Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses

Abstract

Conditioned-based maintenance and prognostics and health management enable to optimize maintenance by scheduling the necessary repairs and replacements of technical system components according to their present and future health states. The assessment of future health states is the prognostics and health management keystone. Many technical production systems are made of numerous components implementing their functions. A method to assess the ability of multicomponent systems to carry out future production tasks is proposed to provide decision supports for production and maintenance planning for a better compromise between their objectives. It is based on components prognoses. To handle inherent uncertainties of these prognoses, the method is based on the Dempster Shafer theory and Bayesian networks inferences. Local prognoses are categorized and transformed to be compliant to Dempster Shafer theory. Patterns of systems are identified for which inferences are defined. The patterns are then used to model systems and to assess their abilities to achieve future tasks. An identification of components that should first undergo maintenance is proposed. An example implementing a fictitious complex systems is presented to show how the provided decision supports can be used for production and maintenance planning purposes.

An approach for joint scheduling of production and predictive maintenance activities

“…From the information about the devices provided by the PHM modules, methods like the ones proposed in [31], [32], assess the ability of sub-functions, functionalities, sub-systems and, so, of the machine to perform the TF to plan. Thus, production management has decision supports for assigning a TF to a machine or not.…”

“…The need of a maintenance task (TM) involving a specific device is declared at a time t when its failure probability makes the required machine functionalities unable to fully achieve within acceptable conditions the planned sequence of TFs under a given probability limit (ps). A method to identify the devices that should undergo maintenance to better improve the probability that the functionality achieves in good condition a production operation is described in [31], [32]. A device can be repaired by a maintainer if this one has the corresponding competencies to carry out the needed TM (association p).…”

“…The different forces and motions induce different levels of degradation and thus, the aging of the machine depends on the materials and the types of operations being performed. In this context generic methods based on Bayesian networks were proposed [31], [32]. These methods are designed for complex systems, providing the ability of machine functions to carry out planned production tasks.…”

“…The devices of technical production resources are continuously observed by PHM modules in order to provide information about their health states (diagnoses) and their future conditions (prognoses) [1]. The processing of these information (diagnoses and prognoses) by methods as the ones proposed in [31], [32] enables the assessments of the machines abilities to carry out production tasks as well as the identification of the devices to maintain. This enables the producer agents to schedule the production operations according to their machines current and future health states and to call the maintenance agents for maintenance activities when the scheduled tasks on their machines cannot be carried out in acceptable condition.…”