Design and implementation of a maintenance planning and control system

The appropriate maintenance of medical devices, including performance inspections and preventive maintenance, is fundamental in mitigating clinical risk caused by adverse events in health care. Although several models for managing and planning preventive maintenance have been developed, the problem is lacking in standard methodology and still presents an open challenge for today's health experts. This paper aims to provide and develop methodology together with support systems able to assist decision makers in constructing preventive maintenance and performance inspection plans, taking into account both the technical and economic needs of hospital clinical engineering departments. Interventions by decision makers are of crucial importance within complex situations where large numbers, types of devices and different contractual situations are involved. SISMA system has achieved optimal results with minimum expense and maximum security for patients and technicians at the University Hospital of Florence where it has been applied in actual case studies.

PurposeThis study focuses on the application of reliability-centered maintenance (RCM) to a textile industry steam boiler. The study aims to demonstrate the development and application of RCM to a steam boiler used in the textile industry.Design/methodology/approachRCM is a structured process that develops maintenance activities needed on physical resources in their operational environment to realize their inherent reliability by logically incorporating an appropriate mixture of reactive, preventive, condition-based and proactive maintenance methods. A detailed analysis of the RCM approach is presented to develop preventive maintenance (PM) program and improve the reliability and availability of the steam boiler system.FindingsThe research reveals that the identification of PM tasks is a good indicator of the PM program's efficiency and can serve as an important maintenance-related downtime source. It is also discovered that the majority of maintenance programs that claim to be proactive are, in fact, reactive. This article also shows how RCM may be successfully implemented to any system, resulting in increased system reliability.Research limitations/implicationsThe paper focuses on a pilot study of the development and implementation of the RCM technique to a textile industry steam boiler. It is suggested that the developed RCM model can be applied to the entire plant.Originality/valueThe paper presents a comprehensive RCM model framework as well as an RCM decision framework, providing maintenance managers and engineers with a step-by-step approach to RCM implementation. The proposed framework is significant in that it may be utilized for both qualitative and quantitative analysis at the same time.

In view of the lack of consideration of equipment maintenance in the production scheduling of prefabricated components, a production scheduling optimization problem is proposed to meet the needs of prefabricated component production planning and preventive maintenance planning. A joint optimization model of aperiodic preventive maintenance production scheduling is established by considering the production process characteristics of prefabricated components and reliability-centered maintenance. The model is solved with the goal of minimizing the total cost, and finally the optimal production scheduling plan and preventive maintenance plan can be determined. The results of numerical analysis show the feasibility and effectiveness of the optimization model, and it is significantly better than the periodic preventive maintenance production scheduling model.

In oil and gas applications, the careful planning and execution of preventive maintenance is important due to the high costs associated with shutdown of critical equipment. Optimization and lifetime management for equipment such as gas turbines is therefore crucial in order to achieve high availability and reliability. In this paper, a novel condition-based gas turbine maintenance strategy is described and evaluated. Using custom-made gas turbine maintenance planning software, maintenance is repeatedly reoptimized to fit into the time intervals where production losses are least costly and result in the lowest possible impact. The strategy focuses on accurate online lifetime estimates for gas turbine components, where algorithms predicting future maintenance requirements are used to produce maintenance deadlines. This ensures that the gas turbines are maintained in accordance with the conditions on site. To show the feasibility and economic effects of a customer-adapted maintenance planning process, the maintenance plan for a gas turbine used in a real-world scenario is optimized using a combinatorial optimization algorithm and input from gas turbine operation data, maintenance schedules and operator requirements. The approach was validated through the inspection of a reference gas turbine after a predetermined time interval. It is shown that savings may be substantial compared to a traditional preventive maintenance plan. In the evaluation, typical cost reductions range from 25 to 65%. The calculated availability increase in practice is estimated to range from 0.5 to 1%. In addition, downtime reductions of approximately 12% are expected, due solely to improved planning. This indicates significant improvements.

Energy efficiency improvement and timely preventive maintenance (PM) are critical in manufacturing industry due to the rising energy cost, environmental concerns, and increasing requirements on system reliability. By strategically turning appropriate machines in down state, the corresponding energy consumption can be reduced, and at the meantime, the necessary PM works can be carried out to increase PM completion rate and reduce potential extra expense on PM during nonproduction shifts. However, there is usually a tradeoff between time dedicated to production and time available for energy saving and PM. In this paper, a systematic method is developed to identify opportunity windows (OWs) during which certain machines can be shut down to save energy and PM tasks can be performed while maintaining a desired production throughput. The method is based on stochastic serial production lines and real-time production data. A profit function is formulated to illustrate the tradeoff between energy cost savings and potential throughput loss. The profit function is used to justify the cost savings by utilizing the proposed OWs during production operation.

The aims of this research are to improve the preventive maintenance plan and to prepare the autonomous maintenance plan of an automatic labeling system for a carbonated soft drink industry as a case study. The labeling system of the case study has experienced the wastes in maintenance. According to the maintenance problem analysis, the main causes are lack of or inadequate preventive maintenance program, inadequate work order monitoring system, and inadequate staff skills (problem-solving skills of maintenance) to perform work. Therefore, in order to reduce the number and length of machine breakdowns and the seriousness of breakdowns, the preventive maintenance program is used instead of breakdown and routine maintenance by which maintenance skills are required, and in order to improve how machines are operated and handled by improving the skills, and the autonomous maintenance manual is set up to prepare for autonomous maintenance. After the improved preventive maintenance plan has been developed and then implemented, it was found that machine downtime reduced by 1.35% and machine availability increased by 1.74%.

PurposeThe purpose of this paper is to develop a model that permits more rational planning for preventive maintenance, by controlling failures in the specific context of equipment breakdown. Thus not only the cost and reliability parameters are dealt with, but also the peculiarities of different contexts in which maintenance activities occur. Furthermore, it aims to include Bayesian methodology in the procedure to overcome main difficulties in failure data.Design/methodology/approachA multi‐criteria decision‐aiding model capable of overcoming the two main difficulties related to preventive maintenance: establishing a replacement periodicity based on more than one criterion, and the ability to provide a solution in uncertainties situations, has been developed from adaptation of classical models. This model also uses Bayesian elements to address uncertainties during equipment failures.FindingsThe paper finds that in a preventive maintenance planning, as a multi‐criteria decision problem, different types of uncertainties may be identified, which may be categorized as external uncertainties and internal uncertainties. In the proposed model a division of procedures has been established, dealing with external uncertainties first, then the internal uncertainties related to the structure of the problem and analysis of the decision are addressed using the multi‐criteria decision‐making method PROMETHEE III that allows the amplification of the notion of indifference. In this way, a suitable structure to connect two types of uncertainties was structured.Practical implicationsThe model will assist the decision‐maker in preventive maintenance planning to take uncertainties into account, seeing the alternatives that are closest, through an amplification of the notion of indifference provided by PROMETHEE III. Furthermore, in practice the proposed model have an impact on maintenance cost and reliability of production plant.Originality/valueThis paper proposes a multi‐criteria decision‐aiding model capable of overcoming the two main difficulties related to preventive maintenance: establishing a replacement periodicity based on more than one criterion; and the ability to provide a solution even when failure data are unavailable or incomplete.

In semiconductor manufacturing, preventive maintenance (PM) activities are typically scheduled via a two tier hierarchical decomposition approach. The first decision tier determines a PM cycle plan while the second tier schedules these planned events into the manufacturing operations. Following recent work based on the use of G/G/m queueing approximations for PM planning, we develop a method to allow for multiple PM cycles in a tool set. We formulate a nonlinear program with the PM cycle durations as continuous decision variables with the objective of minimizing the mean cycle time. We examine certain special cases and characterize the optimal solutions. Numerical studies are conducted with realistic multiple PM cycle data to assess the implications of the proposed approach. The results suggest that it may be possible to obtain significant improvements in the overall cycle time performance of tool sets in semiconductor manufacturing relative to existing PM planning procedures.

An expert-system-based machine monitoring and maintenance management system

An Efficient Jaya Algorithm for Joint Optimization of Preventive Maintenance and Quality Policy in Production Systems

High operational costs and downtime risks can affect industry efficiency making equipment maintenance optimization an urgent need. PT. IAA AMO, as a manufacturing industry in the field of aircraft maintenance, faces significant challenges with the low reliability of Ground Power Unit (GPU) engines which only reaches 86%. This has led to an increase in the company's operating costs. The purpose of this study is to optimize Preventive maintenance on the Ground Power Unit (GPU) in aircraft maintenance at PT. IAA AMO using the Failure Mode and Effect Analysis (FMEA) method. The results show that the MTTF of the GPU is 311 hours, while the MTBF is 373 hours. To prevent sudden breakdowns, preventive maintenance is recommended every 311 hours for non-repairable components and 373 hours for repairable components. This research offers a more effective maintenance strategy to improve GPU reliability and lower operational costs. This research results in more effective maintenance planning, which not only minimizes downtime and operational costs, but also improves the overall reliability of the GPU machine. With the implementation of FMEA methods and the right preventive maintenance strategy, companies can anticipate breakdowns early, extend engine life, and ensure smooth operations in support of aircraft maintenance.

8 - Implementation—Carrying RCM to the Floor

Equipment maintenance is momentous for improving production efficiency, how to integrate maintenance into production to address uncertain problems has attracted considerable attention. This paper addresses a novel approach for integrating preventive maintenance (PM) into production planning of a complex manufacturing system based on availability and cost. The proposed approach relies on two phases: firstly, this study predicts required capacity of each machine through extreme learning machine algorithm. Based on analyzing historical data, the opportunistic periods are calculated for implementing PM tasks to have less impact on production and obtain the PM interval and duration. Secondly, this study obtains the scheduling planning and the least number of maintenance personnel through an improved ant colony optimization algorithm. Finally, the feasibility and benefits of this approach are investigated based on empirical study by using historical data from real manufacturing execution system and equipment maintenance system. Experimental results demonstrate the effectiveness of proposed approach, reduce personnel number while guarantee the maintenance tasks. Therefore, the proposed approach is beneficial to improve the company’s production efficiency.

An integrated single-machine group scheduling model is proposed, which incorporates both learning and forgetting effects and preventive maintenance (PM) planning. The objective is to minimise the expected makespan by optimising job sequence and PM decisions. This model contains sequence-dependent set-up time, actual processing time, planned PM time and expected minimal repair time simultaneously. Based on the properties of group production, three learning functions under different circumstances are proposed to deduce the variable processing time of each part, considering the learning effect when consecutively producing identical or similar parts, together with the forgetting effect when transferring jobs interrupts the production process and makes retrogress in learning. Both run-based maintenance and minimal repair policies are specified to handle the uncertainty of machine breakdowns. The search algorithm for the model is developed, and the numerical example is studied. The computational results and sensitivity analysis show that this improved group scheduling model can well balance the machine resource requirements from different practical manufacturing-related activities.

Maintenance is performed to restore a system's inherent reliability. However, reliability is not enough: a maintenance task must “pay for itself‘ in dollars or readiness. This applies to all types of maintenance: corrective, preventive, and alterative. We can reduce corrective maintenance costs through preventive and alterative maintenance. We can reduce preventive maintenance costs by eliminating inapplicable and ineffecitve tasks. We must weigh alterative maintenance costs against the value of improved readiness and cost‐avoidance of future corrective maintenance.The U. S. Navy is improving alterative maintenance by engineering for reduced maintenance (ERM). This process corrects the root cause of some high‐cost items by replacing the system, component, or coating with an improved design or material. It overcomes the limit of preventive maintenance: higher levels of inherent reliability require design improvement.The U. S. Navy is improving preventive maintenance through condition‐based maintenance (CBM). The Navy's CBM initiative encompasses three parallel efforts: rules (improving maintenance requirements and plans), tools (using computer and diagnostic technology to facilitate maintenance decision‐making), and schools (educating all levels of maintenance decision‐makers in reliability and condition‐based maintenance engineering principles).This paper addresses how the U. S. Navy surface ship community plans to avoid inapplicable and ineffective maintenance and reduce maintenance costs without sacrificing reliability.