Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control

Michael McCann, Yuhua Li, Liam Maguire, Adrian Johnston
Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:277-288, 2010.

Abstract

A complex modern manufacturing process is normally under consistent surveillance via the monitoring of signals/variables collected from sensors. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. It is often the case that useful information is buried in the latter two. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be used to identify the most predictive signals. Once these signals have been identified causal relevance may then be investigated to try and identify the causal features. The Process Engineers may then use these signals to ensure a small scrap rate further downstream in the process, increase the throughput and reduce the per unit production costs. Working in partnership with industry we aim to address this complex problem as part of their process control engineering in the context of wafer fabrication production and enhance current business improvement techniques with the application of causal feature selection as an intelligent systems technique.

Cite this Paper

BibTeX
@InProceedings{pmlr-v6-mccann10a, title = {Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control}, author = {McCann, Michael and Li, Yuhua and Maguire, Liam and Johnston, Adrian}, booktitle = {Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008}, pages = {277--288}, year = {2010}, editor = {Guyon, Isabelle and Janzing, Dominik and Schölkopf, Bernhard}, volume = {6}, series = {Proceedings of Machine Learning Research}, address = {Whistler, Canada}, month = {12 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v6/mccann10a/mccann10a.pdf}, url = {https://proceedings.mlr.press/v6/mccann10a.html}, abstract = {A complex modern manufacturing process is normally under consistent surveillance via the monitoring of signals/variables collected from sensors. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. It is often the case that useful information is buried in the latter two. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be used to identify the most predictive signals. Once these signals have been identified causal relevance may then be investigated to try and identify the causal features. The Process Engineers may then use these signals to ensure a small scrap rate further downstream in the process, increase the throughput and reduce the per unit production costs. Working in partnership with industry we aim to address this complex problem as part of their process control engineering in the context of wafer fabrication production and enhance current business improvement techniques with the application of causal feature selection as an intelligent systems technique.} }
Endnote
%0 Conference Paper %T Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control %A Michael McCann %A Yuhua Li %A Liam Maguire %A Adrian Johnston %B Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 %C Proceedings of Machine Learning Research %D 2010 %E Isabelle Guyon %E Dominik Janzing %E Bernhard Schölkopf %F pmlr-v6-mccann10a %I PMLR %P 277--288 %U https://proceedings.mlr.press/v6/mccann10a.html %V 6 %X A complex modern manufacturing process is normally under consistent surveillance via the monitoring of signals/variables collected from sensors. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. It is often the case that useful information is buried in the latter two. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be used to identify the most predictive signals. Once these signals have been identified causal relevance may then be investigated to try and identify the causal features. The Process Engineers may then use these signals to ensure a small scrap rate further downstream in the process, increase the throughput and reduce the per unit production costs. Working in partnership with industry we aim to address this complex problem as part of their process control engineering in the context of wafer fabrication production and enhance current business improvement techniques with the application of causal feature selection as an intelligent systems technique.
RIS
TY - CPAPER TI - Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control AU - Michael McCann AU - Yuhua Li AU - Liam Maguire AU - Adrian Johnston BT - Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 DA - 2010/02/18 ED - Isabelle Guyon ED - Dominik Janzing ED - Bernhard Schölkopf ID - pmlr-v6-mccann10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 6 SP - 277 EP - 288 L1 - http://proceedings.mlr.press/v6/mccann10a/mccann10a.pdf UR - https://proceedings.mlr.press/v6/mccann10a.html AB - A complex modern manufacturing process is normally under consistent surveillance via the monitoring of signals/variables collected from sensors. However, not all of these signals are equally valuable in a specific monitoring system. The measured signals contain a combination of useful information, irrelevant information as well as noise. It is often the case that useful information is buried in the latter two. Engineers typically have a much larger number of signals than are actually required. If we consider each type of signal as a feature, then feature selection may be used to identify the most predictive signals. Once these signals have been identified causal relevance may then be investigated to try and identify the causal features. The Process Engineers may then use these signals to ensure a small scrap rate further downstream in the process, increase the throughput and reduce the per unit production costs. Working in partnership with industry we aim to address this complex problem as part of their process control engineering in the context of wafer fabrication production and enhance current business improvement techniques with the application of causal feature selection as an intelligent systems technique. ER -
APA
McCann, M., Li, Y., Maguire, L. & Johnston, A.. (2010). Causality Challenge: Benchmarking relevant signal components for effective monitoring and process control. Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, in Proceedings of Machine Learning Research 6:277-288 Available from https://proceedings.mlr.press/v6/mccann10a.html.

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