Title: Artificial neural network based decision-making fault diagnosis in a normalised DC motor system
Authors: V. Manikandan, N. Devarajan
Addresses: Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore 641014, India. ' Department of Electrical and Electronics Engineering, Government College of Technology, Coimbatore 641013, India
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.
Keywords: fault diagnosis; backpropagation neural networks; ANNs; normalised DC motors; Hankel matrix; Markov parameters; SVD; singular value decomposition; decision making.
International Journal of Automation and Control, 2009 Vol.3 No.2/3, pp.171 - 188
Available online: 17 May 2009 *Full-text access for editors Access for subscribers Purchase this article Comment on this article