Change Point-based Spatio-temporal Process Modeling of Image Degradation for Manufacturing Process
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Abstract
As an advent of smart factory technology, data-driven condition-based maintenance (CBM) is developed to automatically control the production process in engineering field. CBM usually focuses on diagnosing the production status based on real-time data from the sensors. In general manufacturing field, the performance of production equipment gradually decreases due to the wear or deterioration of equipment. To determine if the process is in-control, degradation modeling of observed data from the equipment and its statistical inference is conducted. In this paper, we propose image-based degradation modeling and change-point detection using spatio-temporal process (STP). To describe the deteriorating patterns of image observation, degradation based on spatial and temporal relationship is conducted. At the same time, change-point is estimated to distinguish the degradation under normal and abnormal production status. Through the application to the image stream in real industry, the proposed monitoring scheme `vely conduct the bi-phase representation providing the change-point of manufacturing processes.
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Image processing, degradation modeling, spatio-temporal process, change-point detection
Lopes, M., Fauvel, M., Girard, S., & Sheeren, D., (2016). High dimensional Kullback-Leibler divergence for grassland management practices classsification from high resolution satellite image time series. Proceedings of IEEE International Geoscience and Remote Sensing Symposium.
Shi, X., Chen, Z., Wang, H., Yeung, D. Y., Wong, W. K., & Woo, W. C., (2015). Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting. Computer Vision and Pattern Recognition, vol. 28, pp. 802-810.
Fang, X., Paynabar, K., & Gebraeel, N., (2019). Image-based Prognostics using Penalized Tensor Regression. Technometrics, vol. 61, no. 3, pp. 369-384.
Sigrist, F., Künsch, H. R., & Stahel, W. A., (2015). Stochastic partial differential equation based modelling of large space-time data sets. Journal of the Royal Statistical Society. Series B (Statistical Methodology), vol. 77, no. 1, pp. 3-33.
Majumdar, A., Gelfand, A. E., & Banerjee, S., (2005). Spatio-temporal change-point modeling. Journal of Statistical Planning and Inference, vol. 130, pp. 149-166.
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