Difference between revisions of "Smart manufacturing"
Chathurangi (Talk | contribs) |
Chathurangi (Talk | contribs) (→PROJECTS) |
||
| Line 5: | Line 5: | ||
= PROJECTS = | = PROJECTS = | ||
| − | == '''Project-1:''' | + | == '''Project-1:'''Rare Event Prediction == |
| − | + | There are different types of events that can be observed within autonomous systems. For example, our consideration on this topic led us to explore “rare events” as they are particularly important for smart manufacturing applications. Rare events are occurrences that take place with a significantly lower frequency than more common regular events. In manufacturing, predicting such events is particularly important, as they lead to unplanned downtime, shortening equipment lifespan, and high energy consumption. The occurrence of events is usually considered rare if observed in 5-10% of all instances, while the occurrence of less than 1% is considered extremely rare. The rarity of events is inversely correlated with the maturity of a manufacturing industry. Typically, the rarity of events causes the multivariate data generated within a manufacturing process to be highly imbalanced, which leads to bias in predictive models. To this end, we conducted a thorough survey to investigate prior art along four dimensions: (i) data acquisition methods, (ii) data preprocessing techniques, (iii) algorithmic approaches, and (iv) evaluation/assessment approaches. Figure X below summarizes the methods we looked at within each dimension | |
| − | + | ||
| − | + | Regarding modeling rare event prediction, we first evaluated the role of data enrichment approaches combined with supervised machine-learning techniques. Predominantly, we explored using three data enrichment approaches; data augmentation, sampling and imputation. Our study is based on open real-world datasets we obtained from public data sources. | |
| − | + | ||
| − | + | ||
Revision as of 19:52, 14 September 2023
The utilization of AI in manufacturing processes has ushered in a new era of data-driven optimization and problem-solving. AI is a pivotal cornerstone, especially in Industry 4.0 and 5.0 applications, yielding advanced technologies to guide a new era of heightened efficiency, productivity, and refined decision-making. In this capacity, AI emerges as an indispensable force driving transformative advancements within the manufacturing domain, where data-driven insights and smart automation catalyze innovation and shape the future of industrial processes. The assessment of SoA approaches in the manufacturing realm helped us identify three concrete use cases for further investigation: (1) Rare event prediction, (2) Anomaly detection using process knowledge workflow, and (3) explaining the root cause of an event through causal knowledge.
PROJECTS
Project-1:Rare Event Prediction
There are different types of events that can be observed within autonomous systems. For example, our consideration on this topic led us to explore “rare events” as they are particularly important for smart manufacturing applications. Rare events are occurrences that take place with a significantly lower frequency than more common regular events. In manufacturing, predicting such events is particularly important, as they lead to unplanned downtime, shortening equipment lifespan, and high energy consumption. The occurrence of events is usually considered rare if observed in 5-10% of all instances, while the occurrence of less than 1% is considered extremely rare. The rarity of events is inversely correlated with the maturity of a manufacturing industry. Typically, the rarity of events causes the multivariate data generated within a manufacturing process to be highly imbalanced, which leads to bias in predictive models. To this end, we conducted a thorough survey to investigate prior art along four dimensions: (i) data acquisition methods, (ii) data preprocessing techniques, (iii) algorithmic approaches, and (iv) evaluation/assessment approaches. Figure X below summarizes the methods we looked at within each dimension
Regarding modeling rare event prediction, we first evaluated the role of data enrichment approaches combined with supervised machine-learning techniques. Predominantly, we explored using three data enrichment approaches; data augmentation, sampling and imputation. Our study is based on open real-world datasets we obtained from public data sources.
