Support vector machine based classification in condition monitoring of induction motors

Continuous and trouble-free operation of induction motors is an essential part of modern power and production plants. Faults and failures of electrical machinery may cause remarkable economical losses but also highly dangerous situations. In addition to analytical and knowledge-based models, application of data-based models has established a firm position in the induction motor fault diagnostics during the last decade. For example, pattern recognition with Neural Networks (NN) is widely studied. Support Vector Machine (SVM) is a novel machine learning method introduced in early 90's. It is based on the statistical learning theory presented by V.N. Vapnik, and it has been successfully applied to numerous classification and pattern recognition problems such as text categorization, image recognition and bioinformatics. SVM based classifier is built to minimize the structural misclassification risk, whereas conventional classification techniques often apply minimization of the empirical risk. Therefore, SVM is claimed to lead enhanced generalisation properties. Further, application of SVM results in the global solution for a classification problem. Thirdly, SVM based classification is attractive, because its efficiency does not directly depend on the dimension of classified entities. This property is very useful in fault diagnostics, because the number of fault classification features does not have to be drastically limited. However, SVM has not yet been widely studied in the area of fault diagnostics. Specifically, in the condition monitoring of induction motor, it does not seem to have been considered before this research. In this thesis, a SVM based classification scheme is designed for different tasks in induction motor fault diagnostics and for partial discharge analysis of insulation condition monitoring. Several variables are compared as fault indicators, and forces on rotor are found to be important in fault detection instead of motor current that is currently widely studied. The measurement of forces is difficult, but easily measurable vibrations are directly related to the forces. Hence, vibration monitoring is considered in more detail as the medium for the motor fault diagnostics. SVM classifiers are essentially 2-class classifiers. In addition to the induction motor fault diagnostics, the results of this thesis cover various methods for coupling SVMs for carrying out a multi-class classification problem.reviewe

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

Diagnosis of Stator Turn-to-Turn Fault and Stator Voltage Unbalance Fault Using ANFIS

An induction machine is a highly non-linear system that poses a great challenge because of its fault diagnosis due to the processing of large and complex data. The fault in an induction machine can lead to excessive downtimes that can lead to huge losses in terms of maintenance and production. This paper discusses the diagnosis of stator winding faults, which is one of the common faults in an induction machine. Several diagnostics techniques have been presented in the literature. Fault detection using traditional analytical methods are not always possible as this requires prior knowledge of the exact motor model. The motor models are also susceptible to inaccuracy due to parameter variations. This paper presents Adaptive Neuro-fuzzy Inference system (ANFIS) based fault diagnosis of induction motors. The distinction between the stator winding fault and supply unbalance is addressed in this paper. Experimental data is collected by shorting the turns of a health motor as well as creating unbalance in the stator voltage. The data is processed and fed to an ANFIS classifier that accurately identifies the faulted condition and unbalanced supply voltage conditions. The ANFIS provides almost 99% accurate and computationally efficient output in diagnosing the faults and unbalance conditions.DOI:http://dx.doi.org/10.11591/ijece.v3i1.185

Induction Motors

AC motors play a major role in modern industrial applications. Squirrel-cage induction motors (SCIMs) are probably the most frequently used when compared to other AC motors because of their low cost, ruggedness, and low maintenance. The material presented in this book is organized into four sections, covering the applications and structural properties of induction motors (IMs), fault detection and diagnostics, control strategies, and the more recently developed topology based on the multiphase (more than three phases) induction motors. This material should be of specific interest to engineers and researchers who are engaged in the modeling, design, and implementation of control algorithms applied to induction motors and, more generally, to readers broadly interested in nonlinear control, health condition monitoring, and fault diagnosis

Detection and Classification of Stator Short-Circuit Faults in Three-Phase Induction Motor

Induction motors are the backbone of the industries because they are easy to operate, rugged, economical and reliable. However, they are subjected to stator’s faults which damage the windings and consequently lead to machine failure and loss of revenue. Early detection and  classification of these faults are important for the effective operation of induction motors. Stators faults detection and classification based on  wavelet Transform was carried out in this study. The feature extraction of the acquired data was achieved using lifting decomposition and reconstruction scheme while Euclidean distance of the Wavelet energy was used to classify the faults. The Wavelet energies increased for all three conditions monitored, normal condition, inter-turn fault and phase-to-phase fault, as the frequency band of the signal decreases from D1 to A3. The deviations in the Euclidean Distance of the current of the Wavelet energy obtained for the phase-to-phase faults are 99.1909, 99.8239 and 87.9750 for phases A and B, A and C, B and C respectively. While that of the inter-turn faults in phases A, B and C are 77.5572, 61.6389 and 62.5581 respectively. Based on the Euclidean distances of the faults, Df and normal current signals, three classification points were set: K1 = 0.60 x 102, K2 = 0.80 x 102 and K3 = 1.00 x 102. For K2 ≥ Df ≥ K1 inter-turn faults is identified and for K3 ≥ Df ≥ K2 phase to phase fault identified. This will improve the induction motors stator’s fault diagnosis. Keywords: induction motor, stator fault classification, data acquisition system, Discrete Wavelet Transfor

Current Based Fault Detection and Diagnosis of Induction Motors. Adaptive Mixed-Residual Approach for Fault Detection and Diagnosis of Rotor, Stator, Bearing and Air-Gap Faults in Induction Motors Using a Fuzzy Logic Classifier with Voltage and Current Measurement only.

Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications.The full-text was made available at the end of the embargo period on 29th Sept 2017

Diagnosis of broken bars in wind turbine squirrel cage induction generator: Approach based on current signal and generative adversarial networks

Producción CientíficaTo ensure the profitability of the wind industry, one of the most important objectives is to minimize maintenance costs. For this reason, the components of wind turbines are continuously monitored to detect any type of failure by analyzing the signals measured by the sensors included in the condition monitoring system. Most of the proposals for the detection and diagnosis of faults based on signal processing and artificial intelligence models use a fault-free signal and a signal acquired on a system in which a fault has been provoked; however, when the failures are incipient, the frequency components associated with the failures are very close to the fundamental component and there are incomplete data, the detection and diagnosis of failures is difficult. Therefore, the purpose of this research is to detect and diagnose failures of the electric generator of wind turbines in operation, using the current signal and applying generative adversarial networks to obtain synthetic data that allow for counteracting the problem of an unbalanced dataset. The proposal is useful for the detection of broken bars in squirrel cage induction generators, which, according to the control system, were in a healthy state

The use of a Multi-label Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults based on the Start-up Transient

[EN] In this article a data driven approach for the classification of simultaneously occurring faults in an induction motor is presented. The problem is treated as a multi-label classification problem with each label corresponding to one specific fault. The faulty conditions examined, include the existence of a broken bar fault and the presence of mixed eccentricity with various degrees of static and dynamic eccentricity, while three "problem transformation" methods are tested and compared. For the feature extraction stage, the startup current is exploited using two well-known time-frequency (scale) transformations. This is the first time that a multi-label framework is used for the diagnosis of co-occurring fault conditions using information coming from the start-up current of induction motors. The efficiency of the proposed approach is validated using simulation data with promising results irrespective of the selected time-frequency transformation.This work was supported in part by the Spanish MINECO and FEDER program in the framework of the "Proyectos I + D del Subprograma de Generacion de Conocimiento, Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia" under Grant DPI2014-52842-P and in part by the Horizon 2020 Framework program DISIRE under the Grant Agreement 636834.Georgoulas, G.; Climente Alarcón, V.; Antonino-Daviu, J.; Tsoumas, IP.; Stylios, CD.; Arkkio, A.; Nikolakopoulos, G. (2016). The use of a Multi-label Classification Framework for the Detection of Broken Bars and Mixed Eccentricity Faults based on the Start-up Transient. IEEE Transactions on Industrial Informatics. 13(2):625-634. https://doi.org/10.1109/TII.2016.2637169S62563413