CN106934126B - Mechanical part health index construction method based on recurrent neural network fusion - Google Patents

Mechanical part health index construction method based on recurrent neural network fusion Download PDF

Info

Publication number
CN106934126B
CN106934126B CN201710109877.8A CN201710109877A CN106934126B CN 106934126 B CN106934126 B CN 106934126B CN 201710109877 A CN201710109877 A CN 201710109877A CN 106934126 B CN106934126 B CN 106934126B
Authority
CN
China
Prior art keywords
neural network
characteristic
mechanical part
recurrent neural
vibration signal
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201710109877.8A
Other languages
Chinese (zh)
Other versions
CN106934126A (en
Inventor
雷亚国
牛善涛
郭亮
李乃鹏
林京
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xian Jiaotong University
Original Assignee
Xian Jiaotong University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Xian Jiaotong University filed Critical Xian Jiaotong University
Priority to CN201710109877.8A priority Critical patent/CN106934126B/en
Publication of CN106934126A publication Critical patent/CN106934126A/en
Application granted granted Critical
Publication of CN106934126B publication Critical patent/CN106934126B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/17—Mechanical parametric or variational design
    • 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
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/04—Ageing analysis or optimisation against ageing
    • 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)
  • Geometry (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Evolutionary Computation (AREA)
  • Health & Medical Sciences (AREA)
  • Biophysics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Analysis (AREA)
  • Computational Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Computer Hardware Design (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Testing Of Devices, Machine Parts, Or Other Structures Thereof (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A mechanical part health index construction method based on recurrent neural network fusion comprises the steps of firstly obtaining a mechanical part vibration signal, and calculating to obtain a vibration signal time domain characteristic sequence and a vibration signal frequency domain characteristic sequence; calculating similarity characteristics according to the time domain characteristic sequence and the frequency domain characteristic sequence; carrying out three-layer wavelet packet transformation on the vibration signal to obtain a frequency band energy ratio characteristic; screening out a sensitive feature set of the mechanical part degradation process by utilizing the comprehensive evaluation indexes of the features, wherein the sensitive feature set is used for training a recurrent neural network; the method can obtain a new mechanical part health index RNN-HI through the sensitive feature set and the trained recurrent neural network, fully excavates degradation information in a mechanical part vibration signal by utilizing the similarity feature and the recurrent neural network, and not only is the determination of a failure threshold value convenient, but also the accuracy of life prediction is improved.

Description

Mechanical part health index construction method based on recurrent neural network fusion
Technical Field
The invention belongs to the technical field of mechanical equipment state monitoring and service life prediction, and particularly relates to a mechanical part health index construction method based on recurrent neural network fusion.
Background
In modern industrial production, mechanical equipment is more and more complicated, the connection between mechanical parts is more and more compact, and because the operating environment is complicated and changeable, mechanical parts are easy to have faults of different degrees, so that the whole mechanical equipment cannot work, and even economic loss and serious accidents which are difficult to estimate are caused. Therefore, the working state is effectively identified through the state monitoring of the mechanical parts, the accurate prediction of the residual service life is further realized, and the optimal maintenance time of the mechanical equipment is determined according to the effective identification, so that the mechanical equipment is effectively prevented before the mechanical equipment breaks down, and the service life of the mechanical parts is prolonged. The structure of the health index is a crucial link in the state monitoring and life prediction of mechanical parts, and the quality of the health index directly influences the accuracy of the life prediction in the later period.
The traditional health index structure is mainly extracted from an original signal by using a modern signal processing method, and is closely related to the degradation process of mechanical parts in a physical sense, such as root mean square value, peak-to-peak value and the like. Generally, the value range of the health index is greatly influenced by the working condition, so that the difficulty of determining the failure threshold of the mechanical part under the variable working condition is increased; a single health indicator is often sensitive to a particular degradation stage of a mechanical part and does not exhibit a good trend over the life cycle. Meanwhile, as the vibration signals of the mechanical parts have complex variability, the recession process of the mechanical parts is difficult to effectively reflect by directly using the traditional health indexes. The above-mentioned disadvantages may cause early failure to be difficult to find in time during the monitoring process of the state of the mechanical component, and then affect the accuracy of the life prediction.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide a mechanical part health index construction method based on the fusion of a recurrent neural network, which fuses a plurality of health indexes into one through an information fusion method, fully excavates state information in a mechanical part vibration signal, realizes accurate expression of a degradation process, and can control the value of the health index within a constant range to facilitate the determination of a failure threshold value.
In order to achieve the purpose, the invention adopts the technical scheme that:
a mechanical part health index construction method based on recurrent neural network fusion comprises the following steps:
1) acquiring a vibration signal of the mechanical part, calculating a time domain characteristic of the vibration signal, and obtaining a time domain characteristic sequence f1 of the mechanical part (f 1)1,f12,...,f111) Wherein f11，f12，…，f111Mean value, root mean square value, peak-to-peak value, variance, entropy, kurtosis index, skewness index, peak index, wave form factor, pulse index and margin index; fourier transform is carried out on the vibration signal to obtain a global spectrum characteristic sequence f2, and the global spectrum characteristic sequence is divided averagely to obtain four sub-spectrum characteristic sequences f3, f4, f5 and f6 from low frequency to high frequency;
2) sequentially carrying the six characteristic sequences obtained in the step 1) into a formula (1) to calculate and obtain the similarity characteristics of the mechanical parts:
Figure BDA0001234128150000021
wherein K is the length of the characteristic sequence,
Figure BDA0001234128150000022
and
Figure BDA0001234128150000023
respectively, the mean values of the characteristic sequences at the initial time and at the time t,
Figure BDA0001234128150000024
and ft iThe obtained similarity features are sequentially marked as F1, F2, F3, F4, F5 and F6, wherein the values are the ith component of the feature sequence at the initial time and the t time respectively;
3) three-layer wavelet packet transformation is carried out on the vibration signal, and the energy ratio characteristics of eight frequency bands are obtained through calculation according to the formula (2):
Figure BDA0001234128150000025
wherein Ei( i 1, 2.., 8) is the energy value of each frequency band of the wavelet packet,
Figure BDA0001234128150000026
the obtained band energy bit characteristics are sequentially marked as F7, F8, F9, F10, F11, F12, F13 and F14 for the energy sum of all wavelet packet bands;
4) and (3) respectively calculating comprehensive evaluation indexes of six similarity characteristics and eight frequency band energy ratio characteristics F1-F14 of the mechanical parts according to the formula (3):
Figure BDA0001234128150000031
in the formula:
Figure BDA0001234128150000032
Figure BDA0001234128150000033
wherein, FlAnd tlRespectively the characteristic value and time of the ith observation point,
Figure BDA0001234128150000034
and
Figure BDA0001234128150000035
the method comprises the steps that the average values of a characteristic sequence and a time sequence in a full life cycle are respectively, L is the length of the characteristic sequence in the full life cycle, epsilon (x) is a unit step function, Corr measures the linear correlation degree of the characteristic sequence and the time sequence, and Mon measures the monotonicity of the characteristic sequence;
5) standardizing the comprehensive evaluation index Cri of the fourteen calculated characteristics to obtain Cri ', and selecting the characteristics with Cri' being more than 0.5 as a sensitive characteristic set in the degradation process of the mechanical parts;
6) utilizing life-cycle data sets
Figure BDA0001234128150000036
Training a recurrent neural network model, where xt∈RN×1Set of sensitivity features for time t, yt∈[0,1]Is the actual degradation rate of the mechanical parts at the corresponding moment, y t0 represents a mechanical part in a fully healthy state, y t1 represents that the cyclic neural network is in a complete fault state, and the cost function in the training process of the cyclic neural network is shown as the formula (6):
Figure BDA0001234128150000037
wherein the content of the first and second substances,
Figure BDA0001234128150000038
as an output result of the recurrent neural network model, ytIs the true degradation rate;
7) and in the testing stage, the sensitivity characteristic set obtained by calculation is used as the input of the trained recurrent neural network to obtain a new mechanical part health index RNN-HI.
The invention has the beneficial effects that:
the method constructs a new similarity characteristic used for representing the degree of the mechanical parts deviating from the normal state, screens out the sensitive characteristic set of the mechanical parts in the degradation process through comprehensive evaluation indexes of the characteristic, and obtains a new mechanical parts health index RNN-HI by fully fusing and extracting state information in the sensitive characteristic set through a recurrent neural network, so that the degradation trend of the mechanical parts can be well reflected, the obtained health index is used for life prediction, and the accuracy of the prediction result is improved.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic representation of similarity features derived from a sequence of spectral features.
FIG. 3 is a schematic structural diagram of a long-term and short-term memory model.
Fig. 4 shows life-cycle vibration signals of the rolling bearing1_1 and the rolling bearing2_ 6.
Fig. 5 shows the normalized overall evaluation index.
Fig. 6 shows the life prediction result of the rolling bearing2_ 6.
Detailed Description
The invention is further elucidated with reference to the figures and embodiments.
Referring to fig. 1, a method for constructing a mechanical part health index based on recurrent neural network fusion includes the following steps:
1) acquiring a vibration signal of the mechanical part, calculating the time domain characteristic of the vibration signal of the mechanical part, and obtaining a time domain characteristic sequence f1 of the mechanical part (f 1)1,f12,...,f111) Wherein f11，f12，…，f111Mean value, root mean square value, peak-to-peak value, variance, entropy, kurtosis index, skewness index, waveform factor, pulse index and margin index; fourier transform is carried out on the vibration signal to obtain a global spectrum characteristic sequence f2, and the global spectrum characteristic sequence is divided averagely to obtain four sub-spectrum characteristic sequences f3, f4, f5 and f6 from low frequency to high frequency;
2) sequentially carrying the six characteristic sequences obtained in the step 1) into a formula (1) to calculate and obtain the similarity characteristics of the mechanical parts:
Figure BDA0001234128150000051
wherein K is the length of the characteristic sequence,
Figure BDA0001234128150000052
and
Figure BDA0001234128150000053
respectively, the mean values of the characteristic sequences at the initial time and at the time t,
Figure BDA0001234128150000054
and ft iThe obtained similarity characteristics are sequentially recorded as F1, F2, F3, F4, F5 and F6 respectively for the value of the ith component of the characteristic sequence at the initial time and the t time, if the states of the mechanical parts at the two times are close, the similarity characteristics are approximate to 1, otherwise, the similarity characteristics are approximate to 0, and FIG. 2 is a phase obtained from a spectrum characteristic sequenceA similarity characteristic diagram;
3) three-layer wavelet packet transformation is carried out on the vibration signal, and the energy ratio characteristics of eight frequency bands are obtained through calculation according to the formula (2):
Figure BDA0001234128150000055
wherein Ei( i 1, 2.., 8) is the energy value of each frequency band of the wavelet packet,
Figure BDA0001234128150000056
the obtained band energy bit characteristics are sequentially marked as F7, F8, F9, F10, F11, F12, F13 and F14 for the energy sum of all wavelet packet bands;
4) and (3) respectively calculating comprehensive evaluation indexes of six similarity characteristics and eight frequency band energy ratio characteristics F1-F14 of the mechanical parts according to the formula (3):
Figure BDA0001234128150000057
in the formula:
Figure BDA0001234128150000058
Figure BDA0001234128150000059
wherein, FlAnd tlRespectively the characteristic value and time of the ith observation point,
Figure BDA0001234128150000061
and
Figure BDA0001234128150000062
the method comprises the steps that the average values of a characteristic sequence and a time sequence in a full life cycle are respectively, L is the length of the characteristic sequence in the full life cycle, epsilon (x) is a unit step function, Corr measures the linear correlation degree of the characteristic sequence and the time sequence, and Mon measures the monotonicity of the characteristic sequence;
5) standardizing the comprehensive evaluation index Cri of the fourteen calculated characteristics to obtain Cri ', and selecting the characteristics with Cri' being more than 0.5 as a sensitive characteristic set in the degradation process of the mechanical parts;
6) utilizing life-cycle data sets
Figure BDA0001234128150000063
Training a recurrent neural network model, where xt∈RN×1Set of sensitivity features for time t, yt∈[0,1]Is the actual degradation rate of the mechanical parts at the corresponding moment, y t0 represents a mechanical part in a fully healthy state, y t1 represents that the cyclic neural network is in a complete fault state, and the cost function in the training process of the cyclic neural network is shown as the formula (6):
Figure BDA0001234128150000064
wherein the content of the first and second substances,
Figure BDA0001234128150000065
as an output result of the recurrent neural network model, ytFor the real degradation rate, an improved recurrent neural network-long-short-term memory model is used, the problem of gradient explosion and disappearance in the recurrent neural network is solved, fig. 3 is a schematic structural diagram of the long-short-term memory model, and the biggest characteristic is that valve nodes of each layer are added on the basis of the recurrent neural network structure, and are used for judging whether information in a memory unit is added into the calculation of the layer, and a calculation formula of each part is given according to a calculation sequence:
gt＝φ(wgxxt+wghht-1+bg) (7)
it＝σ(wixxt+wihht-1+bi) (8)
ft＝σ(wfxxt+wfhht-1+bf) (9)
ot＝σ(woxxt+wohht-1+bo) (10)
Figure BDA0001234128150000066
Figure BDA0001234128150000067
wherein, wgx、wix,、wfxAnd woxInput layers x at time ttAnd an output layer htWeight value of between, wgh、wih、wfhAnd wohIs the weight between t and t-1 times of the hidden layer, bg、bi、bfAnd boIs a deviation, ht-1Output value of hidden layer at previous time gt,，it，ftAnd otOutput values, s, of input node, input gate, forget gate and output gate, respectivelytAnd st-1The values of the internal states at time t and the previous time, respectively, σ (x) is the gate activation function, φ (x) is the input activation function, h (x) is the output activation function;
7) and in the testing stage, the sensitivity characteristic set obtained by calculation is used as the input of the trained recurrent neural network to obtain a new mechanical part health index RNN-HI.
In order to further prove the effectiveness of the mechanical part health index construction method based on the recurrent neural network fusion, the experimental data of the accelerated life of the rolling bearing of the PRONOSTIA test bed is used for verification.
The PRONOSTIA test bed provides life cycle data of the rolling bearing from a normal state to complete failure, the rolling bearing used in the test is not provided with an initial fault, pressure is provided for the outer ring of the rolling bearing through the air cylinder so that the whole degradation process can be accelerated and completed within a few hours, each degraded rolling bearing can contain any fault mode, and the acceleration sensor is used for collecting data in the vertical direction and the horizontal direction of the outer ring of the rolling bearing. In the experimental process, the sampling frequency is 25600Hz, the number of sampling points is 2560, the sampling time is 0.1s each time, and the time interval of two times of sampling is 10 s. The three groups of experimental data respectively correspond to different rotating speeds and loads, the rotating speeds of bearing1_ 1-bearing 1_7 are 1800rpm, and the loads are 4000N; the rotation speed of bearing2_ 1-bearing 2_7 is 1650rpm, and the load is 4200N; the rotation speed of bearing3_ 1-bearing 3_3 is 1650rpm, the load is 4200N, and each group of experimental data is divided into a training set and a test set. Fig. 4 shows life-cycle vibration signals of the rolling bearing1_1 for training and the rolling bearing2_6 for testing.
And (3) carrying out feature extraction on the rolling bearing vibration signals of the training set to obtain feature sets F1-F14, calculating corresponding comprehensive evaluation indexes, wherein the normalized comprehensive evaluation index Cri' is shown in FIG. 5. It can be seen that the overall evaluation index after F1, F2, F3, F9, F10, F11, F12 and F14 standardization is greater than 0.5, which should be taken as a sensitive feature set, wherein F1, F2 and F3 are the proposed similarity features, and prove the effectiveness of the similarity features in describing the degeneration process. Sensitive feature sets of training set data are extracted to train a recurrent neural network to obtain corresponding health indexes RNN-HI, and for comparison, health indexes SOM-HI based on a self-organizing neural network proposed by Huang are extracted, wherein the comprehensive evaluation index of RNN-HI is 0.7474, the comprehensive evaluation index of SOM-HI is 0.6877, and the two have good effects on describing the degradation trend of a rolling bearing, however, the SOM-HI has no constant failure threshold under three working conditions, the failure threshold of RNN-HI is approximate to 1, and the effect of RNN-HI is better than that of SOM-HI.
In order to verify the effectiveness of RNN-HI in the life prediction of the rolling bearing, the life prediction is carried out by using a dual-exponential life prediction model based on particle filtering, wherein the dual-exponential life prediction model is shown as the following formula:
Y＝aebt+cedt
in the formula, Y is a rolling bearing health index RNN-HI, t is time, a, b, c and d are model parameters, and a particle filter method is used for updating the parameters. As shown in fig. 6, taking the test rolling bearing2_6 as an example, the predicted remaining life median 1470s and the confidence interval of the confidence level of 0.95 are [720s,1690s ].
The life prediction results of all rolling bearings in the test set are shown in table 1, and the life prediction precision is measured by the average percentage error:
Figure BDA0001234128150000081
wherein, ActRILiAnd RULiThe actual remaining life and predicted remaining life of the ith test bearing, respectively. Meanwhile, by comparison, the SOM-HI is used for predicting the residual life of the rolling bearing, corresponding prediction results are given in the table 1, the average percentage error of the RNN-HI is 32.48%, the average percentage error of the SOM-HI is 53.24%, and the mechanical part health index construction method based on the recurrent neural network fusion has higher precision in the residual life prediction.
TABLE 1
Figure BDA0001234128150000091

Claims (1)

1. A mechanical part health index construction method based on recurrent neural network fusion is characterized by comprising the following steps:
1) acquiring a vibration signal of the mechanical part, calculating a time domain characteristic of the vibration signal, and obtaining a time domain characteristic sequence f1 of the mechanical part (f 1)1,f12,...,f111) Wherein f11，f12，…，f111Mean value, root mean square value, peak-to-peak value, variance, entropy, kurtosis index, skewness index, peak index, wave form factor, pulse index and margin index; fourier transform is carried out on the vibration signal to obtain a global spectrum characteristic sequence f2, and the global spectrum characteristic sequence is divided averagely to obtain four sub-spectrum characteristic sequences f3, f4, f5 and f6 from low frequency to high frequency;
2) sequentially carrying the six characteristic sequences obtained in the step 1) into a formula (1) to calculate and obtain the similarity characteristics of the mechanical parts:
Figure FDA0002220142260000011
wherein K is the length of the characteristic sequence,
Figure FDA0002220142260000012
and
Figure FDA0002220142260000013
respectively, the mean values of the characteristic sequences at the initial time and at the time t,
Figure FDA0002220142260000014
and ft iThe obtained similarity features are sequentially marked as F1, F2, F3, F4, F5 and F6, wherein the values are the ith component of the feature sequence at the initial time and the t time respectively;
3) three-layer wavelet packet transformation is carried out on the vibration signal, and the energy ratio characteristics of eight frequency bands are obtained through calculation according to the formula (2):
Figure FDA0002220142260000015
wherein EiThe energy values of the respective frequency bands of the wavelet packet, i ═ 1, 2., 8,
Figure FDA0002220142260000016
the obtained band energy bit characteristics are sequentially marked as F7, F8, F9, F10, F11, F12, F13 and F14 for the energy sum of all wavelet packet bands;
4) and (3) respectively calculating comprehensive evaluation indexes of six similarity characteristics and eight frequency band energy ratio characteristics F1-F14 of the mechanical parts according to the formula (3):
Figure FDA0002220142260000021
in the formula:
Figure FDA0002220142260000022
Figure FDA0002220142260000023
wherein, FlAnd tlCharacteristic values and times, F, of the l-th observation point, respectivelyl+1Is the characteristic value of the (l + 1) th observation point,
Figure FDA0002220142260000024
and
Figure FDA0002220142260000025
the method comprises the steps that the average values of a characteristic sequence and a time sequence in a full life cycle are respectively, L is the length of the characteristic sequence in the full life cycle, epsilon (x) is a unit step function, Corr measures the linear correlation degree of the characteristic sequence and the time sequence, and Mon measures the monotonicity of the characteristic sequence;
5) standardizing the comprehensive evaluation index Cri of the fourteen calculated characteristics to obtain Cri ', and selecting the characteristics with Cri' being more than 0.5 as a sensitive characteristic set in the degradation process of the mechanical parts;
6) utilizing life-cycle data sets
Figure FDA0002220142260000026
Training a recurrent neural network model, where xt∈RN×1Set of sensitivity features for time t, yt∈[0,1]Is the actual degradation rate of the mechanical parts at the corresponding moment, yt0 represents a mechanical part in a fully healthy state, yt1 represents that the cyclic neural network is in a complete fault state, and the cost function in the training process of the cyclic neural network is shown as the formula (6):
Figure FDA0002220142260000027
wherein the content of the first and second substances,
Figure FDA0002220142260000028
as an output result of the recurrent neural network model, ytIs the true degradation rate;
7) and in the testing stage, the sensitivity characteristic set obtained by calculation is used as the input of the trained recurrent neural network to obtain a new mechanical part health index RNN-HI.
CN201710109877.8A 2017-02-28 2017-02-28 Mechanical part health index construction method based on recurrent neural network fusion Active CN106934126B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710109877.8A CN106934126B (en) 2017-02-28 2017-02-28 Mechanical part health index construction method based on recurrent neural network fusion

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710109877.8A CN106934126B (en) 2017-02-28 2017-02-28 Mechanical part health index construction method based on recurrent neural network fusion

Publications (2)

Publication Number Publication Date
CN106934126A CN106934126A (en) 2017-07-07
CN106934126B true CN106934126B (en) 2020-03-31

Family

ID=59423253

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710109877.8A Active CN106934126B (en) 2017-02-28 2017-02-28 Mechanical part health index construction method based on recurrent neural network fusion

Country Status (1)

Country Link
CN (1) CN106934126B (en)

Families Citing this family (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108303253B (en) * 2017-12-06 2019-10-18 华南理工大学 Bearing initial failure recognition methods based on long short-term memory Recognition with Recurrent Neural Network
CN108536939A (en) * 2018-03-29 2018-09-14 上海交通大学 A kind of crane locomotive bridge longevity prediction technique and system
CN108760266B (en) * 2018-05-31 2019-11-26 西安交通大学 The virtual degeneration index building method of mechanical key component based on learning distance metric
CN108960077A (en) * 2018-06-12 2018-12-07 南京航空航天大学 A kind of intelligent failure diagnosis method based on Recognition with Recurrent Neural Network
CN108747590A (en) * 2018-06-28 2018-11-06 哈尔滨理工大学 A kind of tool wear measurement method based on rumble spectrum and neural network
CN109143856A (en) * 2018-07-31 2019-01-04 佛山科学技术学院 Adaptive health indicator extracting method based on depth recurrent neural network
CN109726524B (en) * 2019-03-01 2022-11-01 哈尔滨理工大学 CNN and LSTM-based rolling bearing residual service life prediction method
CN110633792B (en) * 2019-10-22 2022-03-22 西安交通大学 End-to-end bearing health index construction method based on convolution cyclic neural network
CN111680661A (en) * 2020-06-19 2020-09-18 哈尔滨工业大学 Mechanical rotating part performance degradation tracking method based on multi-feature fusion
CN113779699A (en) * 2021-09-14 2021-12-10 树根互联股份有限公司 Excavator fault prediction method and system, electronic equipment and storage medium

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5214271A (en) * 1991-03-21 1993-05-25 Ncr Corporation Method of determining detector lifetime using a stepped resistor network
WO2013187813A1 (en) * 2012-06-13 2013-12-19 Telefonaktiebolaget L M Ericsson (Publ) Handover prediction using historical data
CN104598734A (en) * 2015-01-22 2015-05-06 西安交通大学 Life prediction model of rolling bearing integrated expectation maximization and particle filter
CN104778340A (en) * 2015-05-07 2015-07-15 东南大学 Bearing life prediction method based on enhanced particle filter
CN106053066A (en) * 2016-05-23 2016-10-26 华东交通大学 Antifriction bearing performance degradation assessment method based on empirical mode decomposition and logistic regression

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5214271A (en) * 1991-03-21 1993-05-25 Ncr Corporation Method of determining detector lifetime using a stepped resistor network
WO2013187813A1 (en) * 2012-06-13 2013-12-19 Telefonaktiebolaget L M Ericsson (Publ) Handover prediction using historical data
CN104598734A (en) * 2015-01-22 2015-05-06 西安交通大学 Life prediction model of rolling bearing integrated expectation maximization and particle filter
CN104778340A (en) * 2015-05-07 2015-07-15 东南大学 Bearing life prediction method based on enhanced particle filter
CN106053066A (en) * 2016-05-23 2016-10-26 华东交通大学 Antifriction bearing performance degradation assessment method based on empirical mode decomposition and logistic regression

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring;Zhigang Tian;《J Intell Manuf》;20121231;第227-237页 *
Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data;Feng Jia 等;《Mechanical Systems and Signal Processing》;20160531;第72卷;第303-315页 *
Recurrent neural networks for remaining useful life estimation;Felix O. Heimes 等;《2008 International Conference on Prognostics and Health Management》;20081231;第1-6页 *
机械系统旋转部件退化跟踪与故障预测方法研究;钱宇宁;《中国博士学位论文全文数据库 工程科技Ⅱ辑》;20170215(第02期);第C029-52页 *

Also Published As

Publication number Publication date
CN106934126A (en) 2017-07-07

Similar Documents

Publication Publication Date Title
CN106934126B (en) Mechanical part health index construction method based on recurrent neural network fusion
CN106769052B (en) A kind of mechanical system rolling bearing intelligent failure diagnosis method based on clustering
Shi et al. Rolling bearing initial fault detection using long short-term memory recurrent network
CN109460618B (en) Rolling bearing residual life online prediction method and system
CN110376522B (en) Motor fault diagnosis method of data fusion deep learning network
CN110261109B (en) Rolling bearing fault diagnosis method based on bidirectional memory cyclic neural network
CN108256556A (en) Wind-driven generator group wheel box method for diagnosing faults based on depth belief network
CN109827777A (en) Rolling bearing fault prediction technique based on Partial Least Squares extreme learning machine
CN108168924B (en) Reciprocating compressor service life prediction method based on VMD (model-based decomposition) and MFSS (model-based decomposition) models
CN104198184A (en) Bearing fault diagnostic method based on second generation wavelet transform and BP neural network
CN103115789A (en) Second generation small-wave support vector machine assessment method for damage and remaining life of metal structure
CN110059845B (en) Metering device clock error trend prediction method based on time sequence evolution gene model
CN109766941B (en) Farmland data fusion processing method based on multiple sensors
CN105424366A (en) Bearing fault diagnosis method based on EEMD adaptive denoising
CN102721397A (en) Method for extracting road surface characteristic parameters based on modern time series of vertical dynamic load
CN113947017A (en) Method for predicting residual service life of rolling bearing
CN114705432B (en) Method and system for evaluating health state of explosion-proof motor bearing
TWI491801B (en) Wind power fault prediction system and method thereof
Chen et al. An adversarial learning framework for zero-shot fault recognition of mechanical systems
CN110887664B (en) Method and device for establishing bearing fault identification model
CN108615018A (en) Object state identification method based on the extraction of time domain histogram feature
CN111950757A (en) Method for predicting degradation state trend of key parts of rotary machine
CN114137915A (en) Fault diagnosis method for industrial equipment
CN113487141A (en) Bearing state evaluation method based on multi-source information clustering fusion
Wang et al. Remaining Life Prediction for High-speed Rail Bearing Considering Hybrid Data-model-driven Approach

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant