Research on Fault Diagnosis Method Based on FMEA/FTA and Bayesian Network

2019 ◽  
Author(s):  
Chengbing He ◽  
Runze Wang ◽  
Li Ma ◽  
Xiaobo Li ◽  
Xiaofeng Jiao ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Diagnosis Method

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 the fault diagnosis method for high-speed loom using rough set and Bayesian network

2020 ◽  
Vol 39 (1) ◽  
pp. 1147-1161
Author(s):  
Yanjun Xiao ◽  
Heng Zhang ◽  
Wei Zhou ◽  
Feng Wan ◽  
Zhaozong Meng
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Rough Set ◽  
High Speed ◽  
Diagnosis Method

On the Diagnostic Methods of Bayesian-Network in Smart Grid

2011 ◽  
Vol 71-78 ◽  
pp. 2424-2428
Author(s):  
Han Mei Hu ◽  
Jun Lei Zhao ◽  
Ping Wen Tu
Keyword(s):  
Fault Diagnosis ◽  
Smart Grid ◽  
Bayesian Network ◽  
Real Time ◽  
Fault Tolerant ◽  
Fault Isolation ◽  
Diagnostic Methods ◽  
Self Healing ◽  
Diagnosis Method ◽  
Time Accuracy

Aiming at the smart grid self-healing characteristics, puts forward a Bayesian network fault diagnosis method. According to the protection movement signal and the circuit breaker tripping signal, establish the face of components of the smart grid line fault diagnosis model. The fault diagnosis method is real-time and accuracy, and fault-tolerant ability etc. characteristics. This method not only satisfy intelligent power grid self-healing characteristics on fault diagnosis real-time, accuracy and automatic fault diagnosis of the requirements, but also provide the smart grid fault isolation and system of self recover with strong guarantee.

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