The Optimized Deep Belief Networks With Improved Logistic Sigmoid Units and Their Application in Fault Diagnosis for Planetary Gearboxes of Wind Turbines

2019 ◽  
Vol 66 (5) ◽  
pp. 3814-3824 ◽  
Author(s):  
Yi Qin ◽  
Xin Wang ◽  
Jingqiang Zou
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbines ◽  
Belief Networks ◽  
Deep Belief Networks

scholarly journals Combining DBN and FCM for Fault Diagnosis of Roller Element Bearings without Using Data Labels

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Fan Xu ◽  
Yan jun Fang ◽  
Dong Wang ◽  
Jia qi Liang ◽  
Kwok Leung Tsui
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Vibration Signals ◽  
Fuzzy C Means ◽  
Roller Bearings ◽  
Mode Decomposition

Because deep belief networks (DBNs) in deep learning have a powerful ability to extract useful information from the raw data without prior knowledge, DBNs are used to extract the useful feature from the roller bearings vibration signals. Unlike classification methods, the clustering method can classify the different fault types without data label. Therefore, a method based on deep belief networks (DBNs) in deep learning (DL) and fuzzy C-means (FCM) clustering algorithm for roller bearings fault diagnosis without a data label is presented in this paper. Firstly, the roller bearings vibration signals are extracted by using DBN, and then principal component analysis (PCA) is used to reduce the dimension of the vibration signal features. Secondly, the first two principal components (PCs) are selected as the input of fuzzy C-means (FCM) for roller bearings fault identification. Finally, the experimental results show that the fault diagnosis of the method presented is better than that of other combination models, such as variation mode decomposition- (VMD-) singular value decomposition- (SVD-) FCM, and ensemble empirical mode decomposition- (EEMD-) fuzzy entropy- (FE-) PCA-FCM.

scholarly journals A Multichannel Data Fusion Method Based on Multiple Deep Belief Networks for Intelligent Fault Diagnosis of Main Reducer

Symmetry ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 483 ◽  
Author(s):  
Qing Ye ◽  
Changhua Liu
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Data Fusion ◽  
Support Vector ◽  
Fusion Method ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Intelligent Fault Diagnosis ◽  
Comparison Results ◽  
Multichannel Data

Aiming at the problems of poor efficiency of the intelligent fault diagnosis method of the main reducer and the poor effectiveness of multichannel data fusion, this paper proposes a multichannel data fusion method based on deep belief networks and random forest fusion for fault diagnosis. Multiple deep belief networks (MDBNs) are constructed to obtain deep representative features from multiple modalities of multichannel data. Random forest can fuse deep representative features achieved from MDBNs to construct the model of multiple deep belief networks fusion (MDBNF). The proposed method is applied to fault diagnosis of the main reducer and evaluation of the performance. Multiple deep belief network model fusions (MD BN F) are constructed to improve the multichannel data fusion effect. Single sensory data, multichannel data, and two intelligent models based on support vector machine and deep belief networks are used as comparison in the experiments. The results indicate that the classification accuracy of the test set collected by sensor 1 and sensor 2 is 88.35% and 88.73%, respectively. The comparison results show that the method has good convergence. The data fusion of the proposed diagnostic model can effectively improve the correlation between the collected vibration signals and the failure mode, thereby improving the diagnostic performance by nearly 8%, representing improved diagnostic accuracy.

An integrated approach to planetary gearbox fault diagnosis using deep belief networks

2016 ◽  
Vol 28 (2) ◽  
pp. 025010 ◽  
Author(s):  
Haizhou Chen ◽  
Jiaxu Wang ◽  
Baoping Tang ◽  
Ke Xiao ◽  
Junyang Li
Keyword(s):  
Fault Diagnosis ◽  
Integrated Approach ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Planetary Gearbox

A bearing fault and severity diagnostic technique using adaptive deep belief networks and Dempster–Shafer theory

2019 ◽  
Vol 19 (1) ◽  
pp. 240-261 ◽  
Author(s):  
Kun Yu ◽  
Tian Ran Lin ◽  
Jiwen Tan
Keyword(s):  
Fault Diagnosis ◽  
Hybrid Genetic Algorithm ◽  
Diagnostic Technique ◽  
Current Method ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Bearing Fault ◽  
Operation Conditions ◽  
Bearing Condition Monitoring ◽  
Stage 1

An artificial intelligent bearing fault and hierarchical severity diagnosis framework is proposed in this study. The framework utilizes a combined deep belief networks (DBNs) and Dempster–Shafer (D-S) theory fault diagnosis scheme and adopts a two-stage approach in classifying (1) bearing fault conditions and (2) fault severities. The combined fault diagnostic scheme first employs two parameter-optimized DBNs to process the horizontal and vertical vibration data acquired from the bearing house of a test rig, where the parameters of the DBNs are optimized using a hybrid genetic algorithm and particle swarm optimization algorithm proposed in this study. The classification results from the two DBNs are fused further using the D-S theory to improve the diagnostic accuracy. The fault diagnosis scheme is used first to classify the bearing fault conditions in Stage 1 from a bulk dataset containing all bearing operation conditions under study. The same diagnosis scheme is applied once more to classify the hierarchical fault severities for each fault condition in Stage 2 using the pre-classified data from Stage 1. The effectiveness of the framework is then evaluated on a set of bearing condition monitoring data. A comparison study between the results obtained using the current method and those from existing published work is also presented in the article. It is shown that the accuracy for bearing fault and severity diagnosis can be substantially improved by using the current framework.

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