Abstract
Remaining useful life (RUL) prediction is necessary for guaranteeing machinery’s safe operation. Among deep learning architectures, convolutional neural network (CNN) has shown achievements in RUL prediction because of its strong ability in representation learning. Features from different receptive fields extracted by different sizes of convolution kernels can provide complete information for prognosis. The single size convolution kernel in traditional CNN is difficult to learn comprehensive information from complex signals. Besides, the ability to learn local and global features synchronously is limited to conventional CNN. Thus, a multiscale convolutional neural network (MS-CNN) is introduced to overcome these aforementioned problems. Convolution filters with different dilation rates are integrated to form a dilated convolution block, which can learn features in different receptive fields. Then, several stacked integrated dilated convolution blocks in different depths are concatenated to extract local and global features. The effectiveness of the proposed method is verified by a bearing dataset prepared from the PRONOSTIA platform. The results turn out that the proposed MS-CNN has higher prediction accuracy than many other deep learning-based RUL methods.
Highlights
Prognostics and health management (PHM) are crucial for the mechanical system
Bearings are the critical parts of the mechanical system [1, 2]. e failure of bearings may lead to a severe accident. us, the bearings Remaining useful life (RUL) prediction has drawn more and more attention in the study of PHM
A deep long short-term memory (DLSTM) network was proposed in Ref. [8]
Summary
Prognostics and health management (PHM) are crucial for the mechanical system. RUL prediction is one of the important tasks in modern industry PHM. Us, the bearings RUL prediction has drawn more and more attention in the study of PHM. E bearing RUL prediction methods can be roughly divided into two types: model-based approaches and datadriven approaches [3]. Since DL-based approaches can extract features from the input data without much prior knowledge, they have become more and more popular in RUL prediction and fault diagnosis [5]. A prediction framework constituted by deep autoencoders (AE) is proposed in Reference [6]. Shen et al [7] proposed a contractive autoencoder-based rotating machinery fault diagnosis method. Multisensor condition monitoring data are fused to get more useful information for accurate RUL prediction. Attention-guided ordered neurons are applied in this framework to achieve the accurate gear remaining useful life prediction.
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