@inbook {31717,
	title = {Application of MachineLearning in Statistical Process Control Charts: A Survey and Perspective},
	booktitle = {Control Charts and Machine Learning for Anomaly Detection in Manufacturing},
	year = {2022},
	pages = {7-42},
	publisher = {Springer},
	organization = {Springer},
	abstract = {<section aria-labelledby="Abs1" data-gtm-vis-first-on-screen-50443292_562="183" data-gtm-vis-first-on-screen-50443292_563="183" data-gtm-vis-has-fired-50443292_562="1" data-gtm-vis-has-fired-50443292_563="1" data-gtm-vis-recent-on-screen-50443292_562="183" data-gtm-vis-recent-on-screen-50443292_563="183" data-gtm-vis-total-visible-time-50443292_562="10000" data-gtm-vis-total-visible-time-50443292_563="10000" data-title="Abstract" lang="en">
	<p>
		Over the past decades, control charts, one of the essential tools in Statistical Process Control (SPC), have been widely implemented in manufacturing industries as an effective approach for Anomaly Detection (AD). Thanks to the development of technologies like the Internet of Things (IoT) and Artificial Intelligence (AI), Smart Manufacturing (SM) has become an important concept for expressing the end goal of digitization in manufacturing. However, SM requires a more automatic procedure with capabilities to deal with huge data from the continuous and simultaneous process. Hence, traditional control charts of SPC now find difficulties in reality activities including designing, pattern recognition, and interpreting stages. Machine Learning (ML) algorithms have emerged as powerful analytic tools and great assistance that can be integrating to control charts of SPC to solve these issues. Therefore, the purpose of this chapter is first to presents a survey on the applications of ML techniques in the stages of designing, pattern recognition, and interpreting of control charts respectively in SPC especially in the context of SM for AD. Second, difficulties and challenges in these areas are discussed. Third, perspectives of ML techniques-based control charts for AD in SM are proposed. Finally, a case study of an ML-based control chart for bearing failure AD is also provided in this chapter.
	</p>
</section>
},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-83819-5_2},
	author = {Phuong Hanh Tran and Adel Ahmadi Nadi and Thi Hien Nguyen and Kim Duc Tran and Kim Phuc Tran}
}
