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 LSTM-based framework with metaheuristic optimizer for manufacturing process monitoring
Tác giả hoặc Nhóm tác giả: Chao-Lung Yang, Atinkut Atinafu Yilma, Hendri Sutrisno, Bereket Haile Woldegiorgis, Thi Phuong Quyen Nguyen
Nơi đăng: Alexandria Engineering Journal; Số: 83;Từ->đến trang: 43-52;Năm: 2023
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Quốc tế
TÓM TẮT
ABSTRACT
Quick process shift detection and lower out-of-control run length are essential for monitoring the production process, especially in modern smart manufacturing. Specifically, the out-of-control run length is one of the most critical performance measures to evaluate the manufacturing process monitoring (MPM) model. The sooner the out-of-control is detected, the better the model is. However, developing a monitoring model which can provide quick shift detection for various data dimensions and volumes is challenging. In this research, single (1_LSTM) and stacked (S_LSTM) long-short-term memory (LSTM) based models with metaheuristic optimizer were proposed to detect process shifts quickly in the manufacturing domain. Based on the literature, three metaheuristic methods: Clustering-based organism search (CSOS), Particle Swarm Optimization (PSO), and Simulated Annealing (SA) that are suitable for high-dimensional optimization were utilized in the proposed method to optimize weights in the LSTM-based network. The proposed models were evaluated based on average out-of-control run length (���1) against benchmark methods on various synthesized multivariate normal and real-world datasets. Also, the performances of CSOS, PSO, and SA were compared. The results show that CSOS_S_LSTM outperforms other methods with lower ���1. The result also confirmed the effectiveness and applicability of the proposed models for real-world problems. The experimental results showed that the response time of detection can be improved by 33.19% and 38.77% on average using the proposed 1_LSTM and CSOS LSTM-based metaheuristics models, respectively.
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