Dynamic warping as a sensor reconstruction method for remaining useful life estimation

Abstract

This study proposes Dynamic Warping (DW) as a sensor reconstruction method for Remaining Useful Life (RUL) estimation. The method utilizes the DW model for sensor reconstruction, where Median Absolute Deviation measures the reconstruction error, which is expected to increase when abnormal system behavior is measured. We apply an exponential model to the reconstruction error to estimate a system’s RUL. The DW model is based on the Dynamic Time Warping algorithm applied to a non-temporal context. The concept is to preprocess sensor data into a non-temporal motion profile representing a cycle. We validate our proposed DW model with two baseline models: Singular Value Decomposition (SVD) and LSTM Autoencoder (LSTM-AE). The SVD model is applied to the non-temporal motion profile, while the LSTM-AE model is applied to the original sensor data. A case study was conducted at a semiconductor Original Equipment Manufacturer, whose dataset contained information on a bearing failure in a water-cooled direct drive rotary motor. The failure occurred due to increased friction caused by bearing wear. The valuable motion control signal found was torque applied to the shaft for the R and S phases. It was demonstrated that the proposed method is most efficient, and an alarm can be raised 11 hours before failure, after which the RUL can be estimated, which is promising for warning service engineers for this industrial application. This research shows that the DW model could predict maintenance furthest in advance while only needing a fraction of the training data.