Title: Equipment mean residual life estimation using logical analysis of data

Authors: Alireza Ghasemi; Sasan Esmaeili; Soumaya Yacout

Addresses: Industrial Engineering Department, Dalhousie University, Halifax, Canada ' Industrial Engineering Department, Dalhousie University, Halifax, Canada ' Mathematics and Industrial Engineering Department, Ecole Polytechnique de Montreal, Montreal, Canada

Abstract: Logical analysis of data (LAD) has the advantage of not relying on any statistical theory, which enables it to overcome the conventional problems concerning the statistical properties of the datasets. LAD's other advantage is its straightforward procedure and self-explanatory results. In this paper, we developed methods to calculate equipment's survival probability at a certain future moment, using LAD. We employed LAD's pattern generation procedure and introduced a guideline to use the generated patterns to estimate the equipment's survival probability. The proposed methods were applied on Prognostics and Health Management Challenge dataset provided by NASA Ames Prognostics Data Repository. Prognostics results obtained by the methods are compared with those of the proportional hazards model. The comparison reveals that the proposed methods are promising tools that compare favourably to the PHM. Since the proposed prognostics model is at its beginning phase, future directions are presented to improve the performance of the model.

Keywords: mean residual life; MRL; condition-based maintenance; CBM; logical analysis of data; LAD; prognostics; condition monitoring; equipment lifetime; equipment life estimation; pattern generation; proportional hazards; survival probability.

DOI: 10.1504/IJDSRM.2015.072764

International Journal of Decision Sciences, Risk and Management, 2015 Vol.6 No.1, pp.16 - 33

Received: 09 Sep 2014
Accepted: 16 Apr 2015

Published online: 28 Oct 2015 *

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