Neural observer schemes for robust detection and isolation of process faults
Neural observer schemes for robust detection and isolation of process faults
- Author(s): T. Marcu ; L. Mirea ; P.M. Frank
- DOI: 10.1049/cp:19980358
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- Author(s): T. Marcu ; L. Mirea ; P.M. Frank Source: UKACC International Conference on Control (CONTROL '98), 1998 p. 958 – 963
- Conference: UKACC International Conference on Control (CONTROL '98)
- DOI: 10.1049/cp:19980358
- ISBN: 0 85296 708 X
- Location: Swansea, UK
- Conference date: 1-4 Sept. 1998
- Format: PDF
The present paper suggests neural approaches to observer-based schemes, in order to perform a robust diagnosis of process faults. The symptoms are generated by using dynamic neural networks with mixed structure. The residuals are then classified by means of static artificial nets. Application to a laboratory process is included. It refers to component and instrument fault detection and isolation in a three-tank system.
Inspec keywords: diagnostic expert systems; fault diagnosis; neural nets; observers
Subjects: Computerised instrumentation; Neural computing techniques; Inspection and quality control; Simulation, modelling and identification; Expert systems and other AI software and techniques; Inspection and quality control; Instrumentation; Computerised instrumentation
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