Abstract
GAMM (Graphical Analysis for Maintenance Management) is a method that supports decision-making in the overall maintenance management through the visualization and graphical analysis of data, which has been developed and published by the authors. In addition, it allows for the identification of anomalous behavior in the equipment analyzed, whether derived from its own operations, maintenance activities, improper use of equipment or even as a result of design errors in the equipment itself. As a basis for analysis, the GAMM method uses a nonparametric estimator of the reliability function using all historical data or, alternatively, part of the history, allowing it to perform an analysis even with limited available data. However, for successful results are strictly necessary the experience and advanced knowledge about maintenance management process. With regard to the interpretability of GAMM method, a set of basic rules have been developed in this paper. This set of rules aim to help and support to maintenance managers to get a right and objective interpretation, improving the decision making. Typically, engineers have validated intelligent software system performance by running test cases through a system and comparing results against known results or expert opinion. We adopt this method of validation by comparing the results of selected cases by GAMM with the recommendations of HIMOS (Hybrid Intelligent Maintenance Optimization System, University of Salford), which was compared and validated by an expert panel.