Vibration-based anomaly pattern mining for remaining useful life (RUL) prediction in bearings

Predicting the remaining useful life (RUL) of bearings is critical in ensuring rotating machinery's reliability and maintenance efficiency. Most of the research in this domain focuses on the fault prognosis of bearings without proper investigation of underlying fault feature pattern mining for degradation analysis. This paper investigates the remaining operational lifespan of bearings with an enhanced feature selection strategy and anomaly monitoring of bearing operational data. Specifically, four different models, namely Bi-LSTM, CNN-LSTM, Conv_LSTM, and encoder-decoder LSTM, are utilized to capture complex temporal dependencies and spatial correlations in the bearing sensor data. In the first stage, various feature selection techniques are engaged to select degradation trend monitoring features over time-domain and frequency-domain analysis. Next, anomaly pattern mining techniques are employed to identify abnormal behavior in the data, a crucial input for the subsequent RUL forecasting models. The anomaly patterns are extracted using unsupervised learning methods such as clustering or autoencoders, enabling the detection of early signs of degradation. Subsequently, the RUL forecasting is performed using four deep learning architectures. The performance of the suggested technique is evaluated using a comprehensive dataset of sensor measurements from bearings, which includes the corresponding remaining useful life (RUL) values. The experimental findings demonstrate that the proposed models demonstrate high accuracy in correctly determining the RUL of bearings. This solution offers proactive and cost-effective maintenance procedures by employing advanced deep learning models and anomalous pattern mining techniques, resulting in increased reliability, reduced downtime, and optimized resource allocation.