Event-driven on-line co-simulation with fault diagnostic

Author(s):  
Mikhail Baklashov
Author(s):  
Stefano Sapienza ◽  
Paolo Motto Ros ◽  
David Alejandro Fernandez Guzman ◽  
Fabio Rossi ◽  
Rossana Terracciano ◽  
...  

2008 ◽  
Vol 175 (1) ◽  
pp. 419-429 ◽  
Author(s):  
Luis Alberto M. Riascos ◽  
Marcelo G. Simoes ◽  
Paulo E. Miyagi

2011 ◽  
Vol 109 ◽  
pp. 437-440 ◽  
Author(s):  
Hai Ou Liu ◽  
Ai Zeng ◽  
Wei Qun Song

On the basis of discussing the construction and working principle of AMT for a heavy-duty vehicle, the paper analyzed the main causes for different kinds of faults, and presented three fault diagnostic methods. The diagnostic software was programmed and verified feasible through bench test.


Volume 3 ◽  
2004 ◽  
Author(s):  
Xiyang Chen ◽  
Kewei Zhang ◽  
Yucheng Peng

Hydro-Generator Sets Condition Monitoring and Predictive Maintenance activity has increased dramatically over the past few years. The Fault Diagnostic System is the key technique for the Predictive Maintenance. This paper discusses the Fault Diagnostic System function structure, system design and inferential strategy of Multi-Fault Diagnostic System of the Hydro-Generator Sets. The developing of the power system to big unit requires the higher automation and reliability of the power station. Electrical power systems are constantly exposed to faults and disturbances. This may lead to damage or it may pose a threat to reliable power system operation if a faulty cannot be quickly isolated from the system operation. In accordance with diversity and complexity of Hydro-Generator Sets faults, this paper brings forward a type of fault diagnosis method based on Multi-Diagnosis methods. The Multi-Diagnosis system is constituted of two Sub-Diagnosis systems: one is On-Line Sub-Diagnosis system that based on Bayesian Network (BN) just for the modeling with Bayesian Network has been a powerful tool to solve many uncertainty problems and also with the ability of predicting the future diagnosis; the other is Off-Line Sub-Diagnosis System that based on Model of a hydraulic Turbine-Generator Rotor-Bearing. In order to apply the Bayesian Network model to the engineering fields, we have to solve the problem of constructing the Bayesian Network. Then it suggests a method of constructing Bayesian Network based on the Fault Trees that widely used by the engineers. Base on the construction method, we will construct the Bayesian Network quickly, and Bayesian Network is more suitable for Hydro-Generator Sets fault diagnosis. In accordance with the On-Line Diagnosis Sub-System, it adopts Case-Based Reasoning to make the decision of final diagnosis result or further diagnosis. However, the method mentioned above is limited because of its bottleneck of the knowledge acquisition. The model strategy of the Rotor-Bearing system of Hydro-Generator is discussed and a multi-degree-freedom nonlinear model is developed. It proposes the simulation in accordance with the three fields such as: waterpower, electric and machine. Mechanical, electrical and hydraulic forces acting on rotor externally can be taken into account during the model calculating process. The transient responses of the system are calculated by combined used the transfer matrix method. This paper brings forward a prototype of Hydro-Generator Sets Fault Diagnostic System in order to make a more efficient fault diagnostic decision.


Author(s):  
Jing Wang ◽  
Mark Sumner ◽  
Dave Thomas ◽  
Milijana Odavic ◽  
Edward Christopher

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