Application of a model-based fault detection and diagnosis using parameter estimation and fuzzy inference to a DC-servomotor

Abstract

Fault detection and diagnosis are very much needed in many industrial applications. One of the most popular scheme is the model-based fault diagnostic. Recently, artificial intelligence techniques have been found to be suitable for fault detection and diagnosis and a variety of techniques have been proposed in this area. However, reported applications or real time implementation of the schemes are still very few. In this paper, we use a fault detection and diagnostic scheme based on the model-based approach using parameter estimation and Fuzzy inferencing and experimented it on a d.c. motor servo trainer. The model of the plant is obtained using recursive least squares parameter estimation technique and fuzzy inferencing is used for the interpretation of the fault. Several faults have been identified on the system. The faults are then simulated on the motor and experiments are carried out to diagnose the types of faults. The experiments have shown the proposed technique is viable for real-time application.