Graphical Techniques and Methods: Validating how they Improve Critical Assets Management

Graphical Techniques and Methods: Validating how they Improve Critical Assets Management

Adolfo Crespo Márquez (University of Seville, Spain), Luis Barberá (University of Seville, Spain), Khairy A. H. Kobbacy (Taibah University, Saudi Arabia) and Samir M. Shariff (Taibah University, Saudi Arabia)
Copyright: © 2017 |Pages: 13
DOI: 10.4018/978-1-5225-0651-5.ch004
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GAMM (Graphical Analysis for Maintenance Management) is a method that supports decision-making in maintenance management through the visualization and graphical analysis of data. GAMM also allows the identification of anomalous behavior in equipment, derived from its own operations, maintenance activities, improper use of equipment or even as a result of design errors. As a basis for analysis, the GAMM method uses a nonparametric estimator of the reliability function using historical data, sometimes in very limited amounts. However, for successful results, experience and advanced knowledge in maintenance management are strictly necessary. In order to ease the interpretations of the GAMM method results, with the intention that the method becomes really amicable for managers, a set of basic rules have been developed. This set of rules leads to a proper and objective interpretation of GAMM results, improving the decision making.
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Background And Problem Statement

The GAMM method (Barberá et al. 2013) is a support tool for operational maintenance management and preventive maintenance program evaluation. The tool provides useful information regarding the reliability and maintainability of systems or equipment for analysis when considering variables such as type of intervention, duration of intervention or the existence of a stop during the intervention of the equipment/system. This broadens the spectrum display for maintenance management, acquiring new evaluation parameters and graphically determining possible areas of improvement.

GAMM is a quantitative and qualitative analysis method that intends to support decision making in maintenance management using the logic of a dispersion diagram which integrates the following variables: type of maintenance task, duration, and state of equipment/system during the maintenance intervention. The combination of these new variables with a graphic display (Graphic 1 and 2, Figure 1 and 2) of the sequence of interventions generates synergies with regard to the information given by the diagram, thus establishing new sources of information that can be analyzed. In addition, GAMM is able to estimate the reliability function, which is calculated using the algorithms on the basis of the nonparametric method of Nelson–Aalen, and evaluated in a given time ti, that is, in the moment before the maintenance intervention. The estimation method can consider both, the historic full data, and the censored historical data.

Figure 1.

Examples of the graphs generated by GAMM (Graphic 1)


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