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The IUP Journal of Operations Management :
Development of a Predictive Maintenance Model Using Modified FMEA Approach
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Predictive Maintenance (PdM) is the catchword in the present times. Companies are adopting PdM to obtain competitive advantage as there are many benefits associated with it. In addition to the benefits, PdM however has two major drawbacks: first, it requires costly advanced monitoring and data processing technologies; and second, there is no statistical model on which PdM can be based, making PdM inaccessible to small and medium-scale organizations. This paper makes an attempt to develop a PdM model to minimize the use of technologies, and hence extends the application of PdM to small and medium-scale organizations. Here a new method, modified Failure Mode and Effects Analysis (FMEA), has been developed from FMEA technique, where the output of the system is used as an indicator for predicting maintenance times. The developed model is validated on a palm oil extraction unit.

 
 

Maintenance costs constitute a major part of the total operating costs of any manufacturing or production system. The aim of an optimal maintenance policy is to provide optimal machine/plant availability, reliability and safety at the lowest possible cost (Pham and Wang, 1996). With the large-scale industrialization and cut-throat competition, the emphasis has shifted to system availability, reliability and safety, for which effective maintenance is needed. Maintenance policies are broadly classified into three categories: breakdown maintenance (Bevilacqua and Braglia, 2000), where the system is maintained after a breakdown; preventive maintenance (Zhao, 2003; and Ahmad and Kamaruddin, 2012), which involves regular periodic maintenance of the system to prevent breakdowns; and predictive maintenance (Chu et al., 1998; Dieulle et al., 2001; and Moya, 2004), which involves maintenance operation only when required by the state of the system. According to Tan and Raghavan (2008), maintenance has emerged from the age-old breakdown (or reactive) maintenance to preventive maintenance to now popular Predictive Maintenance (PdM). There are several benefits associated with PdM such as lower insurance rates, increased plant reliability, improved product quality, better asset protection, reduced catastrophic and unexpected machine failures, reduced spare parts inventory, reduced mean time between failure of plant equipments and increased personnel safety, increased machine life, and reduced energy consumption (Christer et al., 1997; Kakkar, 1999; Beltran and Lopez, 2000; Lupinucci et al., 2000; Villar et al., 2000; and Carnero, 2006).

The practical implementation of PdM policy faces two major difficulties: one, absence of any concrete statistical model for PdM (Tan and Raghvan, 2010); and two, requirement of advanced monitoring technologies and sophisticated data acquisition systems. These difficulties make the implementation of PdM policy complex and a costly affair (Wendai and Daescu, 2002). In this paper, an effort has been made to device a PdM policy with least use of costly advanced monitoring technologies. This paper proposes a Modified FMEA technique, FMEORA (Failure Mode, Effects and Output Range Analysis) for PdM.

 
 

Operations Management Journal, Predictive Maintenance (PdM) Model, Failure Mode and Effects Analysis (FMEA), Modified FMEA technique, FMEORA (Failure Mode, Effects and Output Range Analysis), FMEA Approach.