Fault Detection and Diagnosis of Electric Drives Using Intelligent Machine Learning Approaches

Electric motor condition monitoring can detect anomalies in the motor performance which have the potential to result in unexpected failure and financial loss. This study examines different fault detection and diagnosis approaches in induction motors and is presented in six chapters. First, an anomaly technique or outlier detection is applied to increase the accuracy of detecting broken rotor bars. It is shown how the proposed method can significantly improve network reliability by using one-class classification technique. Then, ensemble-based anomaly detection is utilized to compare different methods in ensemble learning in detection of broken rotor bars. Finally, a deep neural network is developed to extract significant features to be used as input parameters of the network. Deep autoencoder is then employed to build an advanced model to make predictions of broken rotor bars and bearing faults occurring in induction motors with a high accuracy

Ensemble of One-Class Classifiers for Detecting Faults in Induction Motors

This paper studies the use of an ensemble of one-class classifiers for broken rotor bars detection in an induction motors. To achieve this goal, the current signal of induction motor is considered into account for the sake of detection. The fault detector is a multiple classifiers system (MCS), which combines various one-class classifiers to enhance the accuracy of the monitoring system compared to individual one-class classifiers. One-class classifiers are combined in different manners to form the ensembles. These include random subspace, bagging and boosting strategies. These ensemble-based schemes are constructed in homogeneous and heterogeneous configuration and compared together for the purpose of fault detection in induction motors

One-class classifiers for detecting faults in induction motors

This paper deals with the problem of broken rotor bar detection in induction motors. In this technique current signal monitoring of an induction motor is applied to characterize its operation condition, and identify the normal state from broken rotor bar situation. To this aim, a fault detection scheme is proposed, which makes use of a low computational cost pre-processing method along with the six state-of-the-art one-class classifiers. Moreover, a feature selection approach is employed to form a suitable feature subset that is highly correlated with the operating condition of the machine. The achieved experimental results show the effectiveness of the proposed detection technique