A framework for an on-line diagnostic expert system with intelligent sensor validation

1997 ◽  
Vol 11 (1) ◽  
pp. 10-19 ◽  
Author(s):  
Young-jin Kim
1999 ◽  
Vol 123 (1) ◽  
pp. 141-144 ◽  
Author(s):  
Ehsan Mesbahi

Abstract An intelligent sensor validation and on-line fault diagnosis technique for a 6 cylinder turbocharged diesel engine is proposed and studied. A single auto-associative 3-layer Artificial Neural Network (ANN), is trained to examine the accuracy of the measured data and allocate a confidence level to each signal. The same ANN is used to recover the missing or faulty data with a close approximation. For on-line fault detection a feed-forward ANN is trained to classify and consequently recognize faulty and healthy behavior of the engine for a wide range of operating conditions. The proposed technique is also equipped with an on-line learning mechanism, which is activated when the confidence level in predicted fault is poor. It is hoped that a feasible, practical, and reliable sensor reading, as well as highly accurate fault diagnosis system, would be achieved.


Author(s):  
J. Kubiak S. ◽  
G. Urquiza B. ◽  
A. Garci´a-Gutierrez

This paper describes the development of an Expert System for identification of generating equipment faults caused by wearing out of their components, which decrease the efficiency and thus the heat rate of a generating plant. In a sister paper [1], the formulation was presented and the algorithms for the principal equipment were developed. The Expert Systems are based on the above algorithms. Also, in some case a vibration analysis is used jointly with thermodynamic analysis to locate precisely a fault, for example in a case of rubbing which damaged the seals of the turbine and/or compressors. The system is used off-line, however it can be installed on-line with a monitoring system. The Expert Systems identify the faults of the gas turbine, the compressor and the steam turbine. Auxiliary equipment faults are presented in the form of tables also, listing the symptoms and their causes [1]. The knowledge levels and the separate bases are built into the systems.


1992 ◽  
Vol 25 (4) ◽  
pp. 259-261
Author(s):  
C. Pfeiffer ◽  
R. Soto ◽  
M. Garcia ◽  
A. De León

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