Condition based maintenance systems (CBM) of induction machines (IMs) require fast and accurate models that can reproduce the fault related harmonics generated by different kinds of faults, in order to help in developing new diagnostic algorithms for detecting the faults at an early stage, to analyse the physical interactions between simultaneous faults of different types, or to train expert systems that can supervise and identify these faults in an autonomous way. To achieve these goals, such models must take into account the space harmonics of the air gap magnetomotive force (MMF) generated by the machine windings under fault conditions, due to the complex interactions between spatial and time harmonics in a faulty machine. One of the most common faults in induction machines is the rotor eccentricity, which can cause significant radial forces and, in extreme cases, produce destructive rotor-stator rub. But the development of a fast, analytic model of the eccentric IM is a challenging task, due to the nonuniformity of the air gap. In this paper, a new method is proposed to obtain such a fast model. This method, which is theoretically justified, enables a fast calculation of the self and mutual inductances of the stator and rotor phases for every rotor position. The proposed method is validated first using a finite elements method (FEM) model, and then, through an experimental test-bed using commercial induction motors with a forced mixed eccentricity fault.

This paper introduces a new approach for improving the fault diagnosis in induction motors under timevarying conditions. A significant amount of published approaches in this field rely on representing the stator current in the time-frequency domain, and identifying the characteristic signatures that each type of fault generates in this domain. However, time-frequency transforms produce three-dimensional representations, very costly in terms of storage and processing resources. Moreover, the identification and evaluation of the fault components in the time-frequency plane requires a skilled staff or advanced pattern detection algorithms. The proposed methodology solves these problem by transforming the complex 3D spectrograms supplied by timefrequency tools into simplegraphs, similar to conventional Fourier spectra. These graphs display a unique pattern for each type of fault, even under supply or load time-varying conditions, making easy and reliable the diagnostic decision even for non-skilled staff. Moreover, the resulting patterns can be condensed in a very small dataset, reducing greatly the storage or transmission requirements regarding to conventional spectrograms. The proposed method is an extension to nonstationary conditions of the harmonic order tracking approach (HOTA). It is introduced theoretically and validated experimentally using commercial induction motors feed through electronic converters.

The diagnosis of induction machines using the Fourier transform relies on tracking the frequency signature of each type of fault in the current's spectrum. But this signature depends on the machine's slip and the supply frequency, so it must be recomputed for each working condition, by trained personnel or by diagnostic software. Besides, sampling the current at high rates during long times is needed to achieve a good spectral resolution, which requires a large memory space to store and process the current spectra. In this paper, a novel approach is proposed to solve both problems. It is based on the fact that each type of fault generates a series of harmonics in the current's spectrum, whose frequencies are multiples of a characteristic main fault frequency. The tracking analysis of the fault components using the harmonic order (defined as the frequency in per unit of the main fault frequency) as independent variable, instead of the frequency, generates a unique fault signature, which is the same for any working condition. Besides, this signature can be concentrated in just a very small set of values, the amplitudes of the components with integer harmonic order. This new approach is introduced theoretically and validated experimentally.

Fault diagnosis of induction motors through the analysis of the stator current is increasingly being used in maintenance systems, because it is non-invasive and has low requirements of hardware and software. Nevertheless, its industrial application faces some practical limitations. In particular, the detection of fault harmonics that are very close to the fundamental component is challenging, as in large induction motors working at very low slip, because the leakage of the fundamental can hide the fault components until the damage is severe. Several methods have been proposed to alleviate this problem, although all of them increase noticeably the complexity of the diagnostic system. In this paper, a novel method is proposed, based on the analysis of the rectified motor current. It is shown that its spectrum contains the same fault harmonics as the spectrum of the original current signal, but with a much lower frequency, and free from the fundamental component leakage. Besides, the proposed method is very easy to implement, either by software, using the absolute value of the current samples, or by hardware, using a simple rectifier. The proposed approach is presented theoretically and validated experimentally with the detection of a broken bars fault of a large induction motor.

Fault diagnosis of rotor asymmetries in induction machines working at a very low slip, through Fourier-based methods, usually requires a long acquisition time to achieve a high spectral resolution and a high sampling frequency to reduce aliasing effects. However, this approach generates a huge amount of data, which makes its implementation difficult using embedded devices with small internal memory, such as digital signal processors and field programmable gate arrays or devices with low computing power. In this paper, a new simplified diagnostic signal designated as the reduced envelope of the stator current is introduced to address this problem. The reduced envelope signal is built using only one sample of the current per cycle without any further processing, and it is demonstrated that it carries the same spectral information about the fault as the full-length current signal. Based on this approach, an embedded device has only to store and process a minimal set of samples compared with the raw current signal for a desired resolution. In this paper, the theoretical basis of the proposed method is presented, as well as its experimental validation using two different motors with broken bars: 1) a high-power induction motor working in a factory; and 2) a low-power induction motor mounted in a laboratory test bed.Index Terms-Broken rotor bar detection, condition monitoring, digital signal processor (DSP), fault diagnosis, field-programmable gate array (FPGA), Fourier transforms, Hilbert transforms (HTs), induction motor, motor-current signature analysis, signal processing. 0885-8969

Transient-based methods for fault diagnosis of induction machines are attracting a rising interest, due to their reliability and ability to adapt to a wide range of induction machine (IM)'s working conditions. These methods compute the time-frequency (TF) distribution of the stator current, where the patterns of the related fault components can be detected. A significant amount of recent proposals in this field have focused on improving the resolution of the TF distributions, allowing a better discrimination and identification of fault harmonic components. Nevertheless, as the resolution improves, computational requirements (power computing and memory) greatly increases, restricting its implementation in low cost devices for performing on-line fault diagnosis. To address these drawbacks, in this paper the use of the short frequency Fourier transform (SFFT) for fault diagnosis of IMs working under transient regimes is proposed. The SFFT not only keeps the resolution of traditional techniques, such as the short time Fourier transform (STFT), but also achieves a drastic reduction of computation time and memory resources, making this proposal suitable for on-line diagnosis. This method is theoretically introduced and experimentally validated using a laboratory test bench.

The discrete wavelet transform has attracted a rising interest in recent years to monitor the condition of rotating electrical machines in transient regime, because it can reveal the time-frequency behaviour of the current's components associated to fault conditions. Nevertheless, the implementation of the wavelet transform, especially on embedded or low-power devices, faces practical problems, such as the election of the mother wavelet, the tuning of its parameters, the coordination between the sampling frequency and the levels of the transform, and the construction of the bank of wavelet filters, with highly different bandwidths, that constitute the core of the discrete wavelet transform. In this paper, a diagnostic system using the harmonic wavelet transform is proposed, which can alleviate these practical problems because it is built using a single FFT of one phase's current. The harmonic wavelet was conceived to perform musical analysis, hence its name, and it has spread into many fields, but, up to the best of the authors' knowledge, it has not been applied before to perform fault diagnosis of rotating electrical machines in transient regime using the stator current. The simplicity and performance of the proposed approach are assessed by comparison with other types of wavelet transforms, and it has been validated with the experimental diagnosis of a 3.15 MW induction motor with broken bars.

Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine’s condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.