Abstract
In this chapter, we provide an introduction to Anomaly Detection and potential applications in manufacturing using Control Charts and Machine Learning techniques. We elaborate on the peculiarities of process monitoring and Anomaly Detection with Control Charts and Machine Learning in the manufacturing process and especially in the smart manufacturing contexts. We present the main research directions in this area and summarize the structure and contribution of the book.
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References
Lindemann B, Fesenmayr F, Jazdi N, Weyrich M (2019) Anomaly detection in discrete manufacturing using self-learning approaches. Procedia CIRP 79:313–318
Qiu P (2020) Big data? Statistical process control can help! Am Stat 74(4):329–344
Wang J, Ma Y, Zhang L, Gao RX, Wu D (2018) Deep learning for smart manufacturing: methods and applications. J Manuf Syst 48:144–156
Lopez F, Saez M, Shao Y, Balta EC, Moyne J, Mao ZM, Barton K, Tilbury D (2017) Categorization of anomalies in smart manufacturing systems to support the selection of detection mechanisms. IEEE Rob Autom Lett 2(4):1885–1892
Tsung F, Zhang K, Cheng L, Song Z (2018) Statistical transfer learning: a review and some extensions to statistical process control. Qual Eng 30(1):115–128
Zwetsloot IM, Mahmood T, Woodall WH (2020) Multivariate time-between-events monitoring: an overview and some overlooked underlying complexities. Qual Eng 33(1):1–13
Nguyen HD, Tran KP, Thomassey S, Hamad M (2021) Forecasting and anomaly detection approaches using LSTM and LSTM autoencoder techniques with the applications in supply chain management. Int J Inf Manag 57:102282
Lee S, Kwak M, Tsui KL, Kim SB (2019) Process monitoring using variational autoencoder for high-dimensional nonlinear processes. Eng Appl Artif Intell 83:13–27
Qiu P, Xie X (2021) Transparent sequential learning for statistical process control of serially correlated data. Technometrics, (just-accepted), 1–29
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Tran, K.P. (2022). Introduction to Control Charts and Machine Learning for Anomaly Detection in Manufacturing. In: Tran, K.P. (eds) Control Charts and Machine Learning for Anomaly Detection in Manufacturing. Springer Series in Reliability Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-83819-5_1
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DOI: https://doi.org/10.1007/978-3-030-83819-5_1
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Online ISBN: 978-3-030-83819-5
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