The nature of failure data sets poses particular challenges to modellers. Failures, particularly of critical equipment, are rare. Lots of equipment are replaced in whole or in part before end of life. As a result failure data-sets are unbalanced and sparse. Failures are seldom labelled accurately, and there is often no ground truth for validation.
Condition monitoring data, when available, is often collected at different time intervals. Poor quality data results in greater model complexity that at best muddies inference, and at worst misleads inference and produces persistent prediction bias. These contextual issues, if not dealt with rigorously in model selection and validation practice, leads to poor model performance and a loss of trust by decision makers. To manage these risks, this theme is explicitly cross-disciplinary combining the Bayesian statistics, engineering, nonlinear system identification, machine learning and deep learning.
Projects
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Publications
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Page:Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology —
Journal Article
Dr Debora Correa
Authors: Eugene Tan, Shannon Alga, Débora Corrêa, Michael Small, Thomas Stemler and David Walker1
2023-03-01
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Page:A Novel Approach to Time Series Complexity via Reservoir Computing —
Conference Publishing
Braden Thorne
Authors: Braden Thorne, Thomas Jüngling , Michael Small , Debora Correa , and Ayham Zaitouny
2022-12-07
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Page:Informative Bayesian Survival Analysis to Handle Heavy Censoring in Lifetime Data —
Conference Publishing
Ryan Leadbetter
Authors: Ryan Leadbetter; Aloke Phatak; Adriano Polpo; Melinda Hodkiewicz
2021-12-15
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Page:Reservoir time series analysis: Using the response of complex dynamical systems as a universal indicator of change —
Journal Article
Braden Thorne
Authors: Thorne, Braden Jüngling, Thomas Small, Michael Corrêa, Débora Zaitouny, Ayham
2022-02-10
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Page:Managing streamed sensor data for mobile equipment prognostics. —
Journal Article
Dr Debora Correa
Authors: Griffiths, T., Corrêa, D., Hodkiewicz, M., & Polpo, A. (2022). Managing streamed sensor data for mobile equipment prognostics. Data-Centric Engineering, 3, E11. doi:10.1017/dce.2022.4
2022-04-07
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Page:Data-Driven Approach for Labelling Process Plant Event Data —
Journal Article
Dr Debora Correa
Authors: Débora Corrêa, Adriano Polpo, Michael Small, Shreyas Srikanth, Kylie Hollins, Melinda Hodkiewicz
2022-01-24
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Page:Parameter extraction with reservoir computing: Nonlinear time series analysis and application to industrial maintenance —
Journal Article
Braden Thorne
Authors: Thorne, B., Jüngling, T., Small, M., & Hodkiewicz, M. (2021).
2021-03-01
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