In a complex system such as railway and aviation equipment’s, it may be necessary to employ a combination of Failure Finding Inspection (FFI) and a scheduled discard task, as suggested by Reliability-Centered Maintenance (RCM). This strategy ensures an adequate level of availability of hidden functions, as well as the reduction of the risk of multiple failures to an acceptable level. However, in some situations, operators prefer to extend the discard life of components beyond their recommended life limit, due to the operational restrictions. This necessitates the definition of an optimal Failure Finding Inspection interval for the extended life period. This paper aims to provide a mathematical model for defining optimal FFI interval, during the extended period of the replacement life. A cost function (CF) is developed to identify the cost per unit of time associated with different FFI intervals, for the proposed extended period of life, i.e. postponement period. The proposed method concerns as-bad-as-old (ABAO) inspection and repairs (due to failures found by inspection). It considers inspection and repair times, and takes into account the costs associated with inspection and repair, the opportunity cost of lost production due to maintenance downtime created by inspection and repair actions, and also the cost of accidents due to the occurrence of multiple failure

It is reported that switches and crossings (S&C) are one of the subsystems that cause the most delays on Swedish Railways while accounting for at least 13% of maintenance costs [6]. It is the main reason why we chose to base our study on this subsystem.Intelligent data processing allows understanding the real reliability characteristics of the assets to be maintained. The first objective of this research is to determine the S&C reliability characteristics based on field data collection. Because field failure data are typically strongly censored, an especial statistics software package was developed to process field failure data, as commercial packages have not been found satisfactory in that respect. The resulting software, named RDAT® (Reliability Data Analysis Tool) has been relied upon for this study: it is especially adapted to statistical failure data analysis.In the next step the availability of studied switches and crossings is estimated based on the reliability characteristics founded in the first step.

Spare parts inventory management differs from both work-in-process inventory management and finished product inventory management mainly due to its unique aspects in function with maintenance. Furthermore, its inventory management shows more complexities and the performance measurement differs from other productions’ either. Studies both in theoretical research and in practice have shown that only some concrete figures had been considered in traditional KPI’s for spare parts inventory management, including the total stock value, cost of keeping stock, critical spares stock-outs, operational downtime due to stock-outs, rate of circulation, etc. However, such KPI’s may only reflect limited results from spares inventory management. In another word, they can not help to find out the root causes of which aspects are the management’s bottlenecks, or from which aspects it can be improved step-by-step. This paper aims to propose a new way to measure the performance of spares inventory management from the perspective of a House of Maintenance Management (HOMM). First, the HOMM-Spares with PDSA (Plan, Do, Study, Act) thinking will be promoted with the consideration of spares management. Second, management review for spares inventory using the HOMM-Spares will be discussed in details and the performance measurement will be clarified. Obviously, with the new promoted measurement system, we can not only review its performance from a more systematic standing point, but also, the continuously improvement plan with a more scientific analysis will be achieved simultaneously. How to support decision making with this spares performance measurement system is demonstrated as well with a case study.

This paper aims to develop a graphical method to facilitate the identification of risk and cost of postponement of restoration for repairable components which are periodically tested and whose failures are hidden, i.e, are detectable by inspection or upon demand. The paper focuses on the items which are under aging, however, the methodology is flexible to implement for any aging pattern.

The paper considers Failure Finding Inspection (FFI) with restoration actions (FFI+Res), for the “safety effect” categories of hidden failures. As-bad-as-old (ABAO) inspection effectiveness and as-good-as-new (AGAN) restoration effectiveness are considered. In case of repair due to findings by inspection, as-bad-as-old repair effectiveness is considered.

The graphical methodology proposed in this paper considers inspection and repair times, as well as the costs associated with accident, inspection, repair, and restoration, and takes into account the opportunity losses due to the maintenance downtime.

The analytical approach is based on the Mean Fractional Dead Time. In the case of an operational limit, when it is not possible to remove the unit for restoration, or one needs to use the unit longer than the expected operating time, the paper introduces an approach to analyzing the possibility of and conditions for providing an extension to the restoration interval that satisfies the risk constraints and the business requirements at the same time.