A prognostic function for complex systems to support production and maintenance co-operative planning based on an extension of object oriented Bayesian networks

Abstract

The high costs of complex systems lead companies to improve their efficiency. This improvement can particularly be achieved by reducing their downtimes because of failures or for maintenance purposes. This reduction is the main goal of Condition-Based Maintenance and of Prognostics and Health Management. Both those maintenance policies need to install appropriate sensors and data processes not only to assess the current health of their critical components but also their future health. These future health assessments, also called prognostics, produce the Remaining Useful Life of the components associated to imprecision quantifications. In the case of complex systems where components are numerous, the matter is to assess the health of whole systems from the prognostics of their components (the local prognostics). In this paper, we propose a generic function that assesses the future availability of complex systems from their local prognostics (the prognostics of their components) by using inferences rules. The results of this function can then be used as decision support indicators for planning productive and maintenance tasks. This function exploits a proposed extension for Object Oriented Bayesian Networks (OOBN) used to model the complex system in order to assess the probabilities of failure of components, functions and subsystems. The modeling of the complex system is required and it is presented as well as modeling transformations to tackle some OOBN limitations. Then, the computing inference rules used to define the future availability of complex systems are presented. The extension added to OOBN consists in indicating the components that should first be maintained to improve the availabilities of the functions and subsystems in order to provide a second kind of decision support indicators for maintenance. A fictitious multi-component system bringing together most of the structures encountered in complex systems is modeled and the results obtained from the application of the proposed generic function are presented as well as ways that production and maintenance planning can used the computed indicators. Then we show how the proposed generic prognostic function can be used to predict propagations of failures and their effects on the functioning of functions and subsystems.

Towards a generic prognostic function of technical multi-component systems taking into account the uncertainties of the predictions of their components

“…Further works will explore the ability of pignistic transformations to ease the decision making process for maintenance and production planning. Another development should be made in order to identify the components that should undergo maintenance as it is proposed in [6] where this is done in an implementation based on object-oriented Bayesian networks.…”

“…Esteban Lemaitre Gonzalez, Xavier Desforges, and Bernard Archimède I Since all measurement systems have their own features related to their accuracy, data about their accuracies must be integrated in the processes. It is also possible that these features are not well known as it is sometimes the case in domain of components prognostics as mentioned in works listed in [6]. However this could be process thanks to the implementation of the possibility theory [12].…”

“…A graph can be used for modeling functional and structural knowledge, where the nodes represent entities and the directed but unweighted arcs are either belonging relations (i.e., a component or a sub-function to a function) or a causal relationship (i.e., the failure of the upstream entity makes the downstream entity inoperative) [6]. In reliability diagrams [21,22], three types of entities are considered corresponding to frames: the components, entities in serial structures that implement what we call simple functions and entities in parallel structures that implement redundancies.…”

“…X. Desforges Furthermore, many studies, as mentioned by Desforges in [6], that deal with prognosis are focused on the assessment of the Remaining Useful Lifetimes (RULs) of components (e.g., ball-bearings, gear trains, train pantographs, braking systems, batteries, etc) [7].…”

Wireless Sensor Network Dynamic Mathematics Modeling and Node Localization

“…However, the cost of the anchor node is higher than that of ordinary nodes. If 10% of the nodes are anchor nodes, the price of the network will increase 10 times [8][9][10][11][12][13]. However, when the unknown node is located, it will no longer need expensive anchor nodes.…”