Remaining Useful Life Prediction by Distribution Contact Ratio Health Indicator and Consolidated Memory GRU

Facing the gap in the unsupervised construction of health indicator (HI) with a uniform failure threshold, a new unsupervised HI construction approach is developed. First, the distribution of the raw vibration signal is estimated by the Gaussian mixture model, then a distribution contact ratio metric (DCRM) is designed to compute the distance between two arbitrary distributions. With DCRM, a distribution contact ratio metric health indicator (DCRHI) is innovatively constructed for well representing the degradation process and obtaining a uniform failure threshold. Next, aiming at the challenge of prediction under limited samples, a novel consolidated memory gated recurrent unit (CMGRU) is proposed by making full use of the historical state information, and it can effectively slow down the forgetting speed of important trend information. Combing the proposed DCRHI and CMGRU, a novel remaining useful life (RUL) prediction methodology is put forward for enhancing the predictive performance. Via two public bearing datasets, several contrast experiments are implemented, and the comparative results show that DCRHI can better describe the degradation process of bearing than other typical unsupervised HIs, and CMGRU has a stronger prediction ability than other classical time series processing networks. Thus, the proposed methodology has great application value in the RUL prediction.

Automated summary

Facing the gap in the unsupervised construction of health indicator (HI) with a uniform failure threshold, a new approach is developed by estimating the distribution of the raw vibration signal using the Gaussian mixture model and designing a distribution contact ratio metric (DCRM). A distribution contact ratio metric health indicator (DCRHI) is constructed to represent the degradation process and obtain a uniform failure threshold. Furthermore, a novel consolidated memory gated recurrent unit (CMGRU) is proposed to slow down the forgetting speed of important trend information and improve the prediction ability. The proposed methodology shows great application value in the RUL prediction.