Multi-Sensor Data-Driven Remaining Useful Life Prediction of Semi-Observable Systems

Semi-observable systems are referred to as a kind of widely used industrial equipment whose physical degradation state is only observable via shutdown inspection. To monitor the degradation process of semi-observable systems online, different types of sensors are generally employed to collect monitoring signals. Lots of studies have been conducted to fuse multi-sensor signals to predict remaining useful life (RUL). Majority of them, however, ignored the partially available state observations which can be viewed as ground truth measurements of physical degradation. To deal with this problem, this paper proposes a multi-sensor data-driven RUL prediction method for semi-observable systems, which leverages degradation information from online multi-sensor signals as well as offline state observations. This method is developed based on a generalizable state-space model combined with particle filtering (PF) framework. In the framework, a state transition function is used to describe the degradation process of system states. A multi-dimensional measurement function is constructed to describe the mapping between states and multi-sensor signals. To enhance the performance of prediction, an algorithm named prioritized sensor group selection (PSGS) is also proposed to select the optimal sensor group for RUL prediction. The effectiveness of the proposed method is demonstrated using an experiment of cutting tool wear.