Selection of diagnostic techniques and instrumentation in a predictive maintenance program. A case study

Authors:
M. C. Carnero
Affiliations:
University of Castilla-La Mancha, Technical School of Industrial Engineering, Avda. Camilo Jose Cela s/n, 13071 Ciudad Real, Spain
Venue:
Decision Support Systems
Year:
2005

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Abstract

Predictive maintenance programs (PMPs) can provide significant advantages in relation to quality, safety, availability and cost reduction in industrial plants. Nevertheless, during implementation, different decision making processes are involved, such as the selection of the most suitable diagnostic techniques. A wrong decision can lead to the failure of the setting up of the predictive maintenance program and its elimination, with the consequent economic losses, as the setting up of these programs is a strategic decision. In this article, a model is proposed that carries out the decision making in relation to the selection of the diagnostic techniques and instrumentation in the predictive maintenance programs. The model uses a combination of tools belonging to operational research such as: analytic hierarchy process (AHP) and factor analysis (FA). The model has been tested in screw compressors when lubricant and vibration analyses are integrated.