Deep Learning-Based Fault Prediction Model for Electrical Equipment

This paper provides a new model for predicting faults in electrical gear using deep learning techniques along with multi-source data and sophisticated signal processing methods. To adequately analyse and understand multi-source sensor information, the model uses a hybrid structure that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and attention components. An all-inclusive feature extraction framework is proposed which consists of and captures temporal and spectral components of the equipment operational data. These are time domain features, frequency domain features, and wavelet transformations. The model was verified by real case data gathered from 50 units of electrical equipment over a duration of twelve months. It included different types of faults such as bearing faults, stator winding faults, and rotor bar problems. The results for the experiments indicate that the model is capable of 95% accuracy in fault prediction which is significantly higher than the results produced by previous machine learning algorithms and older deep learning approaches. The model is shown to provide early fault detection from 12 to 48 hours depending on the fault type allowing maintenance to be planned in advance. Additionally, the attention component is found to be very useful in detecting small fault traces while the hybrid structure guarantees reasonable robustness to different working conditions. This study helps refine predictive maintenance in an industrial context, which may lessen the time that equipment is out of order and the costs of maintaining it.