Remaining Useful Life Prediction of Rolling Bearings Based on Adaptive Continuous Deep Belief Networks and Improved Kernel Extreme Learning Machine

Rolling bearings are crucial components in a wide variety of machinery. Monitoring their conditions and predicting their remaining useful life (RUL) is vital to prevent unexpected breakdowns, optimize maintenance schedules, and reduce operational costs. This article proposes an approach based on adaptive continuous deep belief networks (ACDBN) and improved kernel extreme learning machine (KELM) to predict the RUL of rolling bearings. In the proposed approach, the ACDBN model is used for extracting hidden fault features and the distance between the initial health state and the real-time degradation state is used to construct a health indicator (HI). Then, a hybrid kernel extreme learning machine prediction model optimized by the sparrow search algorithm (SSA-KELM) is proposed to estimate the RUL using the extracted HIs. The SSA is used to find the optimal parameters of the KELM model. The proposed method has been assessed using existing bearing datasets. The obtained results indicate that the proposed method successfully improves RUL prediction accuracy compared to existing approaches in the literature. image First, an unsupervised HI construction method is proposed, which can be used for predicting the rolling bearing's RUL effectively; Then, an adaptive CDBN model is proposed to automatically learn and extract the HIs from raw sensor data of rolling bearings; Next, a hybrid SSA-KELM model is designed to predict the rolling bearings RUL, in which the key parameters of KELM are optimized by SSA method. The proposed method can enhance the prediction accuracy and computational efficiency.