Maintenance schedule optimisation for manufacturing systems

In order to improve the overall equipment effectiveness, a new method for maintenance scheduling in manufacturing systems using the philosophies of TPM and MFOP is presented in this paper，considering both the production process and maintenance operations of manufacturing equipment. An LCC analysis is also conducted for evaluating and searching for the optimal maintenance schedule.

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Maintenance on Job Shop Industry: Review and Analysis

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.842.365 ◽

2016 ◽

Vol 842 ◽

pp. 365-372 ◽

Cited By ~ 3

Author(s):

Herman Budi Harja ◽

Tri Prakosa ◽

Yatna Yuwana Martawirya

Keyword(s):

Manufacturing Systems ◽

Job Shop ◽

Flow Shop ◽

Dynamic Condition ◽

Maintenance Optimization ◽

Reliability Growth ◽

Monitoring Cost ◽

Reliability Modeling ◽

Maintenance Schedule ◽

Reliability Improvement

This paper presents overviews about reliability and maintainability of equipment especially for job-shop manufacturing systems. The job shop industry has the characteristics of a more dynamic production than flow shop industries, where products with a variety of great but small amounts. Its dynamic condition certainly contributes directly to the failure rate and reliability growth of equipment. Therefore, proper maintenance should be done as the reliability improvement. Stages of reliability improvement are reliability modeling, reliability analysis and maintenance optimization. This stage is based on reliability growth of equipment that is indicated the deterioration process of failure components, it can be build from maintenance data history or condition data monitoring.. Cost is often considered in points of a maintenance schedule. This cost was affected by minimizing the negative effects of maintenance and maximizing the benefit of production. The attention at reliability and maintenance optimization is a well researches area until now. This paper presents a brief review of existing reliability and maintenance research. Several reliable methods in this area are discussed and maintenance on job-shop industry as future prospects is investigated. It is shown in this paper that some aspect in the area of maintenance on job-shop industry steel needs to be deeply developed.

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Study of Camshaft Grinders Faults Prediction Based on RBF Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.141.519 ◽

2011 ◽

Vol 141 ◽

pp. 519-523

Author(s):

Ting Dong ◽

Hong Jun Wang ◽

Lei Shi

Keyword(s):

Neural Network ◽

Production Line ◽

Manufacturing Systems ◽

Production Efficiency ◽

Clustering Algorithm ◽

Rbf Neural Network ◽

Approximation Error ◽

Maintenance Schedule ◽

Time Between Failures ◽

Production Type

Maintenance schemes in manufacturing systems are devised to reset the machines functionality in an economical fashion and keep it within acceptable levels. Camshaft grinders play the important role for the camshaft production line which is the massive production type. The camshaft grinders working condition is one of the critical sections which affected the production efficiency and profit of the manufactures. Nowadays the maintenance based on condition is carried out in order to meet the requirements of the market. The Time Between Failures (TBF) could be used for arranging the maintenance schedule. The faults prediction model based on RBF neural network, adopted K-means clustering algorithm to select clustering centre of radial basis function neural network (RBFNN), is proposed for the camshaft grinders which are the key equipment of camshaft production line. The TBF of the camshaft grinders are predicted by using this model, where the distribution density is 1, with the accepted network approximation error. An industrial example is used to illustrate the application of this model. The proposed method is effective and can be used for the suggestions for the practical workshop machines maintenance.

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