Online performance and proactive maintenance assessment of data driven prediction models

Yingjun Shen (), Taohong Wang () and Zhe Song ()
Additional contact information
Yingjun Shen: Nanjing University
Taohong Wang: Nanjing University
Zhe Song: Nanjing University

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 8, No 19, 3959-3993

Abstract: Abstract Many Data-driven decisions in manufacturing need accurate and reliable predictions. Due to high complexity and variability of working conditions, a prediction model may deteriorate over time after deployed. Traditional performance evaluation indexes mainly assess the prediction model from a static perspective, which is difficult to meet the actual needs of model selection and proactive maintenance, resulting in unstable online prediction performance. For regression-based prediction models, this paper designs online prediction performance evaluation indexes (OPPEI) to evaluate the prediction model in terms of its accuracy, degradation speed, and stability. For proactive maintenance, this paper proposes a model maintenance evaluation method based on Principal Component Analysis (PCA). We use PCA to transform various performance indexes and extract the first principal component as a model maintenance evaluation index, which could reduce the over-sensitive or insensitive phenomenon of single indicator. The effectiveness of online prediction performance evaluation indexes and PCA-based proactive maintenance evaluation method are verified by simulation and several real-world load forecasting experiments.

Keywords: Data driven model selection; Predictive model deterioration; Online predictive performance; Proactive maintenance evaluation; Machine learning (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-024-02357-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:35:y:2024:i:8:d:10.1007_s10845-024-02357-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-024-02357-8

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().