Induction machine drive condition monitoring and diagnostic research—a survey

Abstract The subject of machine condition monitoring is charged with developing new technologies to diagnose the machinery problems. Different methods of fault identification have been developed and used effectively to detect the machine faults at an early stage using different machine quantities, such as current, voltage, speed, efficiency, temperature and vibrations. One of the principal tools for diagnosing rotating machinery problems has been the vibration analysis. Through the use of different signal processing techniques, it is possible to obtain vital diagnostic information from vibration profile before the equipment catastrophically fails. A problem with diagnostic techniques is that they require constant human interpretation of the results. The logical progression of the condition monitoring technologies is the automation of the diagnostic process. The research has been underway for a long time to automate the diagnostic process. Recently, artificial intelligent tools, such as expert systems, neural network and fuzzy logic, have been widely used with the monitoring system to support the detection and diagnostic tasks. This paper reviews the progress made in electrical drive condition monitoring and diagnostic research and development in general and induction machine drive condition monitoring and diagnostic research and development, in particular, since its inception. Attempts are made to highlight the current and future issues involved for the development of automatic diagnostic process technology.

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