scholarly journals Rolling Bearing Fault Diagnosis Method Based on Improved Deep Belief Network

2021 ◽  
Vol 1820 (1) ◽  
pp. 012105
Author(s):  
Bingbing Hu ◽  
Jiahui Tang ◽  
Jimei Wu ◽  
Jie Liu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Deep Belief Network ◽  
Belief Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method

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.

Domain adaptive deep belief network for rolling bearing fault diagnosis

2020 ◽  
Vol 143 ◽  
pp. 106427 ◽  
Author(s):  
Changchang Che ◽  
Huawei Wang ◽  
Xiaomei Ni ◽  
Qiang Fu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Deep Belief Network ◽  
Belief Network ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis

scholarly journals Rolling bearing fault diagnosis method based on VMD and LSSVM

2021 ◽  
Vol 1792 (1) ◽  
pp. 012035
Author(s):  
Xingtong Zhu ◽  
Zhiling Huang ◽  
Jinfeng Chen ◽  
Junhao Lu
Keyword(s):  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
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

scholarly journals Rolling-Bearing Fault-Diagnosis Method Based on Multimeasurement Hybrid-Feature Evaluation

Information ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 359 ◽  
Author(s):  
Jianghua Ge ◽  
Guibin Yin ◽  
Yaping Wang ◽  
Di Xu ◽  
Fen Wei
Keyword(s):  
Fault Diagnosis ◽  
Frequency Domain ◽  
Rolling Bearing ◽  
Support Vector ◽  
Distance Information ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Feature Evaluation ◽  
Diagnosis Method ◽  
Diagnosis Model

To improve the accuracy of rolling-bearing fault diagnosis and solve the problem of incomplete information about the feature-evaluation method of the single-measurement model, this paper combines the advantages of various measurement models and proposes a fault-diagnosis method based on multi-measurement hybrid-feature evaluation. In this study, an original feature set was first obtained through analyzing a collected vibration signal. The feature set included time- and frequency-domain features, and also, based on the empirical-mode decomposition (EMD)-obtained time-frequency domain, energy and Lempel–Ziv complexity features. Second, a feature-evaluation framework of multiplicative hybrid models was constructed based on correlation, distance, information, and other measures. The framework was used to rank features and obtain rank weights. Then the weights were multiplied by the features to obtain a new feature set. Finally, the fault-feature set was used as the input of the category-divergence fault-diagnosis model based on kernel principal component analysis (KPCA), and the fault-diagnosis model was based on a support vector machine (SVM). The clustering effect of different fault categories was more obvious and classification accuracy was improved.

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