AI Anomaly Detection for Smart Manufacturing
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-60840-7_8
Reference14 articles.
1. Abdelrahman, O., Keikhosrokiani, P.: Assembly line anomaly detection and root cause analysis using machine learning. IEEE Access 8, 189661–189672 (2020)
2. Cheng, C., Chen, P.: Phase I analysis of nonlinear profiles using anomaly detection techniques. Appl. Sci. 13, 2147 (2023)
3. Bajic, B., Rikalovic, A., Suzic, N., Piuri, V.: Industry 4.0 implementation challenges and opportunities: a managerial perspective. IEEE Syst. J. 15(1), 546–559 (2021)
4. Rikalovic, A., Suzic, N., Bajic, B., Piuri, V.: Industry 4.0 implementation challenges and opportunities: a technological perspective. IEEE Syst. J. 16(2), 2797–2810 (2022)
5. Dai, H.N., Wang, H., Xu, G., Wan, J., Imran, M.: Big data analytics for manufacturing Internet of Things: opportunities, challenges and enabling technologies. Enterp. Inf. Syst. 14, 1–25 (2019)
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