A Novel Framework for Robust Bearing Fault Diagnosis: Preprocessing, Model Selection, and Performance Evaluation

Diagnosing bearing faults is crucial for maintaining, ensuring reliability, and extending the lifespan of rotary machines. This process helps prevent unexpected downtime in industries, ultimately reducing economic losses caused by the failure of rotary machines. Timely diagnosis of bearing faults is crucial to prevent catastrophic breakdowns, minimize maintenance expenses, and ensure uninterrupted productivity. With industries evolving rapidly and machines operating in increasingly diverse conditions, traditional fault detection methods face limitations. Despite extensive research in recent decades, there is an ongoing need for further advancements to enhance existing fault diagnosis techniques. This study addresses these challenges by utilizing advanced machine learning algorithms Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), Gated Recurrent Unit Network (GRU), Bidirectional LSTM, and for precise bearing fault diagnosis. Leveraging the CWRU dataset encompassing diverse fault classes and machine conditions, a comprehensive data preprocessing pipeline was executed to clean, normalize, and augment the dataset, ensuring model readiness and enhancing performance. Performance analysis revealed the proposed models achieving remarkable accuracies on the CWRU dataset. The CNN and LSTM models attained accuracies of 95%, while the RNN and GRU models achieved accuracies of 97% . Additionally, the Bidirectional LSTM model yielded an accuracy of 96%. These results signify substantial advancements in bearing fault diagnosis, emphasizing the models' efficacy in accurately detecting and categorizing faults within the 10 classes of the CWRU dataset. The findings underscore the potential of advanced machine learning techniques in revolutionizing fault diagnosis for rotary machines, addressing the persistent need for more robust and accurate diagnostic methodologies.