This paper investigates diagnostic techniques for electrical machines with special reference to induction machines and to papers published in the last ten years. A comprehensive list of references is reported and examined, and research activities classified into four main topics: 1) electrical faults; 2) mechanical faults; 3) signal processing for analysis and monitoring; and 4) artificial intelligence and decision-making techniques.

The fault diagnosis of rotating electrical machines has received an intense amount of research interest during the last 30 years. Reducing maintenance costs and preventing unscheduled downtimes, which result in losses of production and financial incomes, are the priorities of electrical drives manufacturers and operators. In fact, both correct diagnosis and early detection of incipient faults lead to fast unscheduled maintenance and short downtime for the process under consideration. They also prevent the harmful and sometimes devastating consequences of faults and failures. This topic has become far more attractive and critical as the population of electric machines has greatly increased in recent years. The total number of operating electrical machines in the world was around 16.1 billion in 2011, with a growth rate of about 50% in the last five years [1].

Recently, research concerning electrical machines and drives condition monitoring and fault diagnosis has experienced extraordinarily dynamic activity. The increasing importance of these energy conversion devices and their widespread use in uncountable applications have motivated significant research efforts. This paper presents an analysis of the state of the art in this field. The analyzed contributions were published in most relevant journals and magazines or presented in either specific conferences in the area or more broadly scoped events.

The aim of this paper is to present the advantage of stator fault detection using an external stray flux sensor in a working induction machine. In the past 20 years, stray flux sensors have been used for diagnostic purposes but rarely in the presence of a complex power supply such as a voltage static inverter. In this paper, it is proved that a simple external stray flux sensor is more efficient than the classical stator current signature analysis to detect inter-turn short circuit in three-phase induction machines. The new result is that, even in the presence of power supply harmonics, it is possible to easily detect the stator winding faults in the low-frequency range of the flux spectrum with low-frequency resolution.

Fault detection in alternating-current electrical machines that is based on frequency analysis of stator current has been the interest of many researchers. Several frequency estimation techniques have been developed and are used to help the induction machine fault detection and diagnosis. This paper presents a technique to improve the fault detection technique by using the classical multiple signal classification (MUSIC) method. This method is a powerful tool that extracts meaningful frequencies from the signal, and it has been widely used in different areas, which include electrical machines. In the proposed application, the fault sensitive frequencies have to be found in the stator current signature. They are numerous in a given frequency range, and they are affected by the signal-to-noise ratio. Then, the MUSIC method takes a long computation time to find many frequencies by increasing the dimension of the autocorrelation matrix. To solve this problem, an algorithm that is based on zooming in a specific frequency range is proposed with MUSIC in order to improve the performances of frequency extraction. Moreover, the method is integrated as a part of MUSIC to estimate the frequency signal dimension order based on classification of autocorrelation matrix eigenvalues. The proposed algorithm has been applied to detect a rotor broken bar fault in a three-phase squirrel-cage induction machine under different loads and in steady-state condition.

This paper introduces a new fault-tolerant operation method for a symmetrical six-phase induction machine (6PIM) when one or several phases are lost. A general decoupled model of the induction machine with up to three open phases is given. This model illustrates the existence of a pulsating torque when phases are opened. Then, a new control method reducing the pulsating torque and the motor losses is proposed in order to improve the drive performances. The proposed method is compared to two other existing techniques. The simulation and experimental results obtained on a dedicated test-rig confirm the validity and the efficiency of the proposed method for a fault-tolerant symmetrical 6PIM drive.