Deep Learning-based Health Indicator for Better Bearing RUL Prediction

Abstract

The prognostic performance of data-driven approaches closely depends on the features extracted from the measurement. For a high level of prognostic performance, features must be carefully designed to represent the machine's health state well and are generally obtained by signal processing techniques. These features are themselves used as health indicators (HI) or used to construct HIs. However, many conventional HIs are heavily relying on the type of machine components and expert domain knowledge. To solve these drawbacks, we propose a fully data-driven method, that is, the adversarial autoencoder-based health indicator (AAE-HI) for remaining useful life (RUL) prediction. Accelerated degradation tests of bearings collected from PRONOSTIA were used to validate the proposed AAE-HI method. It is shown that our proposed AAE-HI can autonomously find monotonicity and trendability of features, which will capture the degradation progression from the measurement. Therefore, the performance of AAE-HI in RUL prediction is promising compared with other conventional HIs.