Optimal Training for Associative Memories: Application to Fault Diagnosis in Fossil Electric Power Plants

Ruz-Hernández, José A.; Sánchez, Edgar N.; Suarez, D.A.

doi:10.1007/978-3-540-37421-3_21

Cited by 4 publications

(1 citation statement)

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“…28 The second category is called dynamic NN because these NNs have integrators or delay components in their structure. [29][30][31][32][33][34][35][36][37][38][39][40] Generally, a single dynamic NN is used to model the object system; thereafter, it is trained offline. The third category is online NN observers.…”

Section: Introductionmentioning

confidence: 99%

A Gray-Box Neural Network-Based Model Identification and Fault Estimation Scheme for Nonlinear Dynamic Systems

Cen

Wei

Jiang

2013

Int. J. Neur. Syst.

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A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

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