A neural network approach for identification and fault diagnosis on dynamic systems
- 1 January 1994
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 43 (6) , 867-873
- https://doi.org/10.1109/19.368083
Abstract
The possibilities offered by neural networks for system identification and fault diagnosis problems in dynamic systems are investigated. In particular, an original 'neural' procedure is illustrated: its sensitivity and response time enable it to be used in on-line fault diagnosis applications. Some examples are also reported. Even though they pertain to a simple linear dynamic system, these examples highlight the general applicability and advantages of a neural approachKeywords
This publication has 13 references indexed in Scilit:
- Neural networks for control systems—A surveyAutomatica, 1992
- Measurement problems arising from the use of a recursive algorithm for model identification of electrical systemsIEEE Transactions on Instrumentation and Measurement, 1992
- A general regression neural networkIEEE Transactions on Neural Networks, 1991
- Neural networks for system identificationIEEE Control Systems Magazine, 1990
- Identification and control of dynamical systems using neural networksIEEE Transactions on Neural Networks, 1990
- A Subgrouping Strategy that Reduces Complexity and Speeds Up Learning in Recurrent NetworksNeural Computation, 1989
- Theory of the backpropagation neural networkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Multilayer feedforward networks are universal approximatorsNeural Networks, 1989
- Consistent structure determination of a noisy measurement system modelMeasurement, 1985
- Process fault detection based on modeling and estimation methods—A surveyAutomatica, 1984