A new approach for remaining useful life prediction of bearings using 1D-ternary patterns with LSTM

Bearings frequently experience malfunctions in mechanical systems, directly impacting system performance. Accurate prediction of bearing failures is crucial for maintenance planning and preventing unexpected system breakdowns. Data-driven prognostic techniques are commonly employed to estimate the remaining useful life (RUL) of high-speed bearings. RUL prediction relies on establishing the fundamental relationship between bearing degradation and its current health status, with the accuracy depending on effective feature extraction from the bearing data. In this study, a novel approach is proposed for the RUL prediction of bearings. The 1D-TP method is applied to vibration signals, resulting in two feature vectors, LOWER and UPPER, which are then utilized in combination with LSTM for RUL prediction. The proposed approach is evaluated using a dataset from the PRONOSTIA platform, and performance metrics including MAE, RMSE, SMAPE, RA, and Score are determined. The results demonstrate that the 1D-TP + LSTM method successfully predicts the remaining life of bearings. Accurate RUL assessment and reliability analysis aid personnel in making informed maintenance decisions, preventing losses from mechanical system damage, improving production safety, and safeguarding the mechanical system from harm.

Automated summary

This study proposes a novel approach for the remaining useful life (RUL) prediction of bearings. The 1D-TP method is applied to vibration signals and combined with LSTM for accurate RUL assessment. The results demonstrate the successful prediction of bearing life using the 1D-TP + LSTM method.