Supporting group maintenance through prognostics-enhanced dynamic dependability prediction

Abstract

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

PriMa: a prescriptive maintenance model for cyber-physical production systems

“…As an example, one may refer to the following selected studies: for developing a periodic preventive maintenance model (Franciosi, Lambiase, and Miranda 2017), enhancing preventive maintenance through integrating probabilistic and predictive models (Ruschel, Santos, and Loures 2017), establishing a generic simulation-based predictive maintenance (Zarte, Wunder, and Pechmann 2017), developing cloud-based predictive maintenance framework (Schmidt, Wang, and Galar 2017), and introducing a smart maintenance decision support using corporate big data analytics (Bumblauskas et al 2017) as well as applying various combinations of statistical datamining and supervised machine learning for condition-based maintenance examined in (Accorsi et al 2017). Specifically, dynamic-based prognostic models are used for predicting dependability in (Aizpurua et al 2017), Bayesian modelling is employed for optimisation of maintenance strategies in (Belyi et al 2017), and application of various machine learning methods for self-parameterising process monitoring and selfadjusting process strategies for series production has been investigated in (Denkena et al 2017). In this perspective, Wöstmann, Strauss, and Deuse (2017) examined existing predictive maintenance applications and their transferability to production systems considering a set of prerequisites for a successful implementation (i.e.…”

Performance and Economic Assessment of a Grid-Connected Photovoltaic Power Plant with a Storage System: A Comparison between the North and the South of Italy

“…Stochastic Hybrid Fault Tree Automaton (SHyFTA) is a modelling formalism that belongs to the umbrella of Dynamic Reliability [14,15], an engineering science that aims to study a system by the use of an holistic model that is able to consider the physics of the system process and its inter-relationships with the system dependability, (i.e., the probability of a system performing its task under some specifications like operative conditions, time of mission, restoration, maintenance resources, and so forth [32]).…”

A Study on the Impacts of Maintenance Duration on Dynamic Grouping Modeling and Optimization of Multicomponent Systems

“…A number of papers working on this topic have been recorded in the literature and can be classified into three different types according to the planning horizon: short-term horizon, long-term horizon, and rolling horizon. The grouping maintenance within a short-term planning horizon [19,20,21] does not guarantee the grouping performance within long-term horizon. While the grouping maintenance within long-term planning horizon [22,23] does not allow to dynamically update the maintenance planning.…”