Identifying the Best Fit Failure Distribution and the Parameters of Machine's Component: A New Approach

In industries, failure of the equipment to function became a major contribution to the production losses and high maintenance cost. Therefore, there is a need to have an optimal maintenance strategy such as replacement, repair and inspection. Before any optimal maintenance strategy can be implemented failure distribution and the parameters of the machine’s component need to be identified. Therefore, the main objective in this paper is to propose a new approach in applying the Least-Squares Curve-Fitting (LSCF) and Maximum Likelihood Estimator (MLE) techniques in identifying the failure distribution and the parameters of machine’s component. The new approach proposed can assist maintenance engineers to make more precise identification in failure data analysis as well as in maintenance optimisation analysis. The paper starts by introducing the application of LSCF and MLE techniques to identify the best failure fit distribution and its parameters. It follows by numerical examples to determine whether the best fit failure distribution and its parameters are applicable to be applied in maintenance optimisation analysis. This is carried out by comparing the proposed new approach with a case study from the literature.