Reliability centered maintenance optimization for power distribution systems

a b s t r a c t Today's electricity distribution systems operate in a liberalized market. These systems should therefore be able to provide electricity to customers with a high degree of reliability and be cost-effective for suppliers. RCM (Reliability Centred Maintenance) was invented by the aircraft industry in the 1960s, to organize the increasing need for maintenance for reducing costs without reducing b safety. Today RCMmethods invented by ALADON [1] are seen as very complex and are not fully accepted by the Algerian power industry. The extensive need of human and capital resources in the introduction phase is also a negative factor that could be one of the reasons of why RCM methods are not used in our branch. This article provides a discussion of the two primary objectives of RCM: to ensure safety through preventive maintenance actions, and, when safety is not a concern, preserve functionality in the most economical manner. For the power distribution systems facilities, the mission should be considered at the same level as safety

A FIRST-AND SECOND-ORDER TURBULENCE MODELS IN HYDROGEN NON-PREMIXED FLAME

ABSTRACT The mathematical modelling of turbulent flames is a difficult task due to the intense coupling between turbulent transport processes and chemical kinetics. The model presented within this paper is focused on the turbulence-chemistry interaction. The topic of this study is the numerical simulation of turbulent non-premixed hydrogen flame with different turbulent models in order to invest gate their predictive capability. The two turbulent models are compared: the (k-ε) model with a limited Pope's correction and the Reynolds stress model (RSM). The predictions are validated against experimental data provided by Raman and laser Doppler anemometry (LDA) measurements for a turbulent jet hydrogen-air diffusion flame. The turbulence-chemistry interaction is handled with flame let approach. Simulations of test cases with simple geometries verify the developed model and compare favourably with results of earlier investigations that employed both (k-ε) and RSM closures with the CMC and PDF approache

Experts’ knowledge renewal and maintenance actions effectiveness in high-mix low-volume industries, using Bayesian approach

International audienceIncreasing demand diversity have resulted in high-mix low-volume production where success depends on our ability to quickly design and develop new products. This requires sustainable production capacities and efficient equipment utilization which is ensured through appropriate maintenance strategies. At present, these are derived from experts' knowledge, capitalized in FMECA (Failure Mode, Effect and Criticality Analysis) and/or maintenance procedures. (Abu-Samah et al. 2015) found increasing unscheduled breakdowns, failure durations and number of repair actions in each failure as the key challenges while sustaining production capacities in complex production environment. This is an evidence that maintenance based on the historical knowledge is not always effective to cope up with an evolving nature of equipment failure behaviors. Therefore, in this paper, we present an operational methodology based on Bayesian approach and an extended FMECA method to support experts' knowledge renewal and maintenance actions effectiveness. In the proposed methodology, we capitalize and model experts' existing knowledge from FMECA files as an operational Bayesian network (O-BN) to provide real time feedback on poorly executed maintenance actions. The accuracy of O-BN is monitored through drift in maintenance performance measurement (MPM) indicators that results in learning an unsupervised Bayesian network (U-BN) to discover new causal relations from historical data. The structural difference between O-BN and U-BN highlights potential new knowledge which is validated by experts prior to modify existing FMECA and associated maintenance procedures. The proposed methodology is evaluated in a well reputed high-mix low-volume semiconductor production line to demonstrate its ability to dynamically renew experts' knowledge and improve maintenance actions effectiveness