Long short-term memory based fault diagnosis of rolling element bearings using vibration signals

The reliable and efficient operation of rotating machinery in various industries relies on the condition of bearings, which play a crucial role in reducing friction, supporting loads, preventing wear, and extending equipment lifespan. However, accurate and timely fault detection of bearings is essential to ensure smooth system performance and prevent unexpected failures. This study presents an integrated approach using Long Short-Term Memory (LSTM) networks and Empirical Mode Decomposition (EMD) to enhance the diagnostic accuracy for fault diagnosis of rolling element bearings. The experiment involved operating a test rig over 2000 h in a controlled environment at a constant 800 r/min speed and 2.1 kN load to induce gradual wear defects on the bearing surface. Vibration signals were acquired at various stages of the experiment. The EMD enhanced the acquired signal and selected the optimum Intrinsic mode function (IMF) using the maximum energy ratio method, these signals were used to implement the LSTM model to classify the various stages of bearing faults. The model was evaluated using accuracy, confusion matrix, and t-SNE visualization, achieving an average prediction accuracy of 96.6%. The findings suggest that the proposed model provides a reliable diagnostic tool to enhance fault diagnosis accuracy in rolling element bearings.