To evaluate degradation processes of rolling bears in real-time devices, health indicators (HIs) are required to be built. Due to the constraints of sensors, the degradation pattern cannot be denoted by commonly used signals such as vibration data. Moreover, the practical requirements of HI for prognostics are always ignored, such as monotonicity and trendability. Therefore, a novel HI construction method based on reinforcement learning (RL) is proposed.

Reinforcement learning; HI construction; Data fusion; RUL