生產系統中利用製程不良率評估設備預防維護之研究

顧尚芳

doi:10.6840/cycu200300207

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生產系統中利用製程不良率評估設備預防維護之研究

A Study of Equipment Preventive Maintenance (EPM) with Applying Process Defective Rate for Production System

顧尚芳 , Masters　　Advisor：江瑞清　　

繁體中文 DOI： 10.6840/cycu200300207

預知性預防；可靠度；倒傳遞網路；預防維護；不良率； Defective Rate ； Back-Propagation Neural Network ； Prevention by Prediction ； Reliability ； Preventive Maintenance

Abstract │ Reference (49) │ Times Cited (9) │ Altmetrics

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Abstract

The purpose of this research is about combining the concepts of reliability and cost-effect analysis to develop a preventive maintenance decision system based on manufacturing process defective rate for those industries with the feature of high maintenance cost for the equipments.   
Parameters mainly considered in this paper include: Mean Time Between Failure (MTBF), Production Lot (N), Preventive Maintenance Cost (PMC), Item Cost (IC) and Process Time per Unit (PT). We expect not only to build a preventive maintenance model by using these parameters but also to decide the upper limit of process defective rate (P). Once the process defective rate is beyond to this limit, an immediate equipment maintenance has to be carried out. During this study, we use eM-Plant to simulate the proposed model, and use back-propagation neural network to construct the decision system.
In this study, data collected from the simulation process are utilized to demonstrate the feasibility of this model. The unique feature of the preventive maintenance decision system is that it can be applied to practical manufacturing process, and use its capability of prevention by prediction to detect the time to maintain in advance.

Key Word：Preventive Maintenance , Reliability , Defective Rate , Back-Propagation Neural Network , Prevention by Prediction

Reference ( 49 ) 〈TOP〉

5. Charles, E. Ebeling, An Introduction To Reliability and Maintainability Engineering, McGraw-Hill, International Editions, 1997.
連結：
6. Charytoniuk, W. and M. S. Chen, “ Neural-network-based demand forecasting in aderegulated environment ”, IEEE Transactions on industry applications, Vol.36, No.3, pp.893-898, May/June 2000.
連結：
7. Dantas, A., K. Yamamoto, M. V. Lamar and Y. Yamashita, “ Neural network for travel demand forecasting using GIS and Remote Sensing ”, IEEE proceeding, pp.435-440, 2000.
連結：
9. Hornik, K., M. Stinchcombe and H White, “ Multilayer Feedforward Networks are Universal Approximators”, Neural Networks, Vol. 2, pp.359-366, 1989.
連結：
11. Johnson, Perry L., Kantner, Rob and Kikora, Marcia A., TQM Team-Building and Problem Solving, Southfield, MI: Perry Johnson, Inc., pp.1-1 ~ 10-15, 1990.
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Times Cited (9) 〈TOP〉

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