Probabilistic Model-Based Fault Diagnosis of the Rotor System

2007 ◽  
Author(s):  
Jiye Shao ◽  
Rixin Wang ◽  
Jingbo Gao ◽  
Minqiang Xu
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Network Model ◽  
Rotating Machinery ◽  
Rotor System ◽  
Bayesian Network Model ◽  
Diagnosis System ◽  
Model Based ◽  
Core Components ◽  
Fault Diagnosis System

The rotor is one of the most core components of the rotating machinery and its working states directly influence the working states of the whole rotating machinery. There exists much uncertainty in the field of fault diagnosis in the rotor system. This paper analyses the familiar faults of the rotor system and the corresponding faulty symptoms, then establishes the rotor’s Bayesian network model based on above information. A fault diagnosis system based on the Bayesian network model is developed. Using this model, the conditional probability of the fault happening is computed when the observation of the rotor is presented. Thus, the fault reason can be determined by these probabilities. The diagnosis system developed is used to diagnose the actual three faults of the rotor of the rotating machinery and the results prove the efficiency of the method proposed.

Reducing the Impact of False Alarms in Induction Motor Fault Diagnosis

2003 ◽  
Vol 125 (1) ◽  
pp. 80-95 ◽  
Author(s):  
Kyusung Kim ◽  
Alexander G. Parlos
Keyword(s):  
Fault Diagnosis ◽  
Induction Motors ◽  
Wavelet Packet ◽  
New Technology ◽  
False Alarms ◽  
Motor Speed ◽  
Diagnosis System ◽  
Model Based ◽  
Fault Diagnosis System ◽  
The Impact

Early detection and diagnosis of incipient faults is desirable for on-line condition assessment, product quality assurance, and improved operational efficiency of induction motors. At the same time, reducing the probability of false alarms increases the confidence of equipment owners in this new technology. In this paper, a model-based fault diagnosis system recently proposed by the authors for induction motors is experimentally compared for fault detection and false alarm performance with a more traditional signal-based motor fault estimator. In addition to the nameplate information required for the initial set-up, the proposed model-based fault diagnosis system uses measured motor terminal currents and voltages, and motor speed. The motor model embedded in the diagnosis system is empirically obtained using dynamic recurrent neural networks, and the resulting residuals are processed using wavelet packet decomposition. The effectiveness of the model-based diagnosis system in detecting the most widely encountered motor electrical and mechanical faults, while minimizing the impact of false alarms resulting from power supply and load variations, is demonstrated through extensive testing with staged motor faults. The model-based fault diagnosis system is scalable to motors of different power ratings and it has been successfully tested with fault data from 2.2kW,373kW, and 597kW induction motors.

Bayesian Diagnostic Network Model for Sliding Plug Door

2011 ◽  
Vol 219-220 ◽  
pp. 1496-1499 ◽  
Author(s):  
Hui Chao Shi ◽  
Long Tian ◽  
Liang Wang
Keyword(s):  
Complex System ◽  
Fault Diagnosis ◽  
Expert System ◽  
Bayesian Network ◽  
Knowledge Base ◽  
Network Model ◽  
Model Building ◽  
Bayesian Network Model ◽  
Structure Tree ◽  
System Knowledge

For constructing Bayesian diagnostic network model of complex system is a difficult course, we propose a Bayesian network model auto-construction method based on expert system knowledge base. Bayesian diagnostic network model was built by using the CM structure, and the diagnostic knowledge was organized by product structure tree. We have applied this method to fault diagnosis for sliding plug door, and tested our methodology on many examples of diagnostic problems of sliding plug door, which prove the efficiency of the Bayesian diagnostic network model and model-building method．

scholarly journals Performance of a Bayesian-Network-Model-Based BCI Using Single-Trial EEGs

2015 ◽  
Vol E98.D (11) ◽  
pp. 1976-1981
Author(s):  
Maiko SAKAMOTO ◽  
Hiromi YAMAGUCHI ◽  
Toshimasa YAMAZAKI ◽  
Ken-ichi KAMIJO ◽  
Takahiro YAMANOI
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Single Trial ◽  
Bayesian Network Model ◽  
Model Based

scholarly journals Fault Diagnosis System of Rotating Machinery Vibration Signal

2011 ◽  
Vol 15 ◽  
pp. 671-675 ◽  
Author(s):  
Lei You ◽  
Jun Hu ◽  
Fang Fang ◽  
Lintao Duan
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rotating Machinery ◽  
Diagnosis System ◽  
Fault Diagnosis System
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