Review
Rotor fault condition monitoring techniques for squirrel-cage induction machine—A review

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Abstract

Nowadays, manufacturing companies are making great efforts to implement an effective machinery maintenance program, which provides incipient fault detection. The machine problem and its irregularity can be detected at an early stage by employing a suitable condition monitoring accompanied with powerful signal processing technique. Among various defects occurred in machines, rotor faults are of significant importance as they cause secondary failures that lead to a serious motor malfunction. Diagnosis of rotor failures has long been an important but complicated task in the area of motor faults detection. This paper intends to review and summarize the recent researches and developments performed in condition monitoring of the induction machine with the purpose of rotor faults detection. The aim of this article is to provide a broad outlook on rotor fault monitoring techniques for the researchers and engineers.

Highlights

► A general overview on various rotor failures is presented. ► Different condition monitoring and signal processing techniques are explained. ► By far the most common condition monitoring is based on stator current measurement. ► The achievements of different studies are reviewed and summarized in a table.

Introduction

Induction machines (IMs) have brought about immense changes in the human life style as they facilitate and expedite production processes and related services. They are the workhorses of the industries because of their rugged configuration, low cost, versatility, reasonably small size and capability to operate with an easily available power supply. Nevertheless, in the practical applications, IMs are subjected to the unavoidable stresses, such as electrical, environmental, mechanical and thermal stresses, which create failures in different parts of the IM [1]. These failures disturb the safe operation of the IMs, threaten the normal manufacturing, and hence result in the substantial cost penalties. An effective incipient fault detection technique can reduce the maintenance expenses by preventing the high cost failures and unscheduled downtimes. Accordingly, manufacturing companies are making great efforts for the incipient fault detection using a machinery maintenance plan. The maintenance plans are basically based on the IM condition monitoring for diagnosing the existent failure at an early stage, i.e., before it causes the IM to stop [2]. The condition of an IM is examined from the signals acquired through the sensors and supporting instrumentation methods. Signals are the graphical trend of the IM parameters that can manifest fault signature using an appropriate signal processing technique.

Hitherto, a number of IM condition monitoring techniques, which monitor a certain parameter of the IM allowing its health to be determined, have been developed. Fundamentally, the efficiency of a condition monitoring method is characterized by its cost, accuracy and importantly its ability to quantify the fault. Nevertheless, condition monitoring techniques require the user to have sufficient knowledge and expertize to distinguish a normal operating condition from a potential failure state. The reliability of the fault detection techniques depend upon the best understanding of the electrical and mechanical characteristics of the machines in healthy and faulty condition. Accordingly, one of the scientific and technological challenges associated with the IM fault detection is to find a method which fulfills strong requirements of the industries. To date, considerable efforts have been focused on presenting a practical and reliable fault diagnosis procedure for IMs; however, some inconsistencies confirm more investigations are comprehensively required to attain commercialization of these technique. This paper will focus on various condition monitoring methods accomplished for IM with the aim of rotor fault detection.

Induction machines are structurally composed of a squirrel cage rotor or a wound rotor. Rotor is the most inner part of the IM, which is rotated by an electromagnetic field induced in its coils from the stator field. The rotational force is then applied to the external equipment. Due to the quite large types and applications of squirrel cage rotor, the focus of this paper is on the squirrel cage IMs (SCIMs). This type of machine has a rugged rotor; however, rotor defects such as broken bar, cracked end-ring, bent shaft and eccentricity do occur. Someone may tell that the machine can be serviced in the case of rotor faults, but it is necessary to consider the fact that the severity of the failures will inevitably increase; reaching unacceptable operating conditions [3]. The rotor failures do not initially cause the IM to fail, but they bring about secondary effects that lead to a serious malfunction of IM. In this paper, findings and achievements of more than 150 published papers on condition monitoring of the SCIM to diagnosis rotor fault, which are fruitful for the development of engineering solutions, are addressed and discussed. To get started, a general overview on the structure of squirrel cage rotor is presented. The focus will be then shifted to the various rotor failures and IM condition monitoring techniques. A brief explanation about different types of signal processing methods applied for fault diagnosis of IMs is also presented.

Section snippets

Rotor structure in SCIM

Considering the SCIM configuration, there are four main parts, namely, bars, end-rings, shaft and frame. Rotor winding is composed of bars embedded in the frame slots and shorted at the both ends by the end-rings. The frame allows the rotor bars to be mounted to the shaft. Squirrel cage rotors are basically of two types, cast and fabricated. The cast cage rotors are generally used in small size motors that do not use air ducts. However, in recent times, the casting has made significant progress

Rotor fault

The rotor is subjected to various stresses that severely influence the rotor condition and cause subsequent failures. The various stresses and their causes have been identified in a paper published by Bonnett and Soukup [6]. Rotor faults can be categorized into rotor eccentricity, breakage of rotor cage bars, breakage of end-rings and rotor bow. These asymmetries bring about some secondary failures that cause serious malfunction in IM. The following sections briefly outline the aforementioned

Condition monitoring techniques for rotor fault detection

When a fault takes place, some of the machine parameters are subjected to change that depends upon the degree of the fault. Squirrel cage induction motor with any asymmetry in its rotor presents unevenly distributed rotor currents. The reactions of these currents to the air-gap field generate fault-specific signatures in the spectrum of the current, power, torque and speed. For instance, in the current spectrum, characteristic harmonics appear at frequencies (1±2ks) fs, where “fs” is the

Signal processing

It is commonly difficult or even impossible to make sense of the information contained in a raw signal by looking at it. In addition, raw signals obtained from an instrument measuring a physical process always contain noise. Signal processing is a technique using computer algorithms to analyze and transform the raw signal to a meaningful representation of the information contained in the raw signal while suppressing the effects of noise. Accordingly, a signal acquired from condition monitoring

Summary

In this paper, we have attempted to summarize the researches and developments of the existing machine condition monitoring methods for BRB detection. The main objective is to provide a broad overview over recent developments in this area to demonstrate the capability and boundaries of those methods, and to point out possible directions for future research activities. Fundamentally, the main features of an effective diagnostic procedure for rotor faults are based on two principles: spectrum

Acknowledgements

The authors wish to thank the Research Management Center (RMC) of University Putra Malaysia and the Ministry of High Education for their financial support.

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