Abstract
Deep learning has a strong feature learning ability, which has proved its effectiveness in fault prediction and remaining useful life prediction of rotatory machine. However, training a deep network from scratch requires a large amount of training data and is time-consuming. In the practical model training process, it is difficult for the deep model to converge when the parameter initialization is inappropriate, which results in poor prediction performance. In this paper, a novel deep learning framework is proposed to predict the remaining useful life of rotatory machine with high accuracy. Firstly, model parameters and feature learning ability of the pretrained model are transferred to the new network by means of transfer learning to achieve reasonable initialization. Then, the specific sensor signals are converted to RGB image as the specific task data to fine-tune the parameters of the high-level network structure. The features extracted from the pretrained network are the input into the Bidirectional Long Short-Term Memory to obtain the RUL prediction results. The ability of LSTM to model sequence signals and the dynamic learning ability of bidirectional propagation to time information contribute to accurate RUL prediction. Finally, the deep model proposed in this paper is tested on the sensor signal dataset of bearing and gearbox. The high accuracy prediction results show the superiority of the transfer learning-based sequential network in RUL prediction.
Highlights
Rotatory machine serves as a significant element in mechanical systems while its working conditions are directly related to the production efficiency and production safety [1,2,3]
Inspired by these prior researches, a novel remaining useful life (RUL) prediction model based on Bidirectional Long Short-Term Memory (LSTM) and transfer learning strategy is proposed. e proposed model utilizes the first two convolution blocks of Residual Network (ResNet-50) as sensor signal feature extractor and outputs the predicted RUL values by Bidirectional LSTM. e main contributions of this paper are summarized as follows: (1) A novel RUL prediction framework of rotatory machine based on sequential network is proposed and combined with transfer learning strategy to improve the training efficiency
A pretrained network trained by ImageNet dataset is designed as a feature extractor at the first stage and the advanced parameters of the whole framework are finetuned by specific sensor signals of rotatory machine, which greatly reduces the difficulty of deep network training and realizes efficient RUL prediction
Summary
Rotatory machine serves as a significant element in mechanical systems while its working conditions are directly related to the production efficiency and production safety [1,2,3]. Zhang et al proposed that a new approach based on the LSTM network models the system degradation process, and it has the capability to learn specific patterns from time series [35] Inspired by these prior researches, a novel RUL prediction model based on Bidirectional LSTM and transfer learning strategy is proposed. (1) A novel RUL prediction framework of rotatory machine based on sequential network is proposed and combined with transfer learning strategy to improve the training efficiency. A pretrained network trained by ImageNet dataset is designed as a feature extractor at the first stage and the advanced parameters of the whole framework are finetuned by specific sensor signals of rotatory machine, which greatly reduces the difficulty of deep network training and realizes efficient RUL prediction.
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