PM Schedule Research Articles

Bi-objective unrelated parallel machine joint scheduling of jobs and preventive maintenance with a dynamic speed-scaling technique

Nowadays, industries contribute to a substantial consumption of energy. This consumption leads to significant greenhouse gas emissions, energy dependence and ever-increasing costs for manufacturers. Controlling consumption and improving energy efficiency in industry are therefore major challenges in terms of environmental issues. In this context, this article examines an energy-efficient bi-objective unrelated parallel machine joint scheduling of jobs and preventive maintenance activities problem to minimize both makespan and total energy consumption. The parallel machines are speed-scaling. To solve the problem, we propose a bi-objective mixed integer linear programming (MILP) model. Since the problem involves assigning jobs to machines and selecting an appropriate processing speed level for each job, we characterize each individual by two vectors: a job-machine assignment vector and a speed vector. To the best of our knowledge, no papers considering both production scheduling and Preventive Maintenance periods with minimizing the bi-objective makespan and the total of energy consumption. The gap between theory and practice prevents the satisfying performance of the schedules in industry. The purpose of this paper is to establish a joint model for integrating the production and PM scheduling to optimize the bi-objective of the total of energy consumption (TEC) and makespan (Cmax) simultaneously; and to make the theory applied in practice more efficaciously. The performance of the proposed mixed binary integer programming model is evaluated based on the exact method of Branch and Bound integrated on the CPLEX solver plus analyses using MATLAB software. A study of the results proved the performance of the model developed.

Data-driven dynamic predictive maintenance for a manufacturing system with quality deterioration and online sensors

Manufacturing systems are generally subject to product quality deterioration as a result of degradation of machine tooling-components, and thus tooling-component replacement has always been an effective way to ensure high product quality. For individual tooling components, online degradation data from sensors contain the information about their unique degradation patterns, which can be used to achieve precise degradation prediction. Therefore, this paper proposes a data-driven dynamic predictive maintenance policy which utilizes online degradation data to continuously enhance the degradation prediction of tooling components and further constantly revise the maintenance schedule. To account for individual heterogeneity, the tooling-component degradation rates are assumed as random variables, whose posteriori distributions are continuously updated using the online degradation data based on Bayesian approach. The real-time degradation prediction of tooling components is further used to predict the deterioration of product quality and machine reliability based on the response model and integrated hazard function. The quality and reliability deteriorations are measured by costs to construct a dynamic cost rate function, which is used to make adaptive PM schedules. Case study shows that the proposed policy can make practical maintenance schedules and achieve cost saving in general.

Usulan Pembaharuan jadwal Kegiatan Preventive Maintenance pada Mesin Curing PCR PT.XYZ Menggunakan Metoda Distribusi Weibull

Seringnya terjadi kegiatan perawatan (downtime) tidak terencana mesin Curing pada line produksi ban mobil PT XYZ merupakan indikasi adanya ketidakefektifan perawatan terencana (PM) yang diterapkan perusahaan. Downtime tidak terencana menyebabkan line produksi berhenti dan kehilangan waktu berproduksi, meningkatnya jumlah produk gagal saat setup, dan tingginya biaya perawatan mesin produksi. Perusahaan perlu meningkatan efektivitas dan efisiensi kinerja system perawatan melalui pembaharuan jadwal perawatan agar availability mesin tinggi. Tujuan penelitian ini adalah mengidentifikasi komponen penyebab kegagalan fungsi mesin curing dan menghasilkan rekomendasi waktu interval perawatannya yang sesuai. Metoda distribusi Weibull digunakan untuk mendapatkan nilai index of fit time to failure beberapa komponen kritis mesin, menganalisa laju kerusakan komponen, menghitung nilai mean time between failure (MTBF) komponen sebagai durasi expected lifetime komponen tersebut. Hasil yang diperoleh menunjukkan bahwa terdapat dua komponen kritis pada mesin curing dengan indikasi laju kerusakan seiring waktu (nilai β>1), yaitu silinder press dan EPV dengan nilai MTBF sebesar 89 hari untuk silinder press dan 231 hari untuk EPV. Hasil kajian ini mengusulkan pembaharuan jadwal PM mesin curing agar mempertimbangkan penambahan kegiatan PM baru yang mengakomodir nilai durasi expected lifetime dari komponen-komponen penyebab mesin breakdown, sehingga dapat meminimalkan downtime tidak terencana.

Optimal failure mode-based preventive maintenance scheduling for a complex mechanical device

This paper addresses the issue of PM scheduling for a complex mechanical with different failure modes. Unlike conventional studies which only consider a single failure mode without prior health information, our work jointly optimizes the failure time estimation and maintenance scheduling for mechanical systems under different failure modes. The proposed approach considers the failure modes which are fatal to the system function and performance. Firstly, the fatal failure modes are identified and divided into two types: the degraded and functional failure modes. The former indicates the one that has an explicit detectable degradation process till the predefined failure threshold is exceeded, while the latter indicates the failure modes that occur suddenly. For both types of failure modes, approaches of failure time estimation are presented to provide the actual health status information for maintenance scheduling. Afterwards, the maintenance scheduling problem is formulated on the estimated failure time distributions of the multiple failure modes. The integer-constrained genetic algorithm (GA) is adopted to optimize the preventive maintenance scheduling by minimizing the life-cycle cost rate. The optimal PM scheduling is derived based on the following: (1) when to perform the intermediate and major maintenance and (2) which failure mode should be maintained at an intermediate maintenance epoch. Finally, the performance of the proposed approach is validated through a real case of the ram subsystem of a boring machine.

Non-periodic preventive maintenance with reliability thresholds for complex repairable systems

In general, a non-periodic condition-based PM policy with different condition variables is often more effective than a periodic age-based policy for deteriorating complex repairable systems. In this study, system reliability is estimated and used as the condition variable, and three reliability-based PM models are then developed with consideration of different scenarios which can assist in evaluating the maintenance cost for each scenario. The proposed approach provides the optimal reliability thresholds and PM schedules in advance by which the system availability and quality can be ensured and the organizational resources can be well prepared and managed. The results of the sensitivity anlysis indicate that PM activities performed at a high reliability threshold can not only significantly improve the system availability but also efficiently extend the system lifetime, although such a PM strategy is more costly than that for a low reliabiltiy threshold. The optimal reliability threshold increases along with the number of PM activities to prevent future breakdowns caused by severe deterioration, and thus substantially reduces repair costs.

Association Roundtable

Insights and advice from leaders of local and state biomedical associations“Assure that your PM has purpose and is a benefit. Ask yourself, ‘Are you making a difference and does your data support having the device included in your PM program? Make sure your time is used wisely. If the equipment is under a third-party contract, denote it as such and match your CMMS [computerized maintenance management system] PM schedule to your providers. Your pass-off is a confirmation that the vendor performed the service outlined in your contract. You have a PM window in which the inspection can be performed. Use that window, start your schedule at the beginning of the cycle and not toward the end.”Scott James, Board Member, Intermountain Clinical Instrumentation Society“I believe that the most important tip about an effective PM of any medical device is to be very thorough. It is crucial to read OEM [original equipment manufacturer] directions carefully without skipping steps, and observing any information on the device's screen. Otherwise, a small mistake can result in a large, unintended consequence. It is better to perform PMs correctly and accurately the first time around so that follow-ups are not needed.”Lanita Hill, President, Indiana Biomedical Society“It's not easy—maybe not possible—to find a single tip that would be considered the most important. Obviously, we need to perform a thorough and timely inspection that follows the procedure adopted by the hospital and/or the manufacturer's recommendations. The process must be documented and applicable labeling applied. Have a conversation with the end user to determine if there have been any operational concerns. Also, review the device history since its last maintenance. Whew, that's quite a long ‘single most important tip.’ I reached out to some CMIA members for additional ideas. I was rewarded with this gem: ‘Ensure the device's safety, operation, and cleanliness like it would be used on those most dear to you.’ That's a great tip, not only for preventive maintenance, but all facets of our job. It shows a grounded perspective about the importance of our contribution to healthcare technology.”Fred Yoffe, 2013 Chair, CMIA Board of Directors, California Medical Instrumentation Association (CMIA)“Find out what things fail most often on the units. Make sure you replace those common failure items as this will save the clinicians from having downtime. Make sure you have parts on hand for the common failures.”Russ Magoon, President, Oregon Biomedical Association

Caracterización de la gestión del mantenimiento de equipo biomédico en servicios de urgencia de clínicas y hospitales de Medellín en el período 2008-2009

Maintenance management is a tool that supports Biomedical Engineering staff in the development, control and direction of maintenance programs for medical equipment. Objective: to characterize the maintenance management of biomedical equipment in emergency areas of medical institutions in the metropolitan area of Medellin during 2008-2009. Materials and methods: a survey was conducted with chiefs and maintenance technicians of eleven institutions classified at level 3, belonging to the health network of the municipality of Medellin. Results: among the medical institutions included in the present study, it was found that 80% of equipment failure was related with mishandling of the devices, and 70% of maintenance work developed during the period of study was carried out by qualified Biomedical Technicians/ Technologist. On the other hand, a weakness was observed in programs for medical equipment maintenance of the surveyed institutions, as it was found that most of them had only working PM schedules, and outdated record information of equipment regarding, maintenance, acquisition or decommissioning plans of equipment. Conclusion: the study shows the existence of weaknesses in certain areas of the maintenance, such also poor organization and management, and poor availability of original spare parts.

Study on the Current Application Situation and Development of E-Portfolio Based on ICT for Mechanical Components Using Genetic Algorithms

The maintenance cost and the extended life of the system under any activities-combination, which represents what kind of activities taken for these chosen components, are analyzed for evaluating the unit-cost life of the system. The optimal activities-combination at each PM stage is decided by using genetic algorithm in maximizing the system unit-cost life. Repeatedly, the PM scheduling is progressed to the next stage until the system's unit-cost life is less than its discarded life. Appropriately a mechatronic system is used as an example to demonstrate the proposed algorithm. This paper uses E-Portfolio based on ICT for mechanical components using genetic algorithms and content analysis to study present application situation of E-portfolio in China, and scientifically forecast the trend of the development of E-Portfolio, which is conducive to grasp the research of E-Portfolio and discover some problems existing in the process.

Lot sizing, preventive maintenance, and warranty decisions for imperfect production systems

PurposeSets out to develop an integrated model that considers simultaneously inventory production decisions, PM schedule, and warranty policy for a deteriorating system that experiences shifts to an out of control state. The time to shift follows a general probability distribution with increasing hazard rate, so that time‐based PM is effective in improving the system reliability.Design/methodology/approachA profit function is used to model the production system. Optimization techniques are used to generate optimal solutions for the problem. Although global optimality cannot be guaranteed, empirical results show that global optimal solutions are obtained.FindingsThe integrated model provides decisions on inventory levels, production run length, and PM schedule simultaneously. It is illustrated through numerical examples that investment in PM can lead to savings in warranty claims for repairable products. As a result, the overall profit per unit, in certain cases, is higher with PM than without PM.Research limitations/implicationsThe production system is taken, numerical examples are presented and a sensitivity analysis is conducted to gain more insight into the developed model. In particular, the numerical analysis shows that a better PM program reduces warranty claims.Practical implicationsIn addition to the joint optimization of production/inventory decisions and PM schedule, such models can be very useful in making resource allocation decisions between warranty and PM programs. It is clear from the numerical analysis that a better PM program reduces warranty claims.Originality/valueThe paper provides a joint optimization of production inventory decisions and the PM schedule for a system subject to a time to shift that follows a general probability distribution. Previous research considered only an exponential distribution and did not consider PM.

Application of RCM for safety considerations in a steel plant

Abstract Any operation or process done on machine or its parts to enhance the efficiency of machine before or after the breakdown is called maintenance. A concern may be said to be successful over the years, when it runs non-interrupted, maintains a smooth production flow consistently and at optimum productivity levels. Plant can achieve productivity up to a satisfactory level by proper maintenance work. The maintenance system may be categorized as either ‘Planned’ or ‘Unplanned‘. The classic maintenance problems in modern industries are insufficient pro-active maintenance, frequent problem repetition, erroneous maintenance work, sound maintenance practices not institutionalized, unnecessary and conservative PM, sketchy rationale for PM actions, lack of traceability/visibility for maintenance program, blind acceptance of OEM inputs, PM variability between like/similar units, paucity of predictive maintenance applications. Hence it was necessary to develop an appropriate methodology to develop strategies and programmatic approach to deal with such problems. The reliability centered maintenance offers the most systematic and efficient process to address an overall programmatic approach to the optimization of plant and equipment maintenance In this paper, the concept of RCM has been applied to process of vacuum degassing/vacuum oxygen decarburising (VD/VOD) in steel melting shop of a medium scale steel industry. Safety consideration is the significant aspect for selection of the system. By systematically applying the RCM methodology, failures, failure modes are analyzed. To preserve the system function, preventive maintenance tasks such as inspection/checking, lubrication, cleaning, adjustment, replacement, are allotted for various failure modes. RCM based preventive maintenance schedule for the system is formulated and compared with the company's existing preventive maintenance schedule. For subsystems such as ejectors, water-ring pumps and the RLC/ladle car/furnace, existing maintenance schedule is conservative. For oxygen lance and cooling pumps, maintenance frequency is to be increased to quarterly from yearly in the present schedule. For condensers bimonthly schedule is recommended as compared to none, whereas for cooling pumps the schedule remains unchanged. This reveals that RCM based tasks need not necessarily increase the frequency but can, retain or even decrease the frequency of maintenance based on functional priorities. RCM can also recommend the additional maintenance tasks. Application of RCM based PM schedule requires use of age exploration technique and attitudinal changes.

Reduced chlorhexidine tooth stain coverage by sequential administration of monoperoxyphthalic acid in the beagle dog.

Extrinsic tooth stain is sometimes a side-effect observed with the chronic use of chlorhexidine-containing oral care products. This chlorhexidine (CHX)-induced tooth staining is observed both in humans as well as in experimental animals. The present study tested the hypothesis that the sequential administration of monoperoxyphthalic acid (MPA) will reduce the development of chlorhexidine-induced tooth stain coverage in a beagle dog. For this study, dogs were treated with 30 mL of mouthrinse b.i.d. for 28 days. The following treatment groups ( = 12 dogs/group) were tested: water (negative control), 1.0% MPA rinse, 0.12% CHX rinse, and 1.0% MPA rinse followed by 0.12% CHX rinse. The sequential dosing of 1.0% MPA followed by 0.12+ CHX resulted in significantly (p < or = 0.05) less tooth stain than when dogs were dosed with 0.12% CHX alone. Additionally, the sequential dosing of MPA followed by chlorhexidine resulted in a 75% reduction in plaque formation in this model, which was significantly different (p < or = 0.05) from results with either treatment alone. A further study demonstrated that similar results could be obtained when MPA plus CHX treatment alone. A further study demonstrated that similar results could be obtained when MPA plus CHX treatments were separated by approximately a five-hour period in an AM and PM schedule. We conclude that the oxidizer, MPA, can significantly reduce tooth stain induced by CHX while enhancing its antiplaque and antigingivitis activity.

Focus on: Multicare Medical Center Biomedical Engineering Department.

This paper describes the Biomedical Engineering Department of Multicare Medical Center (Tacoma, WA), a multiple hospital group consisting of Tacoma General Hospital, Mary Bridge Children's Hospital, and Doctors Pavilion. The Department maintains over 4300 separate medical devices, with a replacement value in excess of $20,000,000, and has written its own software program for inventory control and PM scheduling. In addition to the standard tasks, this Department maintains the CT, Rolm VL PBX, anesthesia gas machines, and all of the equipment in Diagnostic Radiology, and performs environmental gas monitoring.