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

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 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.

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