Abstract
This paper brings up a novel method for detecting induction motor stator winding faults at an early stage. The contribution of the work comes from the delicate handling of motorvibration by applying envelope analysis, which makes it possible to capture electrical short-circuit signature in mechanical signals, even if the magnitude of the fault is fairly incipient. Conventional induction motor condition-based maintenance methods usually involve current and voltage measurements, which could be expensive to collect, and vibration-based analysis is often only capable of detecting the fault when it is already quite significant. In contrast, the solution presented in this study provides a refreshing perspective by applying time synchronous averaging to remove the discrete frequency component, and amplitude demodulation to further enhance the signal with the help of kurtogram. Experimental results on a three-phase induction motor show that the method is also able to distinguish different fault severity levels.
Highlights
In various industrial applications, such as high-speed trains, electric vehicles, industrial robots, and machine tools, threephase induction motors are always the driving force and one of the key machines of the whole system
Even with scheduled maintenance practices, unexpected failures of induction motors could still occur in these systems which would lead to excessive downtime and large losses in terms of maintenance cost and lost revenue
Inspired by the rolling-element bearing fault diagnosis (Randall & Antoni, 2011), this paper addresses the issue of detecting induction motor stator inter-turn faults when they are still preliminary
Summary
In various industrial applications, such as high-speed trains, electric vehicles, industrial robots, and machine tools, threephase induction motors are always the driving force and one of the key machines of the whole system. In CBM, maintenance activities are not scheduled for machines merely according to history of maintenance records or predefined maintenance rules on the basis of experience and/or expert knowledge, and based on the present health status of the machines from sensory data, so that the waste owing to redundant maintenance and failures will be avoided. Such maintenance strategies require the integration of the following technologies: (a) on-line condition monitoring, (b) fault detection and diagnostics, and (c) prognostics.
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