Broken rotor bars fault detection in induction motors based on current envelope and neural network

The growing demand for dependable manufacturing techniques has sped up research into condition monitoring and fault diagnosis of critical motor parts. On the other hand, in modern industry, machine maintenance is becoming increasingly necessary. An insufficient maintenance strategy can result in unnecessarily high downtime or accidental machine failure, resulting in significant financial and even human life losses. Downtime and repair costs rise as a result of failure. Furthermore, developing an online condition monitoring method may be one solution to come up for the problem. Early detection of faults is very vital since they grow quickly and can cause further problems to the motor. This paper proposes an effective strategy for the classification of broken rotor bars (BRBs) for induction motors (IMs) that uses a new approach based on Artificial Neural Network (ANN) and stator current envelope. The stator current envelope is extracted using the cubic spline interpolation process. This is based on the idea that the amplitude-modulated motor current signal can be revealed using the motor current envelope. The stator current envelope is used to select seven features, which will be used as input for the neural network. Five IM conditions were experimentally used in this study, including a part of BRB, 1 BRB, 2 BRBs and 3 BRBs. The new feature extraction and selection approach achieves a higher level of accuracy than the conventional method for motor fault classification, according to the experimental results. Indeed, the results are impressive, and it is capable of detecting the exact number of broken rotor bars under full load conditions

An Application Of A Hybrid Wavelet-SVM Based Approach For Induction Motor Fault Detection

Induction motors have been widely used in the industries due to their simple and rugged construction. Failures of this electrical machinery may cause considerable losses. Therefore adapting an efficient method to diagnose a fault at a very early stage would prevent any further consequences of this deficiency. The major concern is related to the mechanical failures, normally caused by the inner component deficiencies. Application of intelligent methods have attracted interest in recent years. Support Vector Machine is a supervised learning method, based on statistical learning theory. This thesis presents three different SVM algorithms: SVM, KPCA-SVM and ROC-SVM, applicable for broken rotor bars detection. SVM proved to be reliable method for classification. While application of KPCA-SVM, shows nonlinear feature extraction can improve the performance of classifier with respect to reduce the number of overlapping samples. Furthermore, ROC-SVM has improved the accuracy by selecting a decision threshold for the classifier.

An Application Of A Hybrid Wavelet-SVM Based Approach For Induction Motor Fault Detection

Induction motors have been widely used in the industries due to their simple and rugged construction. Failures of this electrical machinery may cause considerable losses. Therefore adapting an efficient method to diagnose a fault at a very early stage would prevent any further consequences of this deficiency. The major concern is related to the mechanical failures, normally caused by the inner component deficiencies. Application of intelligent methods have attracted interest in recent years. Support Vector Machine is a supervised learning method, based on statistical learning theory. This thesis presents three different SVM algorithms: SVM, KPCA-SVM and ROC-SVM, applicable for broken rotor bars detection. SVM proved to be reliable method for classification. While application of KPCA-SVM, shows nonlinear feature extraction can improve the performance of classifier with respect to reduce the number of overlapping samples. Furthermore, ROC-SVM has improved the accuracy by selecting a decision threshold for the classifier.