Abstract
Convolution neural network (CNN) has been widely used in the field of remaining useful life (RUL) prediction. However, the CNN-based RUL prediction methods have some limitations. The receptive field of CNN is limited and easy to happen gradient vanishing problem when the network is too deep. The contribution differences of different channels and different time steps to RUL prediction are not considered, and only use deep learning features or handcrafted statistical features for prediction. These limitations can lead to inaccurate prediction results. To solve these problems, this paper proposes an RUL prediction method based on multi-layer self-attention (MLSA) and temporal convolution network (TCN). The TCN is used to extract deep learning features. Dilated convolution and residual connection are adopted in TCN structure. Dilated convolution is an efficient way to widen receptive field, and the residual structure can avoid the gradient vanishing problem. Besides, we propose a feature fusion method to fuse deep learning features and statistical features. And the MLSA is designed to adaptively assign feature weights. Finally, the turbofan engine dataset is used to verify the proposed method. Experimental results indicate the effectiveness of the proposed method.
Highlights
Condition-based maintenance (CBM) is a maintenance strategy that monitors equipment health conditions in real-time and makes optimal maintenance decisions based on monitoring information [12]
We proposed an remaining useful life (RUL) prediction method based on multi-layer self-attention (MLSA) and temporal convolution network (TCN)
Complexity comparison of different methods: This paper proposes a new RUL prediction method based on MLSA and TCN
Summary
Condition-based maintenance (CBM) is a maintenance strategy that monitors equipment health conditions in real-time and makes optimal maintenance decisions based on monitoring information [12]. Multiple features were fused into one fusion feature, and the particle swarm optimization support vector machine was used to predict RUL Another widely used AI-based RUL prediction method is the deep learning algorithm. Ren et al [18] proposed a new feature extraction method, named the Spectrum-Principal-Energy-Vector, and input this feature into an eight-layer CNN to predict the RUL of the bearing. Cheng et al [5] used the Hilbert–Huang transform to construct a new health indicator, named the degradation energy indicator This indicator was used as the label to train a seven-layer CNN model and predicted the bearing RUL through SVM. In the above RUL prediction methods based on deep learning, different channel signals or features extracted from the signals are used as input to the model.
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