  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.
