Multitask learning of health state assessment and remaining useful life prediction for sensor-equipped machines

Prognostics and health management (PHM) uses data collected through sensors to monitor the states of sensor -equipped machines and provide maintenance decisions. PHM includes two essential tasks, i.e., health state (HS) assessment and remaining useful life (RUL) prediction. In existing works, the two tasks are often conducted separately without considering the relationships between HS and RUL. In this paper, we propose a multitask deep learning model for simultaneously assessing the HS and predicting the RUL of a machine; the model consists of four modules: a shared module, a multigate mixture-of-experts (MMOE) layer, an HS module, and an RUL module. In the model, a shared module including a bidirectional long short-term memory (BiLSTM) layer, an encoding layer, and a sampling layer is used to extract the shared information of the two tasks. Then, the MMOE layer is built to identify different information according to the two tasks. In the output layer, the HS module with an attention mechanism is used to evaluate the HS of the studied machine. Moreover, the RUL module predicts the RUL and constructs RUL prediction intervals to quantify uncertainty. Finally, the proposed model outperforms the state-of-the-art benchmark models and is validated on a public dataset of engines.

Automated summary

This paper proposes a multitask deep learning model for simultaneously assessing the health state and predicting the remaining useful life of a machine. The model consists of four modules: a shared module, a multigate mixture-of-experts layer, a health state module, and a remaining useful life module. The proposed model outperforms the state-of-the-art benchmark models and is validated on a public dataset of engines.