Faults Classification Scheme for Three Phase Induction Motor

Faults Classification Scheme for Three Phase Induction Motor

Mohammed Obaid Mustafa (Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden), George Nikolakopoulos (Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden) and Thomas Gustafsson (Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden)
Copyright: © 2014 |Pages: 20
DOI: 10.4018/ijsda.2014010101
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In every kind of industrial application, the operation of fault detection and diagnosis for induction motors is of paramount importance. Fault diagnosis and detection led to minimize the downtime and improves its reliability and availability of the systems. In this article, a fault classification algorithm based on a robust linear discrimination scheme, for the case of a squirrel–cage three phase induction motor, will be presented. The suggested scheme is based on a novel feature extraction mechanism from the measured magnitude and phase of current park's vector pattern. The proposed classification algorithm is applied to detect of two kinds of induction machine faults, which area) broken rotor bar, and b) short circuit in stator winding. The novel feature generation technique is able to transform the problem of fault detection and diagnosis into a simpler space, where direct robust linear discrimination can be applied for solving the classification problem. And thus a clear classification of the healthy and the faulty cases can be robustly performed, by having the optimal hyper plane. This method can separate the feature current classes in a low dimensional subspace. Robust linear discrimination has been one of the most widely used fault detection methods in real-life applications, as this methodology seeks for directions that are efficient for discrimination and at the same time applies a straight-forward implementation. The efficacy of the proposed scheme will be evaluated based on multiple simulation results in different fault types.
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Induction machine is very common in industrial systems. Fault detection, diagnosis and classification of Induction machines have received considerable interest in the last decades. These motors are the most widely utilized electrical machines, mainly due to their advantages such as: stability properties, robustness, durability, power to weight ratio, low production costs and easiness of controlling them (Acosta et al., 2004).

Despite their numerous advantages, still many types of faults might occur, which can cause unexpected safety risks and economic expenses, delays in processes and overall production line breakdown. Faults occur most of the time in the rotor and the stator of an induction motor, while many of these faults reveal their existence gradually and thus sometimes it is very difficult to be identified in due time (Aydin et al., 2007).

Among the most common faults that can be found within the area of induction motors are: a) opening or shorting in one or more of a stator’s phase windings (Nandi & Toliyat, 2005; Seshadrinath et al., 2013), b) broken rotor bar or cracked rotor’s end-rings (Santos et al., 2006), c) static or dynamic air–gap irregularities (Acosta et al., 2004), and d) bearing failures (Bouchikhi & Benbouzid, 2013). More statistical results, obtained from reports by the Motor Reliability Working Group of the IEEE Industry Applications Society (IAS), which surveyed 1141 motors, and the Electrical Power Research Institute (EPRI), which surveyed 6312 motors, are summarized in Table 1. As one can observe, bearing-(mechanical) and winding (stator)-related failures are the dominant trouble areas (Mercangoz & Doyle, 2006). From another point of view motor, faults can be categorized into two types, depending on their origin, e.g. mechanical and electrical, or it can be classified according to their location, e.g. stator and rotor. Usually, other types of fault like bearing faults refer to rotor faults also because they belong to the moving parts (Nandi & Toliyat, 2005).

Table 1.
Percentage of failure by component in induction motor
Failed ComponentIEEE(IAS)EPRI
Bearing Faults44%41%
Winding Faults26%36%
Rotor Faults8%9%
Others Faults22%14%

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