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For many decades, it has been recognized that maintenance activities should be adapted to the specific usage of a system. For that reason, many advanced policies have been developed, such as condition-based and load-based maintenance policies. However, these policies require advanced monitoring techniques and rather detailed understanding of the failure behavior, which requires the support of an OEM or expert, prohibiting application by an operator in many cases. The present work proposes a maintenance policy that relieves the high (technical) demands set by these existing policies and provides a more accurate specification of the required (dynamic) maintenance interval than traditional usage-based maintenance.

The methodology followed starts with a review and critical assessment of existing maintenance policies, which are classified according to six different aspects. Based on the need for a technically less demanding policy that appears from this comparison, a new policy is developed. The consecutive steps required for this functional usage profiles based maintenance policy are then critically discussed: usage profile definition, monitoring, profile severity quantification and the possible extension to the fleet level. After the description of the proposed policy, it is demonstrated in three case studies on real systems.

A maintenance policy based on a simple usage registration procedure appears to be feasible, which enables a significantly more efficient maintenance process than the traditional usage-based policies. This is demonstrated by the policy proposed here.

The proposed maintenance policy based on functional usage profiles offers the operators of fleets of systems the opportunity to increase the efficiency and effectiveness of their maintenance process, without the need for a high investment in advanced monitoring systems and in experts interpreting the results.

The original contribution of this work is the explicit definition of a new maintenance policy, which combines the benefits of considering the effects of usage or environment severity with a limited investment in monitoring technology.

Asset owners and maintainers need to make timely and well-informed maintenance decisions based on the actual or predicted condition of their physical assets. However, only few companies have succeeded to implement predictive maintenance (PdM) effectively. Therefore, this paper aims to identify why only few companies were able to successfully implement PdM.

A multiple-case study including 13 cases in various industries in The Netherlands was conducted. This paper examined the choices made in practice to achieve PdM and possible dependencies between and motivations for these choices.

An implementation process for PdM appeared to comprise four elements: a trigger, data collection, maintenance technique (MT) selection and decision-making. For each of these elements, several options were available. By identifying the choices made by companies in practice and mapping them on the proposed elements, logical combinations appeared. These combinations can provide insight into the PdM implementation process and may also lead to guidance on this topic. Further, while successful companies typically combined various techniques, the mostly applied techniques were still those based on previous experiences.

This research calls for better methods or procedures to guide the selection and use of suitable types of PdM, directed by the firm's ambition level and the available data.

While it is important for firms to make suitable choices during implementation, the literature often focusses only on developing additional techniques for PdM. This paper provides new insights into the application and selection of techniques for PdM in practice and helps practitioners reduce the often applied trial-and-error process.

The purpose of this paper is to show that maintenance performance is potentially better predictable from recording routines.

An attempt is made to observe an effect of a policy. Maintenance cases seem exceptional because of the efficiently obtainable evidence about policy violations which potentially provides access to the counterfactual reality to be avoided by the decision maker. The approach followed in this work is to use maintenance policy compliance to observe the effect of a maintenance policy.

Conventional maintenance scorecards are not geared to accommodate maintenance performance predictions. The proposed alternative representation of maintenance performance indicators much better accommodates maintenance performance predictions.

This work focuses on the operationalisation and the sampling procedure of maintenance performance indicators; the selection of a predictive model is not considered.

This work demonstrates that maintenance decision makers have the possibility to observe the effects of their policy, which potentially enables them to better control their objectives. This work provides guidelines to construct performance indicators that much better accommodate maintenance performance predictions than conventional maintenance performance indicators.

The approach to make the effects of maintenance policy compliance observable is an original contribution to normative decision theory. To construct a maintenance scorecard that accommodates maintenance performance predictions is an original contribution to the field of maintenance performance measurement.