Condition Monitoring of Industrial Machines Using Wavelet Packets and Intelligent Multisensor Fusion

Machine condition monitoring is an increasingly important area of research and plays an integral role in the economic competitiveness in many industries. Machine breakdown can lead to many adverse effects including increased operation and maintenance costs, reduced production output, decreased product quality and even human injury or death in the event of a catastrophic failure. As a way to overcome these problems, an automated machine diagnostics scheme may be implemented, which will continuously monitor machine health for the purpose of prediction, detection, and diagnosis of faults and malfunctions. In this work, a signal-based condition monitoring scheme is developed and tested on an industrial fish processing machine. A variety of faults are investigated including catastrophic on-off type failures, partial faults in gearbox components and sensor failures. The development of the condition monitoring scheme is divided into three distinct subtasks: signal acquisition and representation, feature reduction, and classifier design. For signal acquisition, the machine is instrumented with multiple sensors to accommodate sensor failure and increase the reliability of diagnosis. Vibration and sound signals are continuousl