scholarly journals Research on fault diagnosis method of 750kV substation based on Bayesian network and fault recording information fusion

2020 ◽  
Vol 1550 ◽  
pp. 052020
Author(s):  
Poli Shang ◽  
Haiying Dong ◽  
Xiaonan Li ◽  
Wei Ren
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Information Fusion ◽  
Diagnosis Method ◽  
Fault Recording

Fault Detection and Discrimination of Rotating Machinery Using Frequency Symptom Parameter and Bayesian Network

2013 ◽  
Vol 470 ◽  
pp. 683-688
Author(s):  
Hai Yang Jiang ◽  
Hua Qing Wang ◽  
Peng Chen
Keyword(s):  
Fault Diagnosis ◽  
Fault Detection ◽  
Bayesian Network ◽  
Frequency Domain ◽  
Evaluation Method ◽  
Roller Bearing ◽  
Rotating Machinery ◽  
Discrimination Index ◽  
Vibration Signals ◽  
Diagnosis Method

This paper proposes a novel fault diagnosis method for rotating machinery based on symptom parameters and Bayesian Network. Non-dimensional symptom parameters in frequency domain calculated from vibration signals are defined for reflecting the features of vibration signals. In addition, sensitive evaluation method for selecting good non-dimensional symptom parameters using the method of discrimination index is also proposed for detecting and distinguishing faults in rotating machinery. Finally, the application example of diagnosis for a roller bearing by Bayesian Network is given. Diagnosis results show the methods proposed in this paper are effective.

Research on Fault Diagnosis Strategy of Belt Conveyor

2013 ◽  
Vol 312 ◽  
pp. 607-610 ◽  
Author(s):  
Wei Hu ◽  
Ou Li
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Information Fusion ◽  
Accurate Diagnosis ◽  
Experimental Result ◽  
Fusion Center ◽  
Fusion Technology ◽  
Fuzzy Information ◽  
Belt Conveyor ◽  
Diagnosis Method

In view of the inadequacy of the fault diagnosis of the belt conveyor, the paper takes advantage of the application of fuzzy information fusion technology to fault diagnosis, based on the fuzzy set theory, a fault diagnosis method based on Multi-sensor fuzzy information fusion is developed. The obtain information of many sensors will fuzzy, again its fusion based on the synthetic operation and decision-making rules of the fusion center, in order to gain the accurate state estimation and judgment of belt conveyor. The experimental result indicates that the credibility of diagnosis is improved markedly and the uncertainty is reduced significantly after the multi-sensor fuzzy information fusion, the accurate diagnosis to belt conveyor is realized.

scholarly journals An SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoxun Zhu ◽  
Jianhong Zhao ◽  
Dongnan Hou ◽  
Zhonghe Han
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Fault Identification ◽  
High Complexity ◽  
Test Rig ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Image Identification ◽  
Diagnosis Method

This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.

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