Residual generation and fault diagnosis of rechargeable lead-acid batteries

Abstract

In many process and manufacturing industries, early detection of faults has great practical importance. Since it saves time and cost involved in the repairing of the equipment. Qualitative methods such as neural networks and fuzzy logic are popular tools in model based fault detection and classification of nonlinear dynamic systems. Since it is difficult to accurately model these kind of systems. In the first part of this work, neural network and adaptive neuro-fuzzy logic methods are used in the modeling of a water-tank system to produce residuals for fault classification. This study shows that neural networks have better performance but longer training time compared to the adaptive neuro-fuzzy logic. The second part of this research investigates the classification tree and Fisher Discriminant Analysis (FDA) approaches in fault classification of nonlinear dynamic systems. Comparing the performance of these approaches indicates that FDA method results in longer computational time but lower tree size for high dimensional training data. The contributions of this thesis are modeling and fault diagnosis of lead-acid battery system using qualitative techniques in combination with statistical methods such as classification tree.