Optimal integrated maintenance policy based on quality deterioration

Kouki, Mondher; Dellagi, Sofiène; Achour, Zied; Erray, Walid

doi:10.1109/ieem.2014.7058756

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Kuo [10] presented a maintenance strategy for a joint machine and product quality control issue of a finite horizon discrete time state unobservable Markovian deteriorating batch production system, using dynamic programming. Kouki et al [11] developed an approach joining quality control and maintenance aspects by considering a PM strategy with minimal repair to minimize the total cost (including costs of non-conforming units and maintenance) per unit time. Ho and Quinino [12] presented an integrated model for process control and on-line corrective maintenance using a variable sampling interval (VSI) and Markov chain approach.…”

Section: Arlmentioning

confidence: 99%

“…FC pm i is the fixed cost of preventive maintenance of the i th component or the cost of consumables during the repair of the i th component. N pm i is the number of preventive maintenances of the i th component, which is calculated as [37] N pm i = T evalution T pm i (11) T pm i is the preventive maintenance interval of the i th component and is calculated as [9]…”

Section: ) Preventive Maintenance Policy Cost Modelmentioning

confidence: 99%

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New Integrated Model for Maintenance Planning and Quality Control Policy for Multi-Component System Using an EWMA Chart

et al. 2019

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Section: Arlmentioning

confidence: 99%

Section: ) Preventive Maintenance Policy Cost Modelmentioning

confidence: 99%

New Integrated Model for Maintenance Planning and Quality Control Policy for Multi-Component System Using an EWMA Chart

et al. 2019

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“…The close relationship between maintenance and quality has been considered by many researchers and has resulted in the development of the integrated models (Al-Najjar, 2001;Cassady et al, 2000;Ben-Daya and Rahim, 2000;Chiu and Huang, 1996). In recent years, Kouki et al (2014) investigated the relationship between maintenance and quality by developing an economic strategy joining simultaneously maintenance and quality aspects. They considered a PM strategy with minimal repair in order to reduce the expected total cost per unit time including simultaneously maintenance cost and non-conforming cost.…”

Section: Literature Reviewmentioning

confidence: 99%

Selective maintenance optimization under schedule and quality constraints

Tambe¹,

Kulkarni

2016

International Journal of Quality &Amp; Reliability Management

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Purpose – The traditional practice for maintenance, quality control and production scheduling is to plan independently irrespective of an interrelationship exist between them. The purpose of this paper is to develop an approach for integrating maintenance, quality control and production scheduling. The objective is to investigate the benefits of the integrated effect in terms of the expected total cost of system operation of the three functions. Design/methodology/approach – The proposed approach is based on the conditional reliability of the components. Cost model for integrating selective maintenance, quality control using sampling-based procedure and production scheduling is developed using the conditional reliability. An integrated approach is such that, first an optimal schedule for the batches to be processed is obtained independently while the maintenance and quality control decisions are optimized considering the optimal schedule on the machine. The expected total cost of conventional approach, i.e. “No integration” is calculated to compare the effectiveness of integrated approach. Findings – The integrated approach have shown a higher cost saving as compared to the independent planning approach. The approach is practical to implement as the results are obtained in a reasonable computational time. Practical implications – The approach presented in this paper is generic and can be applied at planned as well as unplanned opportunities. The proposed integrated approach is dynamic in nature, as during maintenance opportunities, it is possible to optimize the decision on maintenance, quality control and production scheduling considering the current age of components and production requirement. Originality/value – The originality of the paper is in the approach for integration of the three elements of shop floor operations that are usually treated separately and rarely touched upon by researchers in the literature.

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“…The control, monitoring and maintenance of production line equipment are fundamental activities for the quality and performance of the productive process [1,2,3,4]. Sensors and actuators play an important role in the operation of various machines such as conveyor belts, generators, mixers, compressors, furnaces, welding machines, among others, so they must always be in proper working condition.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Motor Failure Time Using An Artificial Neural Network

Sampaio

Filho

Silva³

et al. 2019

Sensors

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Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

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Optimal integrated maintenance policy based on quality deterioration

Cited by 5 publications

References 12 publications

New Integrated Model for Maintenance Planning and Quality Control Policy for Multi-Component System Using an EWMA Chart

New Integrated Model for Maintenance Planning and Quality Control Policy for Multi-Component System Using an EWMA Chart

Selective maintenance optimization under schedule and quality constraints

Prediction of Motor Failure Time Using An Artificial Neural Network

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