Artificial neural network based decision-making fault diagnosis in a normalised DC motor system
by V. Manikandan, N. Devarajan
International Journal of Automation and Control (IJAAC), Vol. 3, No. 2/3, 2009

Abstract: The aim of this write up is to deal with the identification of parameter deviations using Artificial Neural Networks (ANNs). The diagnostic approach is accomplished in two steps: In Step 1, the system is identified using a series of input/output variables through an identification algorithm. In Step 2, the fault is diagnosed comparing the Markov parameters of faulty and non-faulty systems. The ANN is trained using predetermined faulty conditions serves to classify the unknown fault. In Step 1, the identification is done by first formulating a Hankel matrix out of input/output variables and then decomposing the matrix via singular value decomposition technique. For identifying the system online, sliding window approach is adopted wherein an open slit slides over a subset of 'n' input/output variables. The faults are introduced at arbitrary instances and the identification is carried out online. Fault residues are extracted making a comparison of the first five Markov parameters of the faulty and non-faulty systems. The proposed diagnostic approach is illustrated on a normalised DC motor system with encouraging results.

Online publication date: Sun, 17-May-2009

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