Single-parameter fault identification through information entropy analysis at the startup-transient current in induction motors

Abstract

Opportune diagnosis of rotating machines contributes to avoid expensive reparations and unscheduled shutting downs, and prevents incomings losses. Most of the developed techniques for induction motor condition monitoring fall in one of three classifications: the detection of a single fault by analyzing one or multiple parameters; the detection of different faults by combining multiple parameters and processing techniques; and expert systems that combine several computing-intensive techniques to analyze different electrical and mechanical parameters in order to detect multiple faults. Recent works have been oriented to provide computationally effective diagnostic tools for condition monitoring, which able to discriminate different faults by analyzing a minimum set of parameters. This work presents a methodology for induction motor condition monitoring by analyzing a single parameter. The analysis combines the reconstruction of a single wavelet-packet node with information entropy to obtain one parameter, which allows the detection of different faults quantitatively by analyzing the startup-transient current signal from the induction motor. Experimental results show that the proposed methodology allows the detection of a healthy motor, a motor with one broken bar, a motor with unbalanced mechanical load, and a motor with a faulty bearing in a quantitative way; with a certainty of more than 99.7%.