Research On Key Technologies For Equipment Autonomic Logistics

With the development of equipment technology and the evolution of combatstyle, the goal of equipment maintenance and support is to be “scientific predictionand precise logistics”. The Autonomic Logistics System (ALS) is an intelligent,predictive, proactive and networked logistics architecture with the employment oftechnologies such as prognostics and health management (PHM) system and jointdistributed information system (JDIS). This new approach shows the potential for costsavings, increased operational availability and better system performance. There is anurgent need to build a flexible networked logistics architecture, to utilize theequipment’s real-time health information efficiently and to schedule logisticsresources successfully in a complex and dynamic logistics environment for betterALS efficiency.In order to establish a precise, dynamic and networked ALS, the characteristicsof logistics networks and the rules of equipment’s remaining useful life (RUL)estimation are analyzed entirely to improve logistics organization flexibility. Andthe optimal maintenance and support decision based on RUL estimation is studieddeeply to improve maintenance decision precision and resource supplycooperativeness.The main research contents include:1. Optimization design on ALS organizational architectureBased on the analysis of the tasks, entities and network characteristics of ALSorganizational architecture, a multi-element weighted super-network model oflogistics organizational architecture is presented using description and analysismethod of complex network and super-network. The performance indices whichdescribe the logistics organizational structure are defined, and two kinds of dynamicevolution (integration) models--stochastic evolution model and adaptive evolutionmodel are respectively proposed based on the evolution (integration) rules of ALSorganizational architecture. And then the effects of evolution modes and parameterson ALS organizational network performance are analyzed deeply to help designlogistics networks.2. Optimal predictive maintenance decision based on RUL estimationSince the RUL estimation process can be divided into discrete type andcumulative type, the dynamic characteristics of RUL estimation processes and therandomicity of RUL estimation results are thus analyzed, and the uncertainty indices,including false alarm rate and missed detection rate which depict RUL estimationperformance, are also proposed.(1) For discrete type of RUL estimation, under the assumption of continuous monitoring and perfect maintenance, a periodic/aperiodic maintenance decision modeland arithmetic are proposed with the aim of minimizing the long-term average costrate or maximizing average operational availability based on regeneration and renewalproperties of maintenance process. This model is demonstrated with a numericalimplementation example using Monte Carlo simulation to analyze the effects ofperiodic/aperiodic RUL estimation schedule function and preventive maintenancethreshold on optimal maintenance decision.(2) For cumulative type of RUL assessment method such as canary in RULestimation of electronic system, and under perfect maintenance assumption, acanary-driven predictive maintenance decision model is proposed with the aim ofminimizing the long-term average cost rate or maximizing average operationalavailability or maximizing average cost-effectiveness ratio, based on renewalproperties of maintenance process. For LRU-independent and LRU-dependentcanaries, the decision variables are respectively as prognostic distance, shapeparameter of life distribution (Weibull distribution) and as accumulated damage ratioand stochastic standard deviation of life distribution (Weibull distribution). Thismodel is demonstrated with a numerical implementation example using Monte Carlosimulation to analyze the effects of all decision variables on maintenance performance,and the optimal maintenance decision criterion is established thereafter in comparisonwith unscheduled maintenance and age replacement policy.3. Joint optimization of maintenance and inventory of ALSThe information integration and resources sharing modes of ALS are proposedbased on a comprehensively considering sense and respond characteristics and on theinformation flow and material flow of ALS. The mechanisam of costs generation onequipment maintenance and on spare part replenishment under the respond resourcessharing mode are analyzed. And a joint optimization model of maintenance andinventory based on RUL estimation is proposed to minimize the long-term averagecost rate. The arithmetic for optimal logistics strategies is designed using Monte Carlosimulation methods which can analyze the effect of missed detection rate and falsealarm rate on ALS performance.In summary, the weighted super-network model of ALS architecture and thedescription method of RUL estimation are proposed, and the key problems such as thedynamic evolution and design of ALS architecture, the optimal equipment predictivemaintenance decision and the cooperative resource supply of ALS are all presentedand resolved after the requirement analysis of establishment and optimization of ALS.This dissertation aims at exploring the construct and decision of ALS in a thoroughand deep way, with the hope that these researches may have some academic andpractical significance.