terminal and a storage medium for monitoring the health of a railway bearing, which are used for acquiring an original vibration signal of a rolling bearing to be monitored; determining health index data of the rolling bearing to be monitored according to the original vibration signal by adopting a Fourier decomposition method based on a cyclic envelope experience; performing fault diagnosis on the rolling bearing to be monitored according to the health index data to obtain a fault diagnosis result of the rolling bearing to be monitored; according to the health index data, the residual life of the rolling bearing to be monitored is predicted, so that a residual life prediction result of the rolling bearing to be monitored is obtained, the health state and the service life of the rolling bearing can be accurately judged, the development of an intelligent operation and maintenance system is promoted, and a large amount of resource waste caused by regular overhaul is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for monitoring the health of a railway bearing according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a railway bearing health monitoring device according to an embodiment of the present application;
fig. 3 is a schematic diagram of a terminal according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as the particular system architecture, techniques, etc., in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
For the purpose of making the objects, technical solutions and advantages of the present application more apparent, the following description will be made by way of specific embodiments with reference to the accompanying drawings.
Referring to fig. 1, a flowchart of an implementation of a method for monitoring health of a railway bearing according to an embodiment of the present application is shown. The subject of the railroad bearing health monitoring method may be a terminal, which may be a raspberry group.
Referring to fig. 1, the method for monitoring the health of the railway bearing comprises the following steps:
in S101, an original vibration signal of the rolling bearing to be monitored is acquired.
The original vibration signal of the rolling bearing to be monitored can be acquired through an accelerometer stuck on the axle box. The accelerometer may be, among other things, an IEPE (Integrated Electronics Piezo Electric, integrated circuit type piezoelectric) acceleration sensor that may generate vibration signal data needed to analyze corresponding characteristics and damage characteristics from motor current signals.
In S102, a cyclic envelope-based empirical fourier decomposition method is employed, and health index data of the rolling bearing to be monitored is determined from the original vibration signal.
The health index data of the rolling bearing to be monitored comprise related index data which can be used for health monitoring of the rolling bearing to be monitored. The health index data of the rolling bearing to be monitored can be obtained by adopting a Fourier decomposition method based on the cyclic envelope experience according to the original vibration signal.
In S103, according to the health index data, fault diagnosis is performed on the rolling bearing to be monitored, and a fault diagnosis result of the rolling bearing to be monitored is obtained.
In this embodiment, fault diagnosis is performed on the rolling bearing to be monitored according to the health index data, and implementation means for obtaining the fault diagnosis result of the rolling bearing to be monitored is not particularly limited, and any implementation means may be used.
For example, the health index data may be input into a pre-trained fault diagnosis model to obtain a fault diagnosis result of the rolling bearing to be monitored. The fault diagnosis model may be a convolutional neural network model, a random forest model, or the like.
The fault diagnosis result of the rolling bearing to be monitored can be healthy, slight fault or serious fault. The fault diagnosis result of the rolling bearing to be monitored may also include a fault type or the like.
In S104, the remaining life of the rolling bearing to be monitored is predicted according to the health index data, so as to obtain a remaining life prediction result of the rolling bearing to be monitored.
The embodiment can also predict the residual life of the rolling bearing to be monitored based on the health index data of the rolling bearing to be monitored to obtain the residual life prediction result of the rolling bearing to be monitored, namely, how long the rolling bearing to be monitored can be used.
In some possible implementations, the step S104 may include:
and if the fault diagnosis result of the rolling bearing to be monitored is healthy (i.e. no fault), predicting the residual life of the rolling bearing to be monitored according to the health index data to obtain the residual life prediction result of the rolling bearing to be monitored.
The method comprises the steps of obtaining an original vibration signal of a rolling bearing to be monitored; determining health index data of the rolling bearing to be monitored according to the original vibration signal by adopting a Fourier decomposition method based on a cyclic envelope experience; performing fault diagnosis on the rolling bearing to be monitored according to the health index data to obtain a fault diagnosis result of the rolling bearing to be monitored; according to the health index data, the residual life of the rolling bearing to be monitored is predicted, so that a residual life prediction result of the rolling bearing to be monitored is obtained, the health state and the service life of the rolling bearing can be accurately judged, the development of an intelligent operation and maintenance system is promoted, and a large amount of resource waste caused by regular overhaul is avoided.
In some embodiments, the step S102 may include:
performing fast Fourier transform on the original vibration signal to obtain a corresponding signal frequency spectrum;
carrying out cyclic envelope on the signal spectrum to obtain a spectrum envelope curve;
frequency band segmentation is carried out on the frequency spectrum envelope curves to obtain a plurality of frequency band envelope curves;
constructing a zero-phase filter, and carrying out signal reconstruction on each frequency band envelope curve by adopting inverse fast Fourier transform to obtain a plurality of single-component signals;
health index data of the rolling bearing to be monitored is determined based on the plurality of single component signals.
In some possible implementations, the above-mentioned band-splitting the spectrum envelope curve to obtain a plurality of band envelope curves may include:
and carrying out frequency band segmentation on the spectrum envelope curve by adopting a local maximum and minimum (localmax min) segmentation technology or a modified local maximum and minimum (localmax min) segmentation technology to obtain a plurality of frequency band envelope curves.
The embodiment can perform Fast Fourier Transform (FFT) on the original vibration signal to obtain a corresponding signal spectrum; the signal spectrum is circularly enveloped to obtain a spectrum envelope curve, so that the number of useless extreme points can be reduced, and the interference of noise on components can be restrained; performing frequency band segmentation on the spectrum envelope curves by adopting an improved local maximum and minimum segmentation technology to obtain spectrum segmentation boundaries, and obtaining a plurality of frequency band envelope curves based on the spectrum segmentation boundaries; constructing a zero-phase filter, and carrying out signal reconstruction on each frequency band envelope curve by adopting Inverse Fast Fourier Transform (IFFT) to obtain single-component signals with a plurality of instantaneous frequencies and physical significance; and analyzing the plurality of single-component signals and constructing and obtaining the health index data of the rolling bearing to be monitored.
Wherein, based on a plurality of single component signals, the health index data of the rolling bearing to be monitored can be determined by adopting the prior art, and the method is not particularly limited.
In some embodiments, the single component signal includes a component time domain waveform and a component envelope curve;
accordingly, the determining health index data of the rolling bearing to be monitored based on the plurality of single component signals includes:
and determining the health index data of the rolling bearing to be monitored based on the time domain waveforms of the components and the envelope curves of the components.
The embodiment can analyze each component time domain waveform and each component envelope curve and construct health index data for each component time domain waveform and each component envelope curve.
In some embodiments, the step S104 may include:
and inputting the health index data into a pre-trained bidirectional long-short-term Memory DA-BLSTM (Dual Attention Based Bidirectional Long Short-term Memory) network model based on an attention mechanism to obtain a residual life prediction result of the rolling bearing to be monitored.
In some embodiments, before the health index data is input into the pre-trained two-way long-short term memory DA-BLSTM network model based on the attention mechanism to obtain the residual life prediction result of the rolling bearing to be monitored, the method further comprises:
acquiring a health index data sample set, wherein the health index data sample set comprises a plurality of health index sample data marked with residual life;
clustering The health index data sample set by adopting a gray correlation-based clustering and classification GRACC (The GRA-based Clustering and Classifying) method to obtain a plurality of clusters;
and dividing each cluster into a training set and a testing set, independently training the pre-constructed DA-BLSTM network model based on the training set corresponding to the cluster, and independently testing the trained DA-BLSTM network model based on the testing set corresponding to the cluster to obtain the pre-trained DA-BLSTM network model.
Wherein GRACC is GRA (The Grey Relation Analysis, gray correlation analysis) based clustering and classification.
In this embodiment, the health index data sample set may be clustered by the GRACC method to obtain a plurality of clusters, so that the similarity in each cluster is as large as possible. Illustratively, three clusters, a healthy cluster, a slightly faulty cluster, and a severely faulty cluster, respectively, may be divided.
Each cluster is divided into a training set and a testing set, for example, 80% of each cluster is used as the training set, 20% of each cluster is used as the testing set, an existing training method can be adopted, and each cluster is independently trained based on the training set and the testing set corresponding to each cluster, so that a pre-trained DA-BLSTM network model is finally obtained.
In some embodiments, the DA-BLSTM network model includes a first layer of convolution, a second layer of convolution, a first layer of maximum pooling, a third layer of convolution, a second layer of maximum pooling, an input attention layer, a bidirectional LSTM (Long Short-term Memory) layer, a directional attention layer, and a fully connected layer that are connected in sequence.
In this embodiment, the input data size of the DA-BLSTM network model may be 400 x 1.
1) The input data sequentially passes through two 1*1 convolution layers (namely a first convolution layer and a second convolution layer) and then passes through a maximum pooling layer (namely a first maximum pooling layer) to respectively obtain characteristic data of 400 x 16, 400 x 32 and 50 x 32;
2) Sequentially passing the 50 x 32 characteristic data obtained in the step 1) through a 1*1 convolution layer (namely a third convolution layer) and a maximum pooling layer (namely a second maximum pooling layer), and circularly processing for three times to respectively obtain 12 x 64, 6 x 256 and 6 x 512 characteristic data;
3) The 6 x 512 characteristic data obtained in the step 2) are input into an attention layer to obtain 6 x 256 characteristic data;
4) The 6 x 256 characteristic data obtained in the step 3) are respectively obtained by a bidirectional LSTM layer to obtain 6 x 32 characteristic data;
5) The 6 x 32 characteristic data obtained in the step 4) pass through a directional attention layer to finally obtain 1 x 16 characteristic data;
6) And 5) the characteristic data of 1×16 obtained in the step 5) are subjected to a full connection layer to finally obtain the data of 1*6.
The railway bearing health monitoring method provided by the application can realize service performance evaluation and state prediction of the rolling bearing under complex operation conditions, and develop a monitoring method integrating detection, diagnosis, prediction and the like, has great significance for guiding the operation and maintenance of the rolling bearing, and can realize high automation and accuracy.
The application adopts PHM (Prognostics and Health Management) technology, which is to collect data information of the system by using a sensor, evaluate, monitor and manage the health state of the system by using an information technology and an artificial intelligent reasoning algorithm, predict the failure of the system before the system fails, and provide a series of maintenance and guarantee suggestions or decisions by combining the existing resource information. The method is a comprehensive technology integrating fault detection, isolation, health assessment, prediction and maintenance decision, and is also an important technology to be applied to research on intelligent operation and maintenance of the rolling bearing. The core of PHM is that a large number of high-end sensors are used to collect data capable of reflecting the running state of the rolling bearing. Then, based on the collected data, the fault cause is found out by means of various scientific algorithms, the fault occurrence position is judged, the residual life of the rolling bearing is predicted, and visualization is realized, so that the health management of the rolling bearing is more convenient.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present application.
The following are device embodiments of the application, for details not described in detail therein, reference may be made to the corresponding method embodiments described above.
Fig. 2 shows a schematic structural diagram of a railway bearing health monitoring device according to an embodiment of the present application, and for convenience of explanation, only the parts related to the embodiment of the present application are shown, and the details are as follows:
as shown in fig. 2, the railroad bearing health monitoring apparatus 30 may include: an acquisition module 31, a health index data extraction module 32, a fault diagnosis module 33, and a life prediction module 34.
An acquisition module 31 for acquiring an original vibration signal of the rolling bearing to be monitored;
the health index data extraction module 32 is configured to determine health index data of the rolling bearing to be monitored according to the original vibration signal by adopting a fourier decomposition method based on a cyclic envelope experience;
the fault diagnosis module 33 is configured to perform fault diagnosis on the rolling bearing to be monitored according to the health index data, so as to obtain a fault diagnosis result of the rolling bearing to be monitored;
and the life prediction module 34 is configured to predict the remaining life of the rolling bearing to be monitored according to the health index data, so as to obtain a predicted result of the remaining life of the rolling bearing to be monitored.
In one possible implementation, the health indicator data extraction module 32 is specifically configured to:
performing fast Fourier transform on the original vibration signal to obtain a corresponding signal frequency spectrum;
carrying out cyclic envelope on the signal spectrum to obtain a spectrum envelope curve;
frequency band segmentation is carried out on the frequency spectrum envelope curves to obtain a plurality of frequency band envelope curves;
constructing a zero-phase filter, and carrying out signal reconstruction on each frequency band envelope curve by adopting inverse fast Fourier transform to obtain a plurality of single-component signals;
health index data of the rolling bearing to be monitored is determined based on the plurality of single component signals.
In one possible implementation, in the health indicator data extraction module 32, the single component signal includes a component time domain waveform and a component envelope curve;
accordingly, determining health indicator data of the rolling bearing to be monitored based on the plurality of single component signals comprises:
and determining the health index data of the rolling bearing to be monitored based on the time domain waveforms of the components and the envelope curves of the components.
In one possible implementation, the lifetime prediction module 34 is specifically configured to:
and inputting the health index data into a pre-trained two-way long-short-term memory DA-BLSTM network model based on an attention mechanism to obtain a residual life prediction result of the rolling bearing to be monitored.
In one possible implementation, the lifetime prediction module 34 is further configured to:
before the health index data are input into a pre-trained two-way long-short-term memory DA-BLSTM network model based on an attention mechanism to obtain a residual life prediction result of a rolling bearing to be monitored, a health index data sample set is obtained, and the health index data sample set comprises a plurality of health index sample data marked with residual life;
clustering the health index data sample set by adopting a gray correlation-based clustering and GRACC classification method to obtain a plurality of clusters;
and dividing each cluster into a training set and a testing set, independently training the pre-constructed DA-BLSTM network model based on the training set corresponding to the cluster, and independently testing the trained DA-BLSTM network model based on the testing set corresponding to the cluster to obtain the pre-trained DA-BLSTM network model.
In one possible implementation, the DA-BLSTM network model includes a first layer of convolution, a second layer of convolution, a first layer of maximum pooling, a third layer of convolution, a second layer of maximum pooling, an input attention layer, a bi-directional LSTM layer, a directional attention layer, and a fully connected layer that are connected in sequence.
Fig. 3 is a schematic diagram of a terminal according to an embodiment of the present application. As shown in fig. 3, the terminal 4 of this embodiment includes: a processor 40 and a memory 41. The memory 41 is used for storing a computer program 42, and the processor 40 is used for calling and running the computer program 42 stored in the memory 41 to execute the steps in the embodiments of the method for monitoring the health of the railway bearing, such as S101 to S104 shown in fig. 1. Alternatively, the processor 40 is configured to invoke and run the computer program 42 stored in the memory 41 to implement the functions of the modules/units in the above-described device embodiments, such as the functions of the modules/units 31 to 34 shown in fig. 2.
Illustratively, the computer program 42 may be partitioned into one or more modules/units that are stored in the memory 41 and executed by the processor 40 to complete the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program 42 in the terminal 4. For example, the computer program 42 may be split into the modules/units 31 to 34 shown in fig. 2.
The terminal 4 may be a computer, a server, a raspberry group or the like. The terminal 4 may include, but is not limited to, a processor 40, a memory 41. It will be appreciated by those skilled in the art that fig. 3 is merely an example of the terminal 4 and is not limiting of the terminal 4, and may include more or fewer components than shown, or may combine some components, or different components, e.g., the terminal may further include input and output devices, network access devices, buses, etc.
The processor 40 may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 41 may be an internal storage unit of the terminal 4, such as a hard disk or a memory of the terminal 4. The memory 41 may also be an external storage device of the terminal 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the terminal 4. Further, the memory 41 may also include both an internal storage unit and an external storage device of the terminal 4. The memory 41 is used for storing the computer program as well as other programs and data required by the terminal. The memory 41 may also be used for temporarily storing data that has been output or is to be output.
Corresponding to the terminal, the embodiment of the application also provides a railway bearing health monitoring system which comprises an accelerometer, a collecting card, a power adapter and the terminal;
the accelerometer is connected with a GPIO (General-purpose input/output) port of the terminal through the acquisition card; the power adapter is connected with a power interface of the terminal;
wherein the terminal is raspberry pie.
The accelerometer is directly connected with the acquisition card, and a data line connecting the accelerometer and the acquisition card can use BNC to UNF10-32 cables. The acquisition card is directly connected with the GPIO port of the terminal, and the 2 x 20 socket with the expansion lead can be used for connecting the acquisition card and the GPIO port of the terminal.
The accelerometer may be an IEPE acceleration sensor. The acquisition card may be an MCC172 acquisition card. The raspberry pie can use the 4 th generation of B model of raspberry pie, and the burnt operating system can use a Linux system, and the raspberry pie comprises a central processing unit, and a memory, an SD card, a wifi module, a power interface and a GPIO interface which are respectively connected with the central processing unit. The power adapter may use a conversion rate of 5V-1500 mA.
Compared with the traditional rolling bearing fault detection means, the method has the advantages that the complex functions such as data acquisition, state monitoring, fault diagnosis and life prediction can be realized only by constructing a monitoring system through raspberry pie and related sensor peripherals, the cost is greatly reduced, and the complex functions are realized at low cost.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions. The functional units and modules in the embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit, where the integrated units may be implemented in a form of hardware or a form of a software functional unit. In addition, the specific names of the functional units and modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working process of the units and modules in the above system may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and in part, not described or illustrated in any particular embodiment, reference is made to the related descriptions of other embodiments.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal and method may be implemented in other manners. For example, the apparatus/terminal embodiments described above are merely illustrative, e.g., the division of the modules or units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection via interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the present application may also be implemented by implementing all or part of the procedures in the methods of the embodiments described above, or by instructing the relevant hardware by a computer program, which may be stored in a computer readable storage medium, and which, when executed by a processor, may implement the steps of the embodiments of the methods of monitoring health of a railway bearing described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium may include content that is subject to appropriate increases and decreases as required by jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is not included as electrical carrier signals and telecommunication signals.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application.
Claims (10)
1. A method of monitoring health of a railway bearing, comprising:
acquiring an original vibration signal of a rolling bearing to be monitored;
determining health index data of the rolling bearing to be monitored according to the original vibration signal by adopting a Fourier decomposition method based on a cyclic envelope experience;
performing fault diagnosis on the rolling bearing to be monitored according to the health index data to obtain a fault diagnosis result of the rolling bearing to be monitored;
and predicting the residual life of the rolling bearing to be monitored according to the health index data to obtain a residual life prediction result of the rolling bearing to be monitored.
2. The method for monitoring the health of a rolling bearing according to claim 1, wherein determining the health index data of the rolling bearing to be monitored according to the original vibration signal by adopting a cyclic envelope-based empirical fourier decomposition method comprises:
performing fast Fourier transform on the original vibration signal to obtain a corresponding signal frequency spectrum;
performing cyclic envelope on the signal spectrum to obtain a spectrum envelope curve;
performing band segmentation on the spectrum envelope curves to obtain a plurality of band envelope curves;
constructing a zero-phase filter, and carrying out signal reconstruction on each frequency band envelope curve by adopting inverse fast Fourier transform to obtain a plurality of single-component signals;
and determining health index data of the rolling bearing to be monitored based on the plurality of single component signals.
3. The method of claim 2, wherein the single component signal comprises a component time domain waveform and a component envelope curve;
accordingly, the determining health indicator data of the rolling bearing to be monitored based on the plurality of single component signals includes:
and determining the health index data of the rolling bearing to be monitored based on the time domain waveforms of the components and the envelope curves of the components.
4. A method of monitoring the health of a railway bearing according to any one of claims 1 to 3, wherein predicting the remaining life of the rolling bearing to be monitored based on the health index data to obtain a predicted result of the remaining life of the rolling bearing to be monitored comprises:
and inputting the health index data into a pre-trained two-way long-short-term memory DA-BLSTM network model based on an attention mechanism to obtain a residual life prediction result of the rolling bearing to be monitored.
5. The method for monitoring the health of a rolling bearing according to claim 4, wherein before inputting the health index data into a pre-trained attention mechanism-based two-way long-short-term memory DA-BLSTM network model to obtain a predicted result of the remaining life of the rolling bearing to be monitored, the method further comprises:
acquiring a health index data sample set, wherein the health index data sample set comprises a plurality of health index sample data marked with residual life;
clustering the health index data sample set by adopting a gray correlation-based clustering and GRACC classification method to obtain a plurality of clusters;
and dividing each cluster into a training set and a testing set, independently training the pre-constructed DA-BLSTM network model based on the training set corresponding to the cluster, and independently testing the trained DA-BLSTM network model based on the testing set corresponding to the cluster to obtain the pre-trained DA-BLSTM network model.
6. The method of claim 4, wherein the DA-stm network model comprises a first layer of convolution, a second layer of convolution, a first layer of maximum pooling, a third layer of convolution, a second layer of maximum pooling, an input attention layer, a bi-directional LSTM layer, a directional attention layer, and a fully connected layer connected in sequence.
7. A railroad bearing health monitoring apparatus, comprising:
the acquisition module is used for acquiring an original vibration signal of the rolling bearing to be monitored;
the health index data extraction module is used for determining health index data of the rolling bearing to be monitored according to the original vibration signal by adopting a cyclic envelope experience Fourier decomposition method;
the fault diagnosis module is used for carrying out fault diagnosis on the rolling bearing to be monitored according to the health index data to obtain a fault diagnosis result of the rolling bearing to be monitored;
and the life prediction module is used for predicting the residual life of the rolling bearing to be monitored according to the health index data to obtain a residual life prediction result of the rolling bearing to be monitored.
8. A terminal comprising a processor and a memory, the memory for storing a computer program, the processor for invoking and running the computer program stored in the memory to perform the method of railroad bearing health monitoring as set forth in any one of claims 1 through 6.
9. A railway bearing health monitoring system comprising an accelerometer, a pick-up card, a power adapter and the terminal of claim 8;
the accelerometer is connected with a GPIO port of the terminal through the acquisition card; the power adapter is connected with a power interface of the terminal;
wherein the terminal is raspberry pie.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the railway bearing health monitoring method according to any one of the preceding claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310399146.7A CN116642696A (en) | 2023-04-14 | 2023-04-14 | Railway bearing health monitoring method, device, system, terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310399146.7A CN116642696A (en) | 2023-04-14 | 2023-04-14 | Railway bearing health monitoring method, device, system, terminal and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116642696A true CN116642696A (en) | 2023-08-25 |
Family
ID=87638826
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310399146.7A Pending CN116642696A (en) | 2023-04-14 | 2023-04-14 | Railway bearing health monitoring method, device, system, terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116642696A (en) |
-
2023
- 2023-04-14 CN CN202310399146.7A patent/CN116642696A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108985279B (en) | Fault diagnosis method and device for MVB waveform of multifunctional vehicle bus | |
| CN111459700B (en) | Equipment fault diagnosis method, diagnosis device, diagnosis equipment and storage medium | |
| CN111241154A (en) | Storage battery fault early warning method and system based on big data | |
| CN101999101B (en) | The defining method of system cloud gray model prediction | |
| CN115170000B (en) | Remote monitoring method and system based on electric energy meter communication module | |
| CN114267178A (en) | Intelligent operation maintenance method and device for station | |
| CN110837852A (en) | Fault diagnosis method and device for rolling mill gearbox and terminal equipment | |
| CN113125135A (en) | Fault diagnosis method for rotary machine, storage medium, and electronic device | |
| CN115437358A (en) | Intelligent state monitoring and fault diagnosis system and fault diagnosis method for industrial robot | |
| CN114048787B (en) | Method and system for intelligently diagnosing bearing fault in real time based on Attention CNN model | |
| CN113740635A (en) | Electrical equipment fault diagnosis method, terminal and multi-probe sensing device | |
| CN114511026A (en) | Fault diagnosis method and device, terminal equipment and storage medium | |
| CN111007452A (en) | Fault diagnosis method and device of data acquisition system | |
| CN113392874B (en) | Abnormal state diagnosis method and device for rail vehicle and terminal equipment | |
| CN112729884B (en) | Equipment fault diagnosis method and device based on big data | |
| US20140032079A1 (en) | Diagnostic and control system and method | |
| CN117331790A (en) | Machine room fault detection method and device for data center | |
| CN116642696A (en) | Railway bearing health monitoring method, device, system, terminal and storage medium | |
| CN114492636B (en) | Transformer winding state signal acquisition system | |
| CN116108376A (en) | Monitoring system and method for preventing electricity stealing, electronic equipment and medium | |
| CN113516023A (en) | Equipment vibration abnormality diagnosis method and system | |
| CN113168739A (en) | Method for checking at least one vehicle and electronic computing device | |
| CN111190922A (en) | Power state detection method, device, equipment and storage medium | |
| CN113514713B (en) | Method and device for detecting performance of traction converter of motor train unit and terminal equipment | |
| CN117369425B (en) | Automobile instrument assembly fault diagnosis method, system, storage medium and computer |
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 |