A convolutional neural network based degradation indicator construction and health prognosis using bidirectional long short-term memory network for rolling bearings

Health prognosis of rolling bearing is of great significance to improve its safety and reliability. This paper presents a novel health prognosis method for the rolling bearing based on convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) model. First, a new nonlinear degradation indicator (DI) is designed which can be utilized as training label. Then, through learning and capturing the mapping relationship between raw vibration signals and DI of the rolling bearing, a CNN model is introduced to estimate the DI value of the rolling bearing. And, BiLSTM models are set up to carry out health prognosis using the estimated DI, including future DI and remaining useful life prediction. An experiment verification is implemented to validate the effectiveness of the proposed method. Results show the excellent ability of future DI prediction, and demonstrate the superiority of the proposed method in the field of remaining useful life prediction compared with other existing deep learning models.

Automated summary

This paper presents a novel health prognosis method for rolling bearings using CNN and BiLSTM models, with a new nonlinear degradation indicator (DI) designed as a training label and the CNN model estimating the DI value. BiLSTM models are then used for health prognosis, including future DI prediction and remaining useful life prediction, showing excellent results compared to other existing deep learning models in this field.