Fault Detection in Machine Bearings using Deep Learning - LSTM 2024-26-0473

In today's industrial sphere, machines are the key supporting various sectors and their operations. Over time, due to extensive usage, these machines undergo wear and tear, introducing subtle yet consequential faults that may go unnoticed. Given the pervasive dependence on machinery, the early and precise detection of these faults becomes a critical necessity. Detecting faults at an early stage not only prevents expensive downtimes but also significantly improves operational efficiency and safety standards. This research focuses on addressing this crucial need by proposing an effective system for condition monitoring and fault detection, leveraging the capabilities of advanced deep learning techniques. The study delves into the application of five diverse deep learning models—LSTM, Deep LSTM, Bi LSTM, GRU, and 1DCNN—in the context of fault detection in bearings using accelerometer data. Accelerometer data is instrumental in capturing vital vibrations within the machinery. To optimize the analysis, a preprocessing phase involves the extraction of time domain features, carefully selecting various signals such as normal, differentiated, and integrated components. This meticulous feature extraction process is pivotal in constructing a comprehensive and informative dataset, laying the foundation for training robust deep learning models. Remarkably, our research uncovers that all five deep learning models—LSTM, Deep LSTM, Bi LSTM, GRU, and 1DCNN—achieved a flawless 100% accuracy in detecting faults. To discern the most efficient model, a comparative assessment based on computation time was conducted. Encouragingly, the results unequivocally endorse GRU as the most efficient choice among the five models. In conclusion, this study underscores the critical significance of early fault detection in the realm of industrial machinery. The flawless fault detection achieved through these five diverse deep learning models, especially the efficiency demonstrated by GRU, holds immense promise in ensuring timely and accurate fault detection. This discovery has far-reaching implications, potentially mitigating operational disruptions and substantially reducing maintenance costs. By leveraging deep learning models and accelerometer data, this research represents a substantial advancement in the domain of predictive maintenance, ultimately contributing to a more reliable and sustainable industrial landscape.