An integrated approach to machine fault diagnosis

Abstract

This paper introduces an integrated methodology to monitor and diagnose machine faults in complex industrial processes such as textile and fiber manufacturing facilities. The approach is generic and applicable to a variety of industrial plants that operate critical processes and may require continuous monitoring and maintenance procedures. A dual approach is pursued: high-bandwidth fault symptomatic evidence, such as vibrations, current spikes, etc., are treated via a feature extractor/neural network classifier construct; while low-bandwidth phenomena, such as temperature, pressure, corrosion, leaks, etc., are better diagnosed with a fuzzy rule base set as an expert system. The technique is illustrated with typical examples from benchmark processes common to many industrial plants.