The presence of electrical and mechanical faults in the induction motors (IMs) can be detected by analysis of the stator current spectrum. However, when an IM is fed by a frequency converter, the spectral analysis of stator current signal becomes difficult. For this reason, the monitoring must depend on multiple signatures in order to reduce the effect of harmonic disturbance on the motor-phase current. The aim of this paper is the description of a new approach for fault detection and diagnosis of IMs using signal-based method. It is based on signal processing and an unsupervised classification technique called the artificial ant clustering. The proposed approach is tested on a squirrel-cage IM of 5.5 kW in order to detect broken rotor bars and bearing failure at different load levels. The experimental results prove the efficiency of our approach compared with supervised classification methods in condition monitoring of electrical machines.

Detection and Diagnosis of Faults in Induction Motor Using an Improved Artificial Ant Clustering Technique

Induction motors are critical components in commercially available equipments and industrial processes due to cost effective and robust performance. Under various operating stresses, motors deteriorate their conditions which result into various faults. Early detection and diagnosis of these faults are desirable for online condition assessment, product quality assurance and improved operational efficiency. From the related work reported so far it is observed that researchers used vibration analysis, harmonics present in stator current, chemical analysis, electromagnetic analysis, etc. As these approaches are complex in view of the requirement of precise measurement and mathematical modeling. As compared to analytical methods, AI based schemes are more efficient and accurate. In this paper optimal MLP NN based classifier is proposed for fault detection which is inexpensive, reliable, and noninvasive by employing more readily available information such as stator current. Detailed design procedure for MLP and SOM NN models is given for which simple statistical parameters are used as input feature space and Principal Component Analysis is used for reduction of input dimensionality. Robustness of classifier to noise is verified on unseen data by introducing controlled Gaussian and Uniform noise in input and output.

Optimal MLP neural network classifier for fault detection of three phase induction motor

Induction motors are subject to different faults which, if undetected, may lead to serious machine failures. From the scrupulous review of related works, it is observed that neuro-fuzzy and neural network (NN)-based fault-detection schemes are performed well for large machines and they are not only expensive but also complex. In this paper, the authors developed the radial-basis-function-multilayer-perceptron cascade-connection NN-based fault-detection scheme for the small and medium sizes of three-phase induction motors. Stator winding interturn short, rotor eccentricity, and both faults simultaneously are selected for demonstration. Simple statistical parameters of stator current are considered as input features. Principal component analysis is used to select suitable inputs to the network. Experimental results are included to show the ability of the proposed classifier for detecting faults. Moreover, the network is tested for the robustness to the uniform and Gaussian noises. Having good classification accuracy with enough robustness to noises, the proposed classifier is suitable for the real-world applications.

Cascade Neural-Network-Based Fault Classifier for Three-Phase Induction Motor

This paper proposes an online fault diagnosis system for induction motors through the combination of discrete wavelet transform (DWT), feature extraction, genetic algorithm (GA), and neural network (ANN) techniques. The wavelet transform improves the signal-to-noise ratio during a preprocessing. Features are extracted from motor stator current, while reducing data transfers and making online application available. GA is used to select the most significant features from the whole feature database and optimize the ANN structure parameter. Optimized ANN is trained and tested by the selected features of the measurement data of stator current. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origins on the induction motors. The results of the test indicate that the proposed system is promising for the real-time application.

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Fault Diagnosis System of Induction Motors Based on Neural Network and Genetic Algorithm Using Stator Current Signals

An effective fault diagnosis method for induction motors is proposed in this paper to improve the reliability of motors using a combination of entropy feature extraction, mutual information, and support vector machine. Sample entropy and multiscale entropy are used to extract the desired entropy features from motor vibration signals. Sample entropy is used to estimate the complexity of the original time series while multiscale entropy is employed to measure the complexity of time series in different scales. The entropy features are directly extracted from the nonlinear, nonstationary induction motor vibration signals which are then sorted by using mutual information so that the elements in the feature vector are ranked according to their importance and relevant to the faults. The first five most important features are selected from the feature vectors and classified using support vector machine. The proposed method is then employed to analyze the vibration data acquired from a motor fault simulator test rig. The classification results confirm that the proposed method can effectively diagnose various motor faults with reasonable good accuracy. It is also shown that the proposed method can provide an effective and accurate fault diagnosis for various induction motor faults using only vibration data.

/pdf/fault-diagnosis-system-of-induction-motors-based-on-47wwzx4p9j.pdf

Fault Diagnosis System of Induction Motors Based on Multiscale Entropy and Support Vector Machine with Mutual Information Algorithm

This paper presents a laboratory assembly to perform, automatically, three-phase squirrel cage induction motor load tests. Besides the motor, mechanical load (fan), drive and electrical protection equipment, the assembly comprises a 12 bits data acquisition system, a software controlled electronic driving system, temperature sensors and a microcomputer. It is useful for fast and reliable motor part temperatures acquisition during any load test proposed via a computer keyboard allowing for the data storage for motor thermal behavior studies. The performance of the assembly was checked by performing load tests according to the duty types proposed in IEC Publ. 34-1, Rotating electrical machines, Part 1: Rating and Performance, 8th edition, 1983. >

E. Avolio

Papers

Automatic three-phase squirrel-cage induction motor test assembly for motor thermal behaviour studies