Performance metrics for artificial intelligence (AI) algorithms adopted in prognostics and health management (PHM) of mechanical systems

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dc.contributor.authorOchella, Sunday
dc.contributor.authorShafiee, Mahmood
dc.date.accessioned2021-04-15T10:42:01Z
dc.date.available2021-04-15T10:42:01Z
dc.date.issued2021-03-04
dc.description.abstractResearch into the use of artificial intelligence (AI) algorithms within the field of prognostics and health management (PHM), in particular for predicting the remaining useful life (RUL) of mechanical systems that are subject to condition monitoring, has gained widespread attention in recent years. It is important to establish confidence levels for RUL predictions, so as to aid operators as well as regulators in making informed decisions regarding maintenance and asset life-cycle planning. Over the past decade, many researchers have devised indicators or metrics for determining the performance of AI algorithms in RUL prediction. While most of the popularly used metrics like Mean Absolute Error (MAE), Root Mean Square Error (RMSE), etc. were adapted from other applications, some bespoke metrics are designed and intended specifically for use in PHM research. This study provides a synopsis of key performance indicators (KPIs) that are applied to AI-driven PHM technologies of mechanical systems. It presents details of the application scenarios, suitability of using a particular metric in different scenarios, the pros and cons of each metric, the trade-offs that may need to be made in choosing one metric over another, and some other factors that engineers should take into account when applying the metricsen_UK
dc.identifier.citationOchella S, Shafiee M. (2021) Performance metrics for artificial intelligence (AI) algorithms adopted in prognostics and health management (PHM) of mechanical systems. Journal of Physics: Conference Series, Volume 1828, Article number 012005. 1st International Symposium on Automation, Information and Computing (ISAIC 2020), 2-4 December 2020, Beijing, Chinaen_UK
dc.identifier.issn1742-6588
dc.identifier.urihttps://doi.org/10.1088/1742-6596/1828/1/012005
dc.identifier.urihttp://dspace.lib.cranfield.ac.uk/handle/1826/16576
dc.language.isoenen_UK
dc.publisherIOP Publishing: Conference Seriesen_UK
dc.rightsAttribution 3.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/*
dc.subjectprognostics and health management (PHM)en_UK
dc.subjectMean Absolute Error (MAE)en_UK
dc.titlePerformance metrics for artificial intelligence (AI) algorithms adopted in prognostics and health management (PHM) of mechanical systemsen_UK
dc.typeConference paperen_UK

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