On-Line Fault Detection of Inverter-Fed Induction Motors Using Advanced Signal Processing Techniques

Wolfram, Armin; Isermann, Rolf

doi:10.1016/s1474-6670(17)37512-2

Cited by 6 publications

(1 citation statement)

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“…Also recently, the IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS published a special issue on motor fault detection and diagnosis, including two survey papers of tutorial nature regarding induction motor fault diagnosis [15], [16]. Application of fault detection methods to inverter-fed motors has also been recently reported [17].…”

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confidence: 99%

Induction motor fault diagnosis based on neuropredictors and wavelet signal processing

Kim

Parlos

2002

IEEE/ASME Trans. Mechatron.

163

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Early detection and diagnosis of incipient faults is desirable for online condition assessment, product quality assurance and improved operational efficiency of induction motors running off power supply mains. In this paper, a model-based fault diagnosis system is developed for induction motors, using recurrent dynamic neural networks for transient response prediction and multi-resolution signal processing for nonstationary signal feature extraction. In addition to nameplate information required for the initial setup, the proposed diagnosis system uses measured motor terminal currents and voltages, and motor speed. The effectiveness of the diagnosis system is demonstrated through staged motor faults of electrical and mechanical origin. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2-, 373-, and 597-kW induction motors. Incremental tuning is used to adapt the diagnosis system during commissioning on a new motor, significantly reducing the system development time.

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confidence: 99%