Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/153757
Title: Online semisupervised learning approach for quality monitoring of complex manufacturing process
Authors: Weng, Weiwei
Pratama, Mahardhika
Ashfahani, Andri
Yapp, Edward Kien Yee
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Weng, W., Pratama, M., Ashfahani, A. & Yapp, E. K. Y. (2021). Online semisupervised learning approach for quality monitoring of complex manufacturing process. Complexity, 2021, 3005276-. https://dx.doi.org/10.1155/2021/3005276
Project: A19C1A0018 
Journal: Complexity 
Abstract: Data-driven quality monitoring is highly demanded in practice since it enables relieving manual quality inspection of the product quality. Conventional data-driven quality monitoring is constrained by its offline characteristic thus being unable to handle streaming nature of sensory data and nonstationary environments of machine operations. Recently, there have been pioneering works of online quality monitoring taking advantage of online learning concepts in the literature, but it is still far from realization of minimum operator intervention in the quality monitoring because it calls for full supervision in labelling data samples. This paper proposes Parsimonious Network++ (ParsNet++) as an online semisupervised learning approach being able to handle extreme label scarcity in the quality monitoring task. That is, it is capable of coping with varieties of semisupervised learning conditions including random access of ground truth and infinitely delayed access of ground truth. ParsNet++ features the one-pass learning approach to deal with streaming data while characterizing elastic structure to overcome rapidly changing data distributions. That is, it is capable of initiating its learning structure from scratch with the absence of a predefined network structure where its hidden nodes can be added and discarded on the fly in respect to drifting data distributions. Furthermore, it is equipped by a feature extraction layer in terms of 1D convolutional layer extracting natural features of multivariate time-series data samples of sensors and coping well with the many-to-one label relationship, a common problem of practical quality monitoring. Rigorous numerical evaluation has been carried out using the injection molding machine and the industrial transfer molding machine from our own projects. ParsNet++ delivers highly competitive performance even compared to fully supervised competitors.
URI: https://hdl.handle.net/10356/153757
ISSN: 1076-2787
DOI: 10.1155/2021/3005276
Rights: © 2021 Weng Weiwei et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles
SIMTech Journal Articles

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