scholarly journals Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

Sensors ◽  
2016 ◽  
Vol 16 (1) ◽  
pp. 76 ◽  
Author(s):  
Ke Li ◽  
Qiuju Zhang ◽  
Kun Wang ◽  
Peng Chen ◽  
Huaqing Wang
Keyword(s):  
Bayesian Network ◽  
Test Filter ◽  
Diagnosis Method ◽  
Condition Diagnosis

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.

scholarly journals Diagnosis Method for the Heat Balance State of an Aluminum Reduction Cell Based on Bayesian Network

Metals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 604
Author(s):  
Jiaming Zhu ◽  
Jie Li
Keyword(s):  
Bayesian Network ◽  
Heat Balance ◽  
Aluminum Reduction Cell ◽  
Conditional Probability Distribution ◽  
Important Indicator ◽  
Aluminum Reduction ◽  
Reduction Cell ◽  
Balance State ◽  
Diagnosis Method ◽  
The Heat Balance

The superheat of an electrolyte is an important indicator of the heat balance state of aluminum reduction cells. In industrial practice, it costs too much to accurately measure the superheat in every cell every day. A common alternative is to calculate the superheat based on additive concentrations in the electrolyte, which has problems of high error and long delay. In this paper, a method to diagnose the heat balance state of an aluminum reduction cell based on Bayesian network is presented, a Bayesian network structure and CPT (conditional probability distribution) were built, and the continuous diagnosis process is presented. This diagnosis method takes important symptoms and factors into account, taking advantage of more useful information instead of only calculated superheat. The application examples show that this method is effective in diagnosing the heat balance state for uncertain and incomplete superheat information.

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