REMOTE MONITORING AND FAULT DIAGNOSIS OF AN INDUSTRIAL MACHINE THROUGH SENSOR FUSION

Fault detection and diagnosis is quite important in engineering systems, and deserves further attention in view of the increasing complexity of modern machinery. Traditional single-sensor methods of fault monitoring and diagnosis may find it difficult to meet modern industrial requirements because there is usually no direct way to measure and accurately correlate a machine fault to a single sensor output. Fusion of information from multiple sensors can overcome this shortcoming. In this thesis, a neural-fuzzy approach of multi-sensor fusion is developed for a network-enabled remote fault diagnosis system. The approach is validated by applying it to an industrial machine called the Iron Butcher, which is a machine used in the fish processing industry for the removal of the head in fish prior to further processing for canning. An important characteristic of the fault diagnosis approach developed in this thesis is to make an accurate decision of the machine condition by fusing information from different sensors. First, sound, vibration and vision signals are acquired from th