Automated fault detection
Assembly Line
Hunting for anomalies: Automated fault detection in electrical motors using ML
The fault types in electrical machines can be divided into two main categories: electrical and mechanical faults. The physics behind these faults differs. Thanks to advanced signal processing, the symptoms can be observed by analyzing the vibration, stray flux, acoustics and electrical current characteristics of the motors. For instance, considering mechanical faults, these can be observed in specific harmonics of the motor’s vibration. Such harmonics can be revealed by performing a FFT on the measured vibration signals. On the other hand, wavelet and envelope analysis of the vibrations can also be used to observe various faults in a low-intervention manner. In earlier days, the monitoring of these harmonics was done manually or by setting simple thresholds on the amplitudes. These methods are neither effective nor practical, considering the number of assets that need monitoring, lack of universal thresholds and the influence of factors like machine characteristics, operating speed and load.
The final development step involved experimentation for model selection. The model has been trained using a predefined initial number of healthy data points. As evaluation metrics for such a binary classification problem with highly imbalanced data, precision-recall area under curve (PR-AUC) and Fβ-score with β=0.5 were selected to give more weight to precision and reduce false positives. PR-AUC and Fβ-score were used for model hyperparameter tuning and anomaly threshold optimization, respectively. In addition to these evaluation metrics, a constraint was employed to consider the computational burden of the calculations. One-Class Support Vector Machines (OCSVM), Isolation Forest, Minimum Covariance Determinant, Robust Random Cut Forest and Local Outlier Factor algorithms were studied; OCSVM was determined to be the best algorithm in terms of evaluation metrics and computational effort →04.