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

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Validation of a new method for the diagnosis of rotor bar failures via wavelet transform in industrial induction machines

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.

A General Approach for the Transient Detection of Slip-Dependent Fault Components Based on the Discrete Wavelet Transform

Condition monitoring of engineering plant has increased in importance as engineering processes are automated and manpower is reduced. However, electrical machinery receives attention only at infrequent intervals when plant is shut down and the application of protective relays to machines has also reduced operator surveillance. A first edition of Condition Monitoring of Electrical Machines, written by Tavner and Penman, was published in 1987. The economics of industry have now changed, as a result of the privatisation and deregulation of the energy industry, placing emphasis on the importance of reliable operation of plant, throughout the whole life cycle, regardless of first cost. The availability of advanced electronics and software in powerful instrumentation, computers, and digital signal processors (DSP) has simplified our ability to instrument and analyse machinery. As a result condition monitoring is now being applied to a wider range of systems, from fault-tolerant drives of a few hundred watts in the aerospace industry, to machinery of a few hundred megawatts in major capital plant. In this new book the original authors have been joined by Ran, an expert in power electronics and control, and Sedding, an expert in the monitoring of electrical insulation systems. Together the authors have revised and expanded the earlier book, merging their own experience with that of machine analysts to bring it up to date. The book is aimed at professional engineers in the energy, process engineering and manufacturing industries, plus research workers and students.

Condition Monitoring of Rotating Electrical Machines

The aim of this paper is to present a wavelet-based method for broken-bar detection in squirrel-cage induction machines. The frequency-domain methods, which are commonly used, need speed information or accurate slip estimation for frequency-component localization in any spectrum. Nevertheless, the fault frequency bandwidth can be well defined for any squirrel-cage induction machine due to numerous previous investigations. The proposed approach consists in the energy evaluation of a known bandwidth with time-scale analysis using the discrete wavelet transform. This new technique has been applied to the stator-current space-vector magnitude and the instantaneous magnitude of the stator-current signal for different broken-bar fault severities and load levels.

Diagnosis of Broken Bar Fault in Induction Machines Using Discrete Wavelet Transform without Slip Estimation

Overall system performance on a production line is one of the major concerns in modern industry where induction motors are present and their condition monitoring is mandatory. Periodic offline monitoring of the motor condition is usually performed in the industry, consuming production time and increasing cost. Broken rotor bars are among the most common failures in induction motors. Reported research projects give a broken-rotor-bar-detection methodology based on personal-computer implementation that is performed offline and requires an expert technician interpretation which is not a cost-effective solution. The novelty of this paper is the development of an automatic online diagnosis algorithm for broken-rotor-bar detection, optimized for single low-cost field-programmable gate-array (FPGA) implementation, which guarantees the development of economical self-operated equipment. The proposed algorithm requires less computation load than the previously reported algorithms, and it is mainly based on the discrete-wavelet-transform application to the start-up current transient; a further single mean-square computation determines a weighting function that, according to its value, clearly points the motor condition as either healthy or damaged. In order to validate the proposed algorithm, several tests were performed, and an FPGA implementation was developed to show the algorithm feasibility for automatic online diagnosis.

Automatic Online Diagnosis Algorithm for Broken-Bar Detection on Induction Motors Based on Discrete Wavelet Transform for FPGA Implementation

Detection of cage motor broken rotor bars has long been an important but difficult job in the detection area of motor faults. The characteristic frequency component of faulted rotor (CFCFR) is very close to the power frequency component but by far less in amplitude, which brings about great difficulty for accurate detection. A new detection method based on wavelet ridge is presented in this paper. Aiming at the motor's starting period during which the motor accelerates progressively and CFCFR approaches the power frequency gradually in frequency spectrum, the wavelet ridge-based method is adopted to analyze this transient procedure and the CFCFR is extracted. The influence of power frequency can be effectively eliminated, and detection accuracy can be greatly improved by using the approach presented in this paper. Also, this is indeed a novel but excellent approach for the detection domain of cage induction motor broken rotor bars.

A novel detection method of motor broken rotor bars based on wavelet ridge

Summary form only given. Detection of cage motor broken rotor bars has long been an important but difficult job in the detection area of motor faults. The characteristic frequency component of faulted rotor (CFCFR) is very close to the power frequency component but by far less in amplitude, which brings about great difficulty for accurate detection. A new detection method based on wavelet ridge is presented in This work. Aiming at motor starting period during which the motor accelerates progressively and CFCFR approaches the power frequency gradually in frequency spectrum, the wavelet ridge-based method is adopted to analyze this transient procedure and the CFCFR is extracted. The influence of power frequency can be effectively eliminated, and detection accuracy can be greatly improved by using the approach presented in this paper. Also, this is indeed a novel but excellent approach for the detection domain of cage induction motor broken rotor bars.

Wenying Huang

Papers

A novel detection method of motor broken rotor bars based on wavelet ridge

A novel detection method of motor broken rotor bars based on wavelet ridge