Intelligent condition diagnosis method for rotating machinery using Relative Ratio Symptom Parameter and Bayesian Network

2010 ◽  
Author(s):  
Jingjing Zhu ◽  
Zhongxing Li ◽  
Ke Li ◽  
Peng Chen
Keyword(s):  
Bayesian Network ◽  
Rotating Machinery ◽  
Relative Ratio ◽  
Diagnosis Method ◽  
Condition Diagnosis

Intelligent Method of Condition Diagnosis for Rotating Machinery Using Relative Ratio Symptom Parameter and Bayesian Network

2011 ◽  
Vol 4 (6) ◽  
pp. 2532-2537 ◽  
Author(s):  
Jingjing Zhu ◽  
Zhongxing Li ◽  
Ke Li ◽  
Hongtao Xue ◽  
Peng Chen
Keyword(s):  
Bayesian Network ◽  
Rotating Machinery ◽  
Relative Ratio ◽  
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 104 Diagnosis method for Rotating Machinery Using Bayesian Network

2010 ◽  
Vol 2010.9 (0) ◽  
pp. 23-28
Author(s):  
Jingjing Zhu ◽  
Ke Li ◽  
Hongtao Xue ◽  
Ho JINYAMA
Keyword(s):  
Bayesian Network ◽  
Rotating Machinery ◽  
Diagnosis Method

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

Intelligent Diagnosis Method for Rotating Machinery Using Ant Colony Optimization

2012 ◽  
Vol 518-523 ◽  
pp. 3814-3819
Author(s):  
Ke Li ◽  
Peng Chen ◽  
Hao Sun
Keyword(s):  
Ant Colony Optimization ◽  
Early Stage ◽  
Rotating Machinery ◽  
Ant Colony ◽  
Vibration Signals ◽  
Clustering Problem ◽  
State Identification ◽  
State Detection ◽  
Diagnosis Method ◽  
Condition Diagnosis

This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and non-dimensional symptom parameters (NSPs) in order to detect faults and distinguish fault types at an early stage. NSPs are defined for reflecting the features of vibration signals measured in each state. Detection index (DI) using statistical theory has also been defined to evaluate the applicability of the NSPs. The DI can be used to indicate the fitness of an NSP for ACO. Lastly, the state identification for the condition diagnosis of rotating machinery is converted to a clustering problem of the values of NSPs calculated from vibration signals in different states of the machine. Ant colony optimization (ACO) is also introduced for this purpose. Practical examples of fault diagnosis for rotating machinery are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in a rotation machinery, such as a unbalance, a misalignment and a looseness states are effectively identified by the proposed method.

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