1,488 research outputs found
Advanced Techniques for Assets Maintenance Management
16th IFAC Symposium on Information Control Problems in Manufacturing INCOM 2018
Bergamo, Italy, 11–13 June 2018. Edited by Marco Macchi, László Monostori, Roberto PintoThe aim of this paper is to remark the importance of new and advanced techniques supporting decision making in different business processes for maintenance and assets management, as well as the basic need of adopting a certain management framework with a clear processes map and the corresponding IT supporting systems. Framework processes and systems will be the key fundamental enablers for success and for continuous improvement. The suggested framework will help to define and improve business policies and work procedures for the assets operation and maintenance along their life cycle. The following sections present some achievements on this focus, proposing finally possible future lines for a research agenda within this field of assets management
Decline and repair, and covariate effects
The failure processes of repairable systems may be impacted by operational and environmental stress factors. To accommodate such factors, reliability can be modelled using a multiplicative intensity function. In the proportional intensity model, the failure intensity is the product of the failure intensity function of the baseline system that quantifies intrinsic factors and a function of covariates that quantify extrinsic factors. The existing literature has extensively studied the failure processes of repairable systems using general repair concepts such as age-reduction when no covariate effects are considered. This paper investigates different approaches for modelling the failure and repair process of repairable systems in the presence of time-dependent covariates. We derive statistical properties of the failure processes for such systems
Supporting group maintenance through prognostics-enhanced dynamic dependability prediction
Condition-based maintenance strategies adapt maintenance planning through the integration of online condition monitoring of assets. The accuracy and cost-effectiveness of these strategies can be improved by integrating prognostics predictions and grouping maintenance actions respectively. In complex industrial systems, however, effective condition-based maintenance is intricate. Such systems are comprised of repairable assets which can fail in different ways, with various effects, and typically governed by dynamics which include time-dependent and conditional events. In this context, system reliability prediction is complex and effective maintenance planning is virtually impossible prior to system deployment and hard even in the case of condition-based maintenance. Addressing these issues, this paper presents an online system maintenance method that takes into account the system dynamics. The method employs an online predictive diagnosis algorithm to distinguish between critical and non-critical assets. A prognostics-updated method for predicting the system health is then employed to yield well-informed, more accurate, condition-based suggestions for the maintenance of critical assets and for the group-based reactive repair of non-critical assets. The cost-effectiveness of the approach is discussed in a case study from the power industry
The safety case and the lessons learned for the reliability and maintainability case
This paper examine the safety case and the lessons learned for the reliability and maintainability case
On the Statistical Modeling and Analysis of Repairable Systems
We review basic modeling approaches for failure and maintenance data from
repairable systems. In particular we consider imperfect repair models, defined
in terms of virtual age processes, and the trend-renewal process which extends
the nonhomogeneous Poisson process and the renewal process. In the case where
several systems of the same kind are observed, we show how observed covariates
and unobserved heterogeneity can be included in the models. We also consider
various approaches to trend testing. Modern reliability data bases usually
contain information on the type of failure, the type of maintenance and so
forth in addition to the failure times themselves. Basing our work on recent
literature we present a framework where the observed events are modeled as
marked point processes, with marks labeling the types of events. Throughout the
paper the emphasis is more on modeling than on statistical inference.Comment: Published at http://dx.doi.org/10.1214/088342306000000448 in the
Statistical Science (http://www.imstat.org/sts/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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