Research on a fault diagnosis method of rolling bearings using variation mode decomposition and deep belief network

2019 ◽  
Vol 33 (9) ◽  
pp. 4165-4172 ◽  
Author(s):  
Huimin Zhao ◽  
Hailong Liu ◽  
Junjie Xu ◽  
Chen Guo ◽  
Wu Deng
Keyword(s):  
Fault Diagnosis ◽  
Deep Belief Network ◽  
Rolling Bearings ◽  
Belief Network ◽  
Mode Decomposition ◽  
Diagnosis Method

scholarly journals A Novel Intelligent Fault Diagnosis Method Based on Variational Mode Decomposition and Ensemble Deep Belief Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 36293-36312 ◽  
Author(s):  
Chao Zhang ◽  
Yibin Zhang ◽  
Chenxi Hu ◽  
Zhenbao Liu ◽  
Liye Cheng ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Deep Belief Network ◽  
Variational Mode Decomposition ◽  
Intelligent Fault Diagnosis ◽  
Belief Network ◽  
Mode Decomposition ◽  
Diagnosis Method

Fault diagnosis method of rolling bearing based on deep belief network

2018 ◽  
Vol 32 (11) ◽  
pp. 5139-5145 ◽  
Author(s):  
Zhiwu Shang ◽  
Xiangxiang Liao ◽  
Rui Geng ◽  
Maosheng Gao ◽  
Xia Liu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Deep Belief Network ◽  
Belief Network ◽  
Diagnosis Method

A rolling bearing fault diagnosis method based on fastDTW and an AGBDBN

2020 ◽  
Vol 62 (8) ◽  
pp. 457-463 ◽  
Author(s):  
Shang Zhiwu ◽  
Liu Xia ◽  
Li Wanxiang ◽  
Gao Maosheng ◽  
Yu Yan
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Deep Belief Network ◽  
Residual Vector ◽  
Belief Network ◽  
Network Training ◽  
Diagnosis Method ◽  
Stationary Vibration ◽  
Dynamic Time

In order to improve fault feature extraction and diagnosis for rolling bearings, a fault diagnosis method based on fast dynamic time warping (fastDTW) and an adaptive Gaussian-Bernoulli deep belief network (AGBDBN) is proposed in this paper. Firstly, for the non-stationary vibration signal characteristics of the bearing, the fastDTW algorithm is used to calculate the residual vector of the fault signal, thereby enhancing the fault characteristic information. Then, according to the continuous vibration value of the bearing vibration signal, a standard deep belief network (DBN) is improved to deal with the problem that the optimal setting for the learning rate is difficult to achieve in the deep neural network training process and the AGBDBN model is used for fault diagnosis. Finally, the proposed method is compared with a variety of model diagnosis methods. The experimental results show that the proposed method achieved good diagnostic results.

scholarly journals A Comprehensive Fault Diagnosis Method for Rolling Bearings Based on Refined Composite Multiscale Dispersion Entropy and Fast Ensemble Empirical Mode Decomposition

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 680 ◽  
Author(s):  
Zhang ◽  
Zhou
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Fault Classification ◽  
Rolling Bearings ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Diagnosis Method ◽  
Candidate Feature

This study presents a comprehensive fault diagnosis method for rolling bearings. The method includes two parts: the fault detection and the fault classification. In the stage of fault detection, a threshold based on refined composite multiscale dispersion entropy (RCMDE) at a local maximum scale is defined to judge the health state of rolling bearings. If the bearing is in fault, a generalized multi-scale feature extraction method is developed to fully extract fault information by combining fast ensemble empirical mode decomposition (FEEMD) and RCMDE. Firstly, the fault vibration signals are decomposed into a set of intrinsic mode functions (IMFs) by FEEMD. Secondly, the RCMDE value of multiple IMFs is calculated to generate a candidate feature pool. Then, the maximum-relevance and minimum-redundancy (mRMR) approach is employed to select the sensitive features from the candidate feature pool to construct the final feature vectors, and the final feature vectors are fed into random forest (RF) classifier to identify different fault working conditions. Finally, experiments and comparative research are carried out to verify the performance of the proposed method. The results show that the proposed method can detect faults effectively. Meanwhile, it has a more robust and excellent ability to identify different fault types and severity compared with other conventional approaches.

scholarly journals A hydraulic fault diagnosis method based on sliding-window spectrum feature and deep belief network

2017 ◽  
Vol 19 (6) ◽  
pp. 4272-4284 ◽  
Author(s):  
Jie Huang ◽  
Xinqing Wang ◽  
Guoting Ren ◽  
Dong Wang
Keyword(s):  
Fault Diagnosis ◽  
Sliding Window ◽  
Deep Belief Network ◽  
Belief Network ◽  
Diagnosis Method ◽  
Spectrum Feature

scholarly journals Analog Circuit Fault Diagnosis Based on Adaptive Gaussian Deep Belief Network

2018 ◽  
Vol 173 ◽  
pp. 03090
Author(s):  
WANG Ying-chen ◽  
DUAN Xiu-sheng
Keyword(s):  
Fault Diagnosis ◽  
Adaptive Learning ◽  
Analog Circuit ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Diagnosis Method ◽  
Diagnosis Accuracy ◽  
Network Application ◽  
Circuit Fault Diagnosis

Aiming at the problem that the traditional intelligent fault diagnosis method is overly dependent on feature extraction and the lack of generalization ability, deep belief network is proposed for the fault diagnosis of the analog circuit; Then, by analyzing the deficiency of deep belief network application, a Gaussian deep belief network based on adaptive learning rate is proposed. The automatic adjustment learning step is adopted to further improve fault diagnosis efficiency and diagnosis accuracy; Finally, particle swarm support vector machine to extract the fault characteristics to identify. The simulation results of circuit fault diagnosis show that the algorithm has faster convergence speed and higher fault diagnosis accuracy.

A fault diagnosis method using Interval coded deep belief network

2020 ◽  
Vol 34 (5) ◽  
pp. 1949-1956 ◽  
Author(s):  
Jiahui Tang ◽  
Jimei Wu ◽  
Bingbing Hu ◽  
Chang Guo ◽  
Jialing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Deep Belief Network ◽  
Belief Network ◽  
Diagnosis Method
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