Skip to main content

Performance Metrics for Artificial Intelligence (AI) Algorithms Adopted in Prognostics and Health Management (PHM) of Mechanical Systems

Ochella, Sunday, Shafiee, Mahmood (2021) Performance Metrics for Artificial Intelligence (AI) Algorithms Adopted in Prognostics and Health Management (PHM) of Mechanical Systems. In: Journal of Physics: Conference Series. 2020 International Symposium on Automation, Information and Computing (ISAIC 2020). 1828. pp. 1-10. IOP Publishing Ltd (doi:10.1088/1742-6596/1828/1/012005) (KAR id:87037)

PDF Publisher pdf
Language: English


Download (830kB) Preview
[thumbnail of Published.pdf]
Preview
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL
https://doi.org/doi:10.1088/1742-6596/1828/1/01200...

Abstract

Research 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 metrics.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1088/1742-6596/1828/1/012005
Uncontrolled keywords: Artificial Intelligence; Prognostics and Health Management (PHM); Mechanical Systems
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology > TJ Mechanical engineering and machinery
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts
Depositing User: Mahmood Shafiee
Date Deposited: 09 Mar 2021 23:53 UTC
Last Modified: 10 Mar 2021 16:37 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/87037 (The current URI for this page, for reference purposes)
Shafiee, Mahmood: https://orcid.org/0000-0002-6122-5719
  • Depositors only (login required):

Downloads

Downloads per month over past year