The purpose of this paper is to develop a framework for the application of proportional hazard modelling to plant maintenance. Two regimes are investigated; a good-as-new regime where the hazard rate of the system is refreshed by either failure or preventive maintenance action. and a bad-as-old regime where only preventive maintenance action refreshes the hazard rate. The output of the analysis is the recommendation of optimal preventative maintenance plans under both of these regimes. Data for a local firm are used to illustrate the models. The inclusion of proportional hazard modelling is shown to yield improved maintenance plans in both regimes. A proposal for an adaptive scheme is made such that the maintenance plan can be adjusted as changing plant conditions warrant it.
An age model is proposed for modelling imperfect repairable systems operating under a non‐homogeneous Poisson framework. The imperfect repair model investigated here effectively includes good‐as‐new, imperfect repair and bad‐as‐old regimes that have appeared separately in reliability engineering literature. To incorporate the impact of the equipment's operating environment, proportional intensities assumptions are integrated into the age model. Maximum likeihood estimates are derived for the parameters of interest.
QIIME is a widely used, open-source microbiome analysis software package that converts raw sequence data into interpretable visualizations and statistical results. QIIME2 has recently succeeded QIIME1, becoming the most updated platform. The protocols in this article describe our effort in automating core functions of QIIME2, using datasets available at docs.qiime2.org. While these specific examples are microbial 16S rRNA gene sequences, our automation can be easily applied to other types of QIIME2 analysis.
In this paper, an analysis is conducted of the equipment failure regime known as bad‐as‐old. In such a regime, the repair of the machine after an unexpected emergency breakdown does not reset the hazard rate. Only a planned overhaul refreshes the hazard rate. This bad‐as‐old regime is modelled as a non‐homogeneous Poisson process. The structure is comprised of a base‐line hazard rate, here assumed to be Weibull, plus a covariate structure, since such (covariate) factors are found to influence equipment failure times. The results are compared to similar results1 when the underlying failure regime is assumed to be good‐as‐new. The analysis performed here is also compared to a previous analysis2 in which the base‐line hazard rate was of a non‐parametric form. Several tests are presented as well as an examination of the residuals in order to verify the appropriateness of the model chosen.
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