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 Bearing Fault Diagnosis Based on Deep Belief Network and Multisensor Information Fusion

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Jie Tao ◽  
Yilun Liu ◽  
Dalian Yang
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Deep Belief Network ◽  
Identification Accuracy ◽  
Learning Ability ◽  
Belief Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
The Individual

In the rolling bearing fault diagnosis, the vibration signal of single sensor is usually nonstationary and noisy, which contains very little useful information, and impacts the accuracy of fault diagnosis. In order to solve the problem, this paper presents a novel fault diagnosis method using multivibration signals and deep belief network (DBN). By utilizing the DBN’s learning ability, the proposed method can adaptively fuse multifeature data and identify various bearing faults. Firstly, multiple vibration signals are acquainted from various fault bearings. Secondly, some time-domain characteristics are extracted from original signals of each individual sensor. Finally, the features data of all sensors are put into the DBN and generate an appropriate classifier to complete fault diagnosis. In order to demonstrate the effectiveness of multivibration signals, experiments are carried out on the individual sensor with the same conditions and procedure. At the same time, the method is compared with SVM, KNN, and BPNN methods. The results show that the DBN-based method is able to not only adaptively fuse multisensor data, but also obtain higher identification accuracy than other methods.

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
Sign in / Sign up

Export Citation Format

Share Document