FPGA-based entropy neural processor for online detection of multiple combined faults on induction motors

For industry, a faulty induction motor signifies production reduction and cost increase. Real-world induction motors can have one or more faults present at the same time that can mislead to a wrong decision about its operational condition. The detection of multiple combined faults is a demanding task, difficult to accomplish even with computing intensive techniques. This work introduces information entropy and artificial neural networks for detecting multiple combined faults by analyzing the 3-axis startup vibration signals of the rotating machine. A field programmable gate array implementation is developed for automatic online detection of single and combined faults in real time. © 2012 Elsevier Ltd.

3-axis vibration signals; Artificial neural networks; Field programmable gate array; Induction motors; Information entropy; Multiple combined faults Cost-increases; Field-programmable gate array implementations; Information entropy; Multiple combined faults; Neural processors; On-line detection; Operational conditions; Production reduction; Real time; Rotating machine; Vibration signal; Field programmable gate arrays (FPGA); Neural networks; Induction motors

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