A maturity framework for data driven maintenance
This work provides a structured approach for assessing data-driven maintenance maturity, which is incremental as it builds on existing concepts in maintenance and decision-making.
The paper tackles the challenge of transitioning from human-driven to autonomous maintenance decisions by proposing a maturity framework that evaluates data/decision maturity, real-world translation, model computability, and causality. It applies this framework to a fault detection problem, finding that experience-based and model-based approaches yield identical decisions but differ in causality assignment.
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.