Abstract
Much industrial equipment integrates multiple types of sensors for data collection and real-time connection with the Industrial Internet of Things (IIoT) at now. With the popularity and application of miniaturized and low-cost sensors, to manage the health of equipment in the IIoT, such as evaluating the current health indicator (HI) of the equipment, and predicting its remaining useful life (RUL), the machine learning-based method reflects a broader application prospect due to its good at data mining and analysis. A RUL prediction method of equipment in this paper is proposed. First, a specific data processing method is proposed according to the characteristics of the data, the outputs of the hidden layer of stacked denoising autoencoder (SDAE) as the features of the data set which are extracted to complete the HI construction. Then, the trajectory pointwise difference and similarity method are proposed to generate predicted equipment RUL. This paper uses the C-MAPSS engine data set for experimental verification. The experimental results show that the method proposed in this paper can effectively use the data of multi-dimensional sensor data to get the degradation progress of the equipment, thereby assessing the health of the equipment and more accurately estimating the RUL.
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Acknowledgment
This work was supported by the National Key Research and Development Projectof China (No. 2018YFB1702600, 2018YFB1702602), National Natural Science Foundation of China (No. 61402167, 61772193, 61872139), Hunan Provincial Natural Science Foundation of China (No. 2017JJ4036, 2018JJ2139), and Research Foundation of Hunan Provincial Education Department of China (No. 17K033, 19A174).
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Tan, Z., Wen, Y. (2021). Research on Machine Learning Method for Equipment Health Management in Industrial Internet of Things. In: Sun, Y., Liu, D., Liao, H., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2020. Communications in Computer and Information Science, vol 1330. Springer, Singapore. https://doi.org/10.1007/978-981-16-2540-4_19
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DOI: https://doi.org/10.1007/978-981-16-2540-4_19
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