A maturity framework for data driven maintenance

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.