A LS-SVM based Approach for Turbine Engines Prognostics Using Sensor Data

Li, Yongxiang; Shan, Xiaoming; Zhao, Wei; Gong, Wei

doi:10.1109/icit.2019.8755209

Cited by 2 publications

(1 citation statement)

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“…AI-based methods can be divided into shallow machine learning algorithms and deep learning algorithms [4]. The shallow models used for RUL prediction include support vector machine (SVM) [15,31], random forest (RF) [33], decision tree (DT) [28], etc. Since the trend of the raw data is unclear and contains noise [14], it is necessary to extract features from the raw data before inputting the model.…”

Section: Introductionmentioning

confidence: 99%

Machine remaining life prediction based on multi-layer self-attention and temporal convolution network

Shang

Zhang

et al. 2021

Complex Intell. Syst.

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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.

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