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Integrated Research of Predictive Maintenance Strategy and Economic Production Quantity Based on System Status

Author: WangYing
Tutor: PanErShun
School: Shanghai Jiaotong University
Course: Management Science and Engineering
Keywords: economical production quantity (EPQ) predictive maintenance strategy (PdM) ARMA model partial least square method (PLS)
CLC: F273.4
Type: Master's thesis
Year: 2012
Downloads: 67
Quote: 0
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Equipment is the key character of production controlling. The basis and key to ensure machine productivity and reduce manufacturing cost is adopting suitable maintenance. Based on machine status prediction, predictive maintenance strategy (PdM) can reduce risk of machine failure usefully. Moreover, considering the influence of equipment situation on the planning of production lot, predictive maintenance should be integrated into the traditional economical production quantity model (EPQ). With the aims to minimize the expected average cost, suitable maintenance is adopted into mass production to find the best economic lot and maintenance strategy.This paper studies a single machine and its production process. Assessment method of machine status is studied first. Partial Least Square (PLS) method is adopted to evaluate health status of equipment. Then based on equipment healthy index (HI) series, a prediction model is built, which can describe machine’s deterioration process. Meanwhile, the machine defect probability is find based on precise evaluation, which explore the used of equipment status prediction in PdM. On the basis of machine degradation, three statuses are settled and different maintenance methods are adopted on each status, which can improve maintenance effectivity and equipment capacity. Later, the influence of system status on economical lot is studied. Considering the influence of minimal maintenance, imperfect maintenance, replacement, equipment operation, maintenance time, inventory and production installation on expected average cost, two integrated models of EPQ and PdM are bullied. Finally, case studies are discussed to show the efficiency of these integrated models.This research work intends to help enterprise reduce manufacturing cost, improve machine reliability and production stability. It can provide a great scientific support to improve competitiveness of enterprise and realize digital production management.

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