A maturity framework for data driven maintenance

TL;DR

This paper presents a comprehensive maturity framework for data-driven maintenance, addressing challenges and comparing experience-based and model-based approaches in fault detection and causality analysis.

Contribution

It introduces a novel four-aspect maturity framework for data-driven maintenance and demonstrates its application through a practical fault detection case study.

Findings

Both approaches yield similar decisions

Differences in causality attribution between approaches

Maturity assessment should include multiple aspects

Abstract

Maintenance decisions range from the simple detection of faults to ultimately predicting future failures and solving the problem. These traditionally human decisions are nowadays increasingly supported by data and the ultimate aim is to make them autonomous. This paper explores the challenges encountered in data driven maintenance, and proposes to consider four aspects in a maturity framework: data / decision maturity, the translation from the real world to data, the computability of decisions (using models) and the causality in the obtained relations. After a discussion of the theoretical concepts involved, the exploration continues by considering a practical fault detection and identification problem. Two approaches, i.e. experience based and model based, are compared and discussed in terms of the four aspects in the maturity framework. It is observed that both approaches yield the same decisions, but still differ in the assignment of causality. This confirms that a maturity assessment not only concerns the type of decision, but should also include the other proposed aspects.