Preventive Maintenance Scheduling

2013 ◽  
Vol 4 (2) ◽  
pp. 69-94
Author(s):  
S.A. Oke ◽  
O.E. Charles-Owaba ◽  
A.E. Oluleye
Keyword(s):  
Cost Function ◽  
Inventory Management ◽  
Preventive Maintenance ◽  
Unit Cost ◽  
Spare Parts ◽  
Maintenance Scheduling ◽  
Maintenance Cost ◽  
Test Problems ◽  
Idle Period ◽  
Cost Parameter

In this work, the effectiveness of preventive maintenance scheduling (PMS) decisions was reported based on a techno-economic model that reflects cost objective function for ship maintenance activities. With a potential to impact on both transportation businesses and users of transportation services, the model provides an alternative to the combined classical literature problems of spare-parts inventory management and control, failure prediction and reliability. The PMS model developed incorporates separate and combined functions of indirect, direct and factor maintenance costs. Idleness period for various formulated schedules are evaluated and compared. First, a general form of the preventive maintenance cost function incorporating unit cost of maintaining ships, a set of cost function parameters and variables was developed. The operations research framework for the problem is then applied to obtain test cases in which cost parameter(s) was/were used for scheduling decisions. Monte Carlo simulation is applied to generate additional test problems. Practical data were used to validate the model. For each problem, optimal schedules based on one to four cost parameters were determined. For each schedule, the total maintenance cost, cost of idleness, total ship idle period and total ship operation period were computed under inflation, opportunity and combined opportunity and inflation and compared with the values corresponding to maintenance cost parameter using t-test (p<0.05). Thus, the use of combined data from maintenance, opportunity and inflation for preventive maintenance scheduling of a fleet of ships is more effective than direct maintenance cost approach.

Maintenance scheduling based on PROMETHEE method in conjunction with group technology philosophy

2018 ◽  
Vol 35 (7) ◽  
pp. 1423-1444 ◽  
Author(s):  
Abdelhakim Abdelhadi
Keyword(s):  
Preventive Maintenance ◽  
Group Technology ◽  
Spare Parts ◽  
Strategic Decision ◽  
Maintenance Cost ◽  
Manufacturing Strategy ◽  
Content Type ◽  
Clustering Problem ◽  
Promethee Method ◽  
The Impact

Purpose The purpose of this paper is to implement a strategic decision-making framework by selecting clusters of maintainable machines and scheduling their maintenance as part of a company’s manufacturing strategy. Design/methodology/approach Multi-criteria clustering problem in conjunction with the application of a group technology is used to establish clusters of maintainable machines based on their need for maintenance according to the type of failures they can encounter. Findings Using the concept of group technology in conducting preventive maintenance will result in the grouping of machines according to the impact of a failure based on the criteria specified by the decision makers. Accordingly, it will facilitate the process of executing the maintenance itself by ordering spare parts and informing the maintenance personnel which will lead to minimize the maintenance cost. Originality/value The results presented in this paper are reliable, objective may be used to minimize the total cost of conducting preventive maintenance in a manufacturing environment.

scholarly journals Joint Optimization of Preventive Maintenance and Spare Parts Inventory with Appointment Policy

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Jing Cai ◽  
Yibing Yin ◽  
Li Zhang ◽  
Xi Chen
Keyword(s):  
Inventory Management ◽  
Preventive Maintenance ◽  
Spare Parts ◽  
Combination Method ◽  
Joint Optimization ◽  
Inventory Level ◽  
Cost Rate ◽  
Proposed Model ◽  
Spare Parts Inventory ◽  
The Cost

Under the background of the wide application of condition-based maintenance (CBM) in maintenance practice, the joint optimization of maintenance and spare parts inventory is becoming a hot research to take full advantage of CBM and reduce the operational cost. In order to avoid both the high inventory level and the shortage of spare parts, an appointment policy of spare parts is first proposed based on the prediction of remaining useful lifetime, and then a corresponding joint optimization model of preventive maintenance and spare parts inventory is established. Due to the complexity of the model, the combination method of genetic algorithm and Monte Carlo is presented to get the optimal maximum inventory level, safety inventory level, potential failure threshold, and appointment threshold to minimize the cost rate. Finally, the proposed model is studied through a case study and compared with both the separate optimization and the joint optimization without appointment policy, and the results show that the proposed model is more effective. In addition, the sensitivity analysis shows that the proposed model is consistent with the actual situation of maintenance practices and inventory management.

Expected maintenance waste reduction benefits after implementation of Just in Time (JIT) philosophy in maintenance (a statistical analysis)

2019 ◽  
Vol 25 (1) ◽  
pp. 25-40 ◽  
Author(s):  
Sandeep Phogat ◽  
Anil Kumar Gupta
Keyword(s):  
Data Interpretation ◽  
General Information ◽  
Spare Parts ◽  
Waste Reduction ◽  
Maintenance Cost ◽  
Just In Time ◽  
Content Type ◽  
Great Saving ◽  
Research Findings ◽  
Structured Questionnaire

Purpose The maintenance department of today, like many other departments, is under sustained pressure to slash costs, show outcome and support the assignment of the organization, as it is a commonsensical prospect from the business perspective. The purpose of this paper is to examine expected maintenance waste reduction benefits in the maintenance of organizations after the implementation of just-in-time (JIT) managerial philosophy. For this, a structured questionnaire was designed and sent to the 421 industries in India. Design/methodology/approach The designed questionnaire was divided into two sections A and B to assist data interpretation. The aim of the section A was to build general information of participants, type of organization, number of employees, annual turnover of the organization, etc. Section B was also a structured questionnaire developed based on a five-point Likert scale. The identified critical elements of the JIT were included in the questionnaire to identify the maintenance waste reduction benefits in the maintenance of organizations. Findings On the basis of the 133 responses, hypothesis testing was done with the help of Z-test, and it was found out that in maintenance, we can reduce a large inventory of spare parts and also shorten the excessive maintenance activities due to the implementation of JIT philosophy. All the four wastes: waste of processing; waste of rejects/rework/scrap in case of poor maintenance; waste of the transport of spares, and waste of motion, have approximately equal weightage in their reduction. Waste of waiting for spares got the last rank, which showed that there are little bit chances in the reduction of waiting for spares after the implementation of JIT philosophy in maintenance. Practical implications The implication of the research findings for maintenance of organizations is that if maintenance practitioners implement elements of JIT philosophy in maintenance then there will be a great reduction in the maintenance wastes. Originality/value This paper will be abundantly useful for the maintenance professionals, researchers and others concerned with maintenance to understand the significance of JIT philosophy implementation to get the expected reduction benefits in maintenance wastes of organizations which will be helpful in the great saving of maintenance cost and time side by side great increment in the availability of machines.

scholarly journals Multi-Criteria Spare Parts Classification Using the Deep Convolutional Neural Network Method

2021 ◽  
Vol 11 (15) ◽  
pp. 7088
Author(s):  
Ke Yang ◽  
Yongjian Wang ◽  
Shidong Fan ◽  
Ali Mosleh
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Inventory Management ◽  
Hierarchical Classification ◽  
Critical Issue ◽  
Spare Parts ◽  
Deep Convolutional Neural Network ◽  
Learning Ability ◽  
Second Phase ◽  
Good Decision

Spare parts management is a critical issue in the industrial field, alongside planning maintenance and logistics activities. For accurate classification in particular, the decision-makers can determine the optimal inventory management strategy. However, problems such as criteria selection, rules explanatory, and learning ability arise when managing thousands of spare parts for modern industry. This paper presents a deep convolutional neural network based on graph (G-DCNN) which will realize multi-criteria classification through image identification based on an explainable hierarchical structure. In the first phase, a hierarchical classification structure is established according to the causal relationship of multiple criteria; in the second phase, nodes are colored according to their criteria level status so that the traditional numerical information can be visible through graph style; in the third phase, the colored structures are transferred into images and processed by structure-modified convolutional neural network, to complete the classification. Finally, the proposed method is applied in a real-world case study to validate its effectiveness, feasibility, and generality. This classification study supplies a good decision support to improve the monitor-focus on critical component and control inventory which will benefit the collaborative maintenance.

scholarly journals Optimal Preventive Maintenance of Wind Turbine Components with Imperfect Continuous Condition Monitoring

Energies ◽  
2019 ◽  
Vol 12 (19) ◽  
pp. 3801 ◽  
Author(s):  
Ahmed Raza ◽  
Vladimir Ulansky
Keyword(s):  
Wind Turbine ◽  
Condition Monitoring ◽  
Preventive Maintenance ◽  
False Negative ◽  
Maintenance Cost ◽  
Arbitrary Distribution ◽  
Average Lifetime ◽  
True Negative ◽  
Degradation Failure ◽  
Lifetime Maintenance

Among the different maintenance techniques applied to wind turbine (WT) components, online condition monitoring is probably the most promising technique. The maintenance models based on online condition monitoring have been examined in many studies. However, no study has considered preventive maintenance models with incorporated probabilities of correct and incorrect decisions made during continuous condition monitoring. This article presents a mathematical model of preventive maintenance, with imperfect continuous condition monitoring of the WT components. For the first time, the article introduces generalized expressions for calculating the interval probabilities of false positive, true positive, false negative, and true negative when continuously monitoring the condition of a WT component. Mathematical equations that allow for calculating the expected cost of maintenance per unit of time and the average lifetime maintenance cost are derived for an arbitrary distribution of time to degradation failure. A numerical example of WT blades maintenance illustrates that preventive maintenance with online condition monitoring reduces the average lifetime maintenance cost by 11.8 times, as compared to corrective maintenance, and by at least 4.2 and 2.6 times, compared with predetermined preventive maintenance for low and high crack initiation rates, respectively.

Analisis Pemeliharaan Mesin Conveyor Menggunakan Metode Preventive dan Breakdown Maintenance untuk Meminimumkan Biaya Pemeliharaan Mesin pada PT X

2021 ◽  
Vol 1 (1) ◽  
pp. 1-9
Author(s):  
Meli Amelia ◽  
Tasya Aspiranti
Keyword(s):  
Data Analysis ◽  
Preventive Maintenance ◽  
Research Method ◽  
Research Data ◽  
Maintenance Cost ◽  
Know How ◽  
Descriptive Research ◽  
Quantitative Descriptive ◽  
Engine Maintenance ◽  
Care Study

Abstract. This research aims to know how the implementation of maintenance conducted by PT X and how maintenance by PT X used the preventive and breakdown maintenance methods to minimize engine maintenance cost. The research method used in this study is care study whereas this type of research is quantitative descriptive research. Technique of collecting data in this research by obsererving, interviewing and collecting documents related to research. Data analysis used by using preventive and breakdown maintenance methods. The result of this research is PT X performs maintenance of the engine by using preventive maintenance such as routine maintenance, semi-overhaul forecast maintenance and annual maintenance and breakdown maintenance are usually performed when the machine is fully damaged or dead. PT X should implement preventive maintenance because it is more efficient at 13,2% than the company’s maintenance. Abstrak. Penelitian ini bertujuan untuk mengetahui bagaimana pelaksanaan pemeliharaan mesin yang dilakukan PT X dan bagaimana pemeliharaan mesin yang yang dilakukan PT X dengan menggunakan metode preventive dan breakdown maintenance untuk meminimumkan biaya pemeliharaan mesin. Metode penelitian yang dilakukan dalam penelitian ini studi kasus sedangkan jenis penelitian ini adalah penelitian deskriptif kuantitatif. Teknik pengumpulan data dalam penelitian ini dengan melakukan observasi, wawancara dan pengumpulan dokumen-dokumen yang berkaitan dengan penelitian. Analisis data yang digunakan dengan menggunakan metode preventive dan breakdown maintenance. Hasil dari penelitian ini adalah PT X hendaknya melakukan pemeliharaan mesin dengan menggunakan preventive maintenance seperti perawatan rutin, perawatan semi overhaul dan perawatan tahunan dan breakdown maintenance biasa dilakukan saat mesin mengalami kerusakan atau mati total. PT X hendaknya melaksanakan preventive maintenance karena lebih efisien sebesar 13,2% dibandingkan pemeliharaan yang dilakukan perusahaan.

Reliability Analysis Using Deep Learning

2018 ◽  
Author(s):  
Chong Chen ◽  
Ying Liu ◽  
Xianfang Sun ◽  
Shixuan Wang ◽  
Carla Di Cairano-Gilfedder ◽  
...  
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Reliability Analysis ◽  
General Principle ◽  
Preventive Maintenance ◽  
Maintenance Cost ◽  
Empirical Results ◽  
Time Between Failures ◽  
Learning Techniques ◽  
Deep Learning Model

Over the last few decades, reliability analysis has gained more and more attention as it can be beneficial in lowering the maintenance cost. Time between failures (TBF) is an essential topic in reliability analysis. If the TBF can be accurately predicted, preventive maintenance can be scheduled in advance in order to avoid critical failures. The purpose of this paper is to research the TBF using deep learning techniques. Deep learning, as a tool capable of capturing the highly complex and nonlinearly patterns, can be a useful tool for TBF prediction. The general principle of how to design deep learning model was introduced. By using a sizeable amount of automobile TBF dataset, we conduct an experiential study on TBF prediction by deep learning and several data mining approaches. The empirical results show the merits of deep learning in performance but comes with cost of high computational load.

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