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.

In this paper, the authors propose a method for the diagnosis of rotor bar failures in induction machines, based on the analysis of the stator current during the startup using the discrete wavelet transform (DWT). Unlike other approaches, the study of the high-order wavelet signals resulting from the decomposition is the core of the proposed method. After an introduction of the physical and mathematical bases of the method, a description of the proposed approach is given; for this purpose, a numerical model of induction machine is used in such a way that the effects of a bar breakage can clearly be shown, avoiding the influence of other phenomena not related with the fault. Afterward, the new diagnosis method is validated using a set of commercial induction motors. Several experiments are developed under different machine conditions (healthy machine and machine with different levels of failure) and operating conditions (no load, full load, pulsating load, and fluctuating voltage). In each case, the results are compared with those obtained using the classical approach, based on the analysis of the steady-state current using the Fourier transform. Finally, the results are discussed, and some considerations about the influence of the DWT parameters (type of mother wavelet, order of the mother wavelet, sampling rate, or number of levels of the decomposition) over the diagnosis are done

This paper proposes an online/offline induction motor current signature analysis (MCSA) with advanced signal-and-data-processing algorithms, based on the Hilbert transform. MCSA is a method for motor diagnosis with stator-current signals. Although it is one of the most powerful online methods for diagnosing motor faults, it has some drawbacks that can degrade the performance and accuracy of a motor-diagnosis system. In particular, it is very difficult to detect broken rotor bars when the motor is operating at low slip or under no load, due to fast Fourier transform (FFT) frequency leakage and the small amplitude of the current components related to the fault. Therefore, advanced signal-and-data-processing algorithms are proposed. They consist of a proper sample selection algorithm, a Hilbert transformation of the stator-sampled current, and spectral analysis via FFT of the modulus of the resultant time-dependent vector modulus for achieving MCSA efficiently. Experimental results obtained on a 1.1 kW three-phase squirrel-cage induction motor are discussed.

In this paper, a general methodology based on the application of discrete wavelet transform (DWT) to the diagnosis of the cage motor condition using transient stator currents is exposed. The approach is based on the identification of characteristic patterns introduced by fault components in the wavelet signals obtained from the DWT of transient stator currents. These patterns enable a reliable detection of the corresponding fault as well as a clear interpretation of the physical phenomenon taking place in the machine. The proposed approach is applied to the detection of rotor asymmetries in two alternative ways, i.e., by using the startup current and by using the current during plugging stopping. Mixed eccentricities are also detected by means of the transient-based methodology. This paper shows how the evolution of other non-fault-related components such as the principal slot harmonic (PSH) can be extracted with the proposed technique. A compilation of experimental cases regarding the application of the methodology to the previous cases is presented. Guidelines for the easy application of the methodology by any user are also provided under a didactic perspective.

Transient-based fault diagnosis in induction motors has gained increasing attention over the recent years. This is due to its ability to avoid eventual wrong diagnostics of the conventional motor current signature analysis in certain industrial situations (presence of load toque oscillations, light loading conditions, and so on). However, the application of these transient methodologies requires the use of advanced signal processing tools. This paper presents a detailed comparison between the two main groups of transforms that are employed in transient analysis: discrete and continuous. This paper does not focus on trivial fault cases but on difficult real situations where the application of the conventional methods often leads to false diagnostics (outer bar breakages in double-cage motors, motors with rotor axial duct influence, and combined faults). Indeed, it is the first time that continuous tools are applied to some of these controversial situations. The results in this paper prove the special advantages of the continuous transforms, tearing down some false myths about their use.