Vicente Climente-Alarcon

Induction Motor Diagnosis Based on a Transient Current Analytic Wavelet Transform via Frequency B-Splines

In this paper, a new induction motor diagnosis methodology is proposed. The approach is based on obtaining a 2-D time-frequency plot representing the time-frequency evolution of the main components in an electrical machine transient current. The identification of characteristic patterns in the time-frequency plane caused by many of the fault-related components enables a reliable machine diagnosis. Unlike other continuous-wavelet-transform-based methods, this work uses frequency B-spline (FBS) wavelets. It is shown that these wavelets enable an efficient filtering in the region neighboring the main frequency, as well as enable a high level of detail in the time-frequency maps. As a consequence, the evolution of the most important current components is precisely traced. These characteristics make it easy to identify the patterns related to the fault components. The technique is applied to the experimental no-load start-up current of motors in a healthy state and with broken bars; the FBS capabilities are revealed. One of the novelties of this paper is the fact that the diagnosis is carried out via the identification not only of the traditional lower sideband harmonic but also of the upper sideband harmonic and four additional fault-related components.

Induction Motor Diagnosis by Advanced Notch FIR Filters and the Wigner–Ville Distribution

During the last years, several time-frequency decomposition tools have been applied for the diagnosis of induction motors, for those cases in which the traditional procedures, such as motor current signature analysis, cannot yield the necessary response. Among them, the Cohen distributions have been widely selected to study transient and even stationary operation due to their high-resolution and detailed information provided at all frequencies. Their main drawback, the cross-terms, has been tackled either modifying the distribution, or carrying out a pretreatment of the signal before computing its time-frequency decomposition. In this paper, a filtering process is proposed that uses advanced notch filters in order to remove constant frequency components present in the current of an induction motor, prior to the computation of its distribution, to study rotor asymmetries and mixed eccentricities. In transient operation of machines directly connected to the grid, this procedure effectively eliminates most of the artifacts that have prevented the use of these tools, allowing a wideband analysis and the definition of a precise quantification parameter able to follow the evolution of their state.

Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines

This article presents a novel computational method for the diagnosis of broken rotor bars in three phase asynchronous machines. The proposed method is based on Principal Component Analysis (PCA) and is applied to the stators three phase start-up current. The fault detection is easier in the start-up transient because of the increased current in the rotor circuit, which amplifies the effects of the fault in the stators current independently of the motors load. In the proposed fault detection methodology, PCA is initially utilized to extract a characteristic component, which reflects the rotor asymmetry caused by the broken bars. This component can be subsequently processed using Hidden Markov Models (HMMs). Two schemes, a multiclass and a one-class approach are proposed. The efficiency of the novel proposed schemes is evaluated by multiple experimental test cases. The results obtained indicate that the suggested approaches based on the combination of PCA and HMMs, can be successfully utilized not only for identifying the presence of a broken bar but also for estimating the severity (number of broken bars) of the fault.

Vibration Transient Detection of Broken Rotor Bars by PSH Sidebands

In recent years, the study of transients of induction motors for diagnosis purposes has gained strength in order to overcome some inherent problems of the classical diagnosis of such machines, which uses Fourier analysis of steady-state quantities. Novel time-frequency techniques have been applied to these transient quantities, in order to detect the characteristic evolutions of fault-related harmonic components. The detection of these patterns, which are usually specific for each type of fault, enables reliable diagnostic of the corresponding failures. In this context, most of the works hitherto developed have been based on analysis of currents. However, in some applications, vibration measurements are also available. The goal of this work is to validate the applicability of this transient-based diagnosis framework to vibration measurements. A specific time-frequency decomposition tool, the Zhao-Atlas-Marks distribution, is proposed. Experimental results prove the ability of the approach to complement the information obtained from the current analysis. This may be very useful in applications in which the diagnosis via currents is uncertain or in which vibration signals can be easily measured.

Application of the Teager–Kaiser Energy Operator to the Fault Diagnosis of Induction Motors

The diagnosis of induction motors through the spectral analysis of the stator current allows for the online identification of different types of faults. One of the major difficulties of this method is the strong influence of the mains component of the current, whose leakage can hide fault harmonics, especially when the machine is working at very low slip. In this paper, a new method for demodulating the stator current prior to its spectral analysis is proposed, using the Teager-Kaiser energy operator. This method is able to remove the mains component of the current with an extremely low usage of computer resources, because it operates just on three consecutive samples of the current. Besides, this operator is also capable of increasing the signal-to-noise ratio of the spectrum, sharpening the spectral peaks that reveal the presence of the faults. The proposed method has been deployed to a PC-based offline diagnosis system and tested on commercial induction motors with broken bars, mixed eccentricity, and single-point bearing faults. The diagnostic results are compared with those obtained through the conventional motor current signature analysis method.

Automatic Pattern Identification Based on the Complex Empirical Mode Decomposition of the Startup Current for the Diagnosis of Rotor Asymmetries in Asynchronous Machines

This paper presents an advanced signal processing method applied to the diagnosis of rotor asymmetries in asynchronous machines. The approach is based on the application of complex empirical mode decomposition to the measured start-up current and on the subsequent extraction of a specific complex intrinsic mode function. Unlike other approaches, the method includes a pattern recognition stage that makes possible the automatic identification of the signature caused by the fault. This automatic detection is achieved by using a reliable methodology based on hidden Markov models. Both experimental data and a hybrid simulation-experimental approach demonstrate the effectiveness of the proposed methodology.

Combination of Noninvasive Approaches for General Assessment of Induction Motors

There exists no single quantity able to diagnose all possible failures taking place in induction motors. Currents and vibrations monitoring are rather common in the industry, but each of these quantities alone can only detect some specific failures. Moreover, even for the specific faults that a quantity is supposed to detect, many problems may rise. As a consequence, a reliable and general diagnosis system cannot rely on a single quantity. On the other hand, it would be desirable to rely on quantities that can be measured in a noninvasive way, which is a crucial requirement in many industrial applications. This paper proposes a twofold method to detect electromechanical failures in induction motors. The method relies on analysis of currents (steady state + transient) combined with analysis of infrared data captured by using appropriate cameras. Each of these noninvasive techniques may provide complementary information that may be very useful to diagnose an enough wide range of failures. In the present paper, the detection of three illustrative faults is analyzed: broken rotor bars, cooling system problems and bearing failures. The results show the potential of the methodology that may be particularly suitable for large, expensive motors, where the prevention of eventual failures justifies the costs of such system, due to the catastrophic implications that these unexpected faults may have.

A Symbolic Representation Approach for the Diagnosis of Broken Rotor Bars in Induction Motors

One of the most common deficiencies of currently existing induction motor fault diagnosis techniques is their lack of automatization. Many of them rely on the qualitative interpretation of the results, a fact that requires significant user expertise, and that makes their implementation in portable condition monitoring devices difficult. In this paper, we present an automated method for the detection of the number of broken bars of an induction motor. The method is based on the transient analysis of the start-up current using wavelet approximation signal that isolates a characteristic component that emerges once a rotor bar is broken. After the isolation of this component, a number of stages are applied that transform the continuous-valued signal into a discrete one. Subsequently, an intelligent icon-like approach is applied for condensing the relative information into a representation that can be easily manipulated by a nearest neighbor classifier. The approach is tested using simulation as well as experimental data, achieving high-classification accuracy.

Rotor-Bar Breakage Mechanism and Prognosis in an Induction Motor

This paper proposes a condition-based maintenance and prognostics and health management (CBM/PHM) procedure for a rotor bar in an induction motor. The methodology is based on the results of a fatigue test intended to reproduce in the most natural way a bar breakage in order to carry out a comparison between transient and stationary diagnosis methods for incipient fault detection. Newly developed techniques in stator-current transient analysis have allowed tracking the developing fault during the last part of the test, identifying the failure mechanism, and establishing a physical model of the process. This nonlinear failure model is integrated in a particle filtering algorithm to diagnose the defect at an early stage and predict the remaining useful life of the bar. An initial generalization of the results to conditions differing from the ones under which the fatigue test was developed is studied.

Electric machines diagnosis techniques via transient current analysis

Induction motor condition monitoring has primarily relied on the analysis of currents during steady-state operation through the Fast Fourier Transform (FFT). Nonetheless, this conventional approach has many constraints, most of them related to the usual operation of most motors under situations differing from a pure stationary regime, or directly under transient conditions. Indeed, these situations are the most common in many industrial processes, a fact that reveals the necessity of developing techniques suited for the analysis of machine quantities during non-stationary operation. A vast work has been developed during these recent years in this area, raising many transient-based techniques being able to diagnose machines under transient conditions which furthermore overcome some of the drawbacks of conventional stationary analysis. Most of these techniques are based on the application of proper signal processing tools, especially adapted to analyze transient signals (time-frequency decomposition (TFD) tools). This paper carries out a review of the most significant techniques sustained on transient-analysis, grouping them in accordance to the nature of the TFD tool used in each case. The review intends to emphasize the most relevant contributions of each work and to serve as a useful reference to all authors involved in the area.