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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2503.01858 |
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| _version_ | 1866916642231943168 |
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| author | Chang, Shing I Ghafariasl, Parviz |
| author_facet | Chang, Shing I Ghafariasl, Parviz |
| contents | It has been 100 years since statistical process control (SPC) or statistical process monitoring (SPM) was first introduced for production processes and later applied to service, healthcare, and other industries. The techniques applied to SPM applications are mostly statistically oriented. Recent advances in Artificial Intelligence (AI) have reinvigorated the imagination of adopting AI for SPM applications. This manuscript begins with a concise review of the historical development of the statistically based SPM methods. Next, this manuscript explores AI and Machine Learning (ML) algorithms and methods applied in various SPM applications, addressing quality characteristics of univariate, multivariate, profile, and image. These AI methods can be classified into the following categories: classification, pattern recognition, time series applications, and generative AI. Specifically, different kinds of neural networks, such as artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN), are among the most implemented AI methods impacting SPM. Finally, this manuscript outlines a couple of future directions that harness the potential of the Large Multimodal Model (LMM) for advancing SPM research and applications in complex systems. The ultimate objective is to transform statistical process monitoring (SPM) into smart process control (SMPC), where corrective actions are autonomously implemented to either prevent quality issues or restore process performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01858 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Review of Artificial Intelligence Impacting Statistical Process Monitoring and Future Directions Chang, Shing I Ghafariasl, Parviz Systems and Control Artificial Intelligence It has been 100 years since statistical process control (SPC) or statistical process monitoring (SPM) was first introduced for production processes and later applied to service, healthcare, and other industries. The techniques applied to SPM applications are mostly statistically oriented. Recent advances in Artificial Intelligence (AI) have reinvigorated the imagination of adopting AI for SPM applications. This manuscript begins with a concise review of the historical development of the statistically based SPM methods. Next, this manuscript explores AI and Machine Learning (ML) algorithms and methods applied in various SPM applications, addressing quality characteristics of univariate, multivariate, profile, and image. These AI methods can be classified into the following categories: classification, pattern recognition, time series applications, and generative AI. Specifically, different kinds of neural networks, such as artificial neural networks (ANN), convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial networks (GAN), are among the most implemented AI methods impacting SPM. Finally, this manuscript outlines a couple of future directions that harness the potential of the Large Multimodal Model (LMM) for advancing SPM research and applications in complex systems. The ultimate objective is to transform statistical process monitoring (SPM) into smart process control (SMPC), where corrective actions are autonomously implemented to either prevent quality issues or restore process performance. |
| title | A Review of Artificial Intelligence Impacting Statistical Process Monitoring and Future Directions |
| topic | Systems and Control Artificial Intelligence |
| url | https://arxiv.org/abs/2503.01858 |