Advanced maintenance techniques (AMTs) are practices that can help practitioners to make better-informed maintenance decisions, such as ensuring just-in-time maintenance, corporate business planning or lifetime extension of physical assets. These techniques take the current, but preferably also the future, state of assets into account. Although there is much literature on the development of specific techniques, reports on their adoption and use show that only few companies have successfully applied these methods. Guidance is needed on their selection and application. In this chapter, a typology of companies–based on an ongoing multiple-case study–that apply these maintenance techniques is proposed. This typology identifies typical difficulties practitioners experience in applying AMTs. Finally, a four-step procedure is offered with the aim of helping practitioners to overcome the discussed difficulties in the application of AMTs.
TopIntroduction
In challenging economic times, the costs of maintenance–which form around 25% of the total operating costs of a typical manufacturing company (Cross, 1988; Komonen, 2002)–can make or break a business. Current progress in advanced maintenance techniques (AMTs) enable companies to improve maintenance decisions, for example overhaul or lifetime extension of assets, and thereby decrease maintenance costs. Research literature describes diffused enhancement on developing prognostic techniques, a type of AMTs, that can be used to make more informed maintenance decisions. AMTs are practices that help decision makers to support maintenance decision making by taking the current, but preferably also the future, state of assets into account. Practitioners rarely know for certain when their equipment will fail. Therefore, knowledge about the current and future state can help in determining whether the revenues that are generated by the asset will compensate the risk of its failure. In other words, operators need to define if the operation can be continued or if the remaining lifetime can be thrown away by replacing parts (Haddad et al., 2014).
AMTs can help practitioners to detect imminent failures, make a health assessment of the system and predict the remaining useful life (Lee et al., 2014), or ascertain the probability a machine operates without failures or faults up to a certain time given the current machine condition and operational profile. The latter might be important in a nuclear power plant, for example (Jardine et al., 2006). More exact information about the failure behavior of assets and the time it will (probably) fail enables decision makers to make more effective (doing the right things) and efficient (doing things right) maintenance decisions.
By supporting maintenance decisions, these techniques help to:
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Reduce Life Cycle Costs: Fewer unnecessary repairs and replacements (not throwing remaining useful life away); and
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Reduce Business and Safety Risks: Fewer (safety) incidents and less unplanned downtime (Bo et al., 2010).
Conservative maintenance intervals (conducting more maintenance than strictly required) can create inefficient–costly–maintenance processes as these are set by asset owners to prevent their critical equipment from failing unexpectedly (Tinga, 2010). However, these high costs and safety issues caused by unexpected failures do not legitimize infinite preventive expenditures when other options, such as more advanced practices to predict imminent failures, are available.