Modelling maintenance effects on manufacturing equipment performance: results from simulation analysis

In this chapter maintenance concept is defined and maintenance types are classified with regard to implementing maintenance policies toward preventive maintenance. Especially achieving planned maintenance policies toward preventive (planned) maintenance and condition based maintenance policies toward predictive maintenance is taken into consideration primarily. Maintenance concept is defined and maintenance types are classified. Due care is given for ‘Preventive Maintenance’ in this chapter. In general ‘Maintenance’ term could be defined as; The integration of all possible technical and administrative actions, including planning, supervising, monitoring and controlling toward retaining an item, a system, a machine to restore their original functional state in which they can perform the intended functions. In addition maintenance, include protective and corrective actions to keep the plant operational system in intended conditions or to maintain the acceptable manufacturing conditions. Optimum maintenance policies aims to sustain system reliability and robustness within minimum cost. In line with the progress of industry, increase in the system, material and manpower costs, increasing demand for robustness and the complex structure of the machines increases also the importance of maintenance policies. Maintenance types could be divided into two main parts namely; 1. Preventive Maintenance, 2. Corrective Maintenance. Preventive Maintenance is also classified into following sub groups; 1.a- Planned Maintenance, 1.b- Predictive Maintenance, 1.c- Advanced Maintenance Implementations; 1.c.1 Reliability Centered Maintenance 1.c.2 Risk Based Maintenance. All of these maintenance types elaborated with relevant figures in the chapter. In this chapter Comparison of Planned and Unplanned (corrective) Maintenance (With regard to transaction and output) is defined with a table. Additionally, Comparison of Planned and Unplanned (corrective) Maintenance (With regard to infra structure) is also tabulated. ‘Benefits of Preventive & Predictive Maintenance’ and ‘Predictive Maintenance Methods’ are defined with relevant descriptive figures in the chapter. For Corrective Maintenance, basic definitions and corrective maintenance steps, types of corrective Maintenance, improvement strategies in corrective maintenance effectiveness are also given. In the final part of ‘Results and conclusions’ expected and verified benefits of implementing maintenance policies for planned and predictive maintenance are explained. Comparisons in some maintenance policies is given.

Simultaneous control of production, preventive and corrective maintenance rates of a failure-prone manufacturing system

Two-stage maintenance of a production system with exponentially distributed On- and Off-periods

Although the healthcare technology management (HTM) community has decades of accumulated medical device-related maintenance data, little knowledge has been gleaned from these data. Finding and extracting such knowledge requires the use of the well-established, but admittedly somewhat foreign to HTM, application of inferential statistics. This article sought to provide a basic background on inferential statistics and describe a case study of their application, limitations, and proper interpretation. The research question associated with this case study involved examining the effects of ventilator preventive maintenance (PM) labor hours, age, and manufacturer on needed unscheduled corrective maintenance (CM) labor hours. The study sample included more than 21,000 combined PM inspections and CM work orders on 2,045 ventilators from 26 manufacturers during a five-year period (2012-16). A multiple regression analysis revealed that device age, manufacturer, and accumulated PM inspection labor hours all influenced the amount of CM labor significantly (P < 0.001). In essence, CM labor hours increased with increasing PM labor. However, and despite the statistical significance of these predictors, the regression analysis also indicated that ventilator age, manufacturer, and PM labor hours only explained approximately 16% of all variability in CM labor, with the remainder (84%) caused by other factors that were not included in the study. As such, the regression model obtained here is not suitable for predicting ventilator CM labor hours.

High reliability (and availability) with low life-cycle costs are general goals for all maintenance programs. An effective Preventative Maintenance (PM) regime balances the cost of maintenance with the elimination of degradation failure mechanisms through preemptive intervention. Failures in operation require corrective maintenance (CM) and are often the most expensive to repair. PM is conducted to reduce the probability of specific failures and hence reduce the CM burden and cost. However, PM actions can themselves introduce additional damage and failure mechanisms. Determining the optimum PM frequency requires these competing factors to be quantified. Too little PM allows sub-systems and components to wear out, decreasing overall system reliability. Conversely, too much PM will introduce an inordinate amount of damage (and failure mechanisms) that decreases system reliability, along with increasing the overall cost. In order to optimize maintenance, the failure modes of the individual components need to be analyzed and the maintenance program matched to how the equipment fails, how predictable the failure is and what the overall impact the failure has to the mission of the system it is a part of.

Shutting down a power plant for any reason is a costly process. Loss of production revenues from one unit can exceed millions of dollars per day. It is therefore good practice to keep outages as short and efficient as possible. A major component of planned outages is Preventive Maintenance (PM). It is important that PM be applied only where needed. Unnecessary PM is costly, not only in terms of labor and material costs, but in additional lost time and production. Further, there are cases where PM is contraindicated by standard reliability models, i.e. components with decreasing failure rates. This can paradoxically introduce failures, incurring additional repair costs and production loss. The risk management group at the South Texas Project Electric Generating Station (STPEGS) has successfully developed software to optimize PM schedules by minimizing total costs and risks associated with corrective maintenance (CM) and PM. The optimum PM interval is determined as a multiple of outage cycles. Components are evaluated based on maintenance type, risk ranking, cost of performing CM at power, and regulatory or internal commitments. This information provided to systems engineering assists in determining whether to advance PM from an upcoming outage, or defer PM to a subsequent outage. Components with maintenance that can be performed online are outside of the scope of this paper and are not considered here.

The opportunistic maintenance of a k-out-of-n:G system with imperfect preventive maintenance (PM) is studied in this paper, where partial failure is allowed. In many applications, the optimal maintenance actions for one component often depend on the states of the other components and system reliability requirements. Two new (τ, T) opportunistic maintenance models with the consideration of reliability requirements are proposed. In these two models, only minimal repairs are performed on failed components before time τ and the corrective maintenance (CM) of all failed components are combined with PM of all functioning but deteriorated components after τ; if the system survives to time T without perfect maintenance, it will be subject to PM at time T. Considering maintenance time, asymptotic system cost rate and availability are derived. The results obtained generalize and unify some previous research in this area. Application to aircraft engine maintenance is presented. © 2000 John Wiley & Sons;, Inc. Naval Research Logistics 47: 223–239, 2000

This paper shows the application of Discrete Event Simulation to study the effect of proposed maintenance strategies to the availability. The simulation study was conducted on a Surface Mount Technology (SMT) line. The model is built in the Discrete Event Simulation software WITNESS 14 to imitate this selected case study line to test out two proposed maintenance strategies. The first proposed strategy consists of Corrective Maintenance (CM) task only whereas the second proposed strategy is the introduction of Preventive Maintenance (PM) to the line. The effect of the two cases to the availability of each machine and system availability were verified with the simulation model to determine the most optimal solution. Although the simulation testing did not show improvement in availability for all station after the introduction of PM, the increment in system availability indicate that PM is the most optimal maintenance strategy for the SMT line.

The purpose of this study is to establish and maintain a program for monitoring quality indicators (QIs) for the improvement of the Biomedical Equipment Maintenance Management program at Christian Medical College (CMC) Vellore. Fault index (FI) indicators were applied to planned preventive maintenance (PPM)/corrective maintenance (CM) data. The findings were turned into meaningful reports to gain quantitative and qualitative insight into the maintenance program and equipment performance. This study was carried out in the Biomedical Engineering department of CMC Vellore, along with the Quality Management Cell in April 2020. This study was conducted on 2019 data on an experimental basis to find out the applicability and usefulness of selected QIs. Data were collected from the computerized maintenance management software (CMMS) called BMESOFT which was developed in-house in CMC Vellore. Reports were generated by applying the QIs. These reports were analyzed, and findings were used for improving the quality of maintenance activities. The FI indicator and analysis of critical biomedical equipment helped us to identify the repetitive faults and affected equipment. It was found that FI was high for ventilators and infusion pumps. The trend was repetitive in all quarters. This study helped us to identify the affected equipment, affected model, and affected location, and the ratio of PPM revealed that the annual average rate of completion of PPM work orders was 92.46% with respect to a set target of 90%. The Ratio of CM revealed that the annual average rate of completion of CM work orders was 90.86% with respect to a set target of 90%. To achieve a target of 95% completion, the maintenance activities need to be reviewed, and measures should be taken to improve the completion ratio. The factors affecting these ratios have been listed out and the corrective measures were planned to improve the same. This study is an attempt to apply FI as the QI to biomedical equipment maintenance data and confirm its applicability in improving the biomedical equipment management program. The experimental analysis of these QIs on 2019 data clearly showed that FI QIs are very effective in evaluating the performance of biomedical equipment management program and also find the root cause of repetitive faults.

Distribution systems are among the most complicated sectors of power systems. Unlike Generation and Transmission, most challenges in the distribution sector reflect the operational complexity rather than technological problems, and maintenance is crucial here. Transformers, vital equipment of every power system, require a fair amount of maintenance. Preventive-Maintenance is proven effective in improving the continuity of service and reliability of customers, but it is also proven costly. The complexity of maintenance modelling and optimization has forced utility companies to carryon with time-based maintenance with a lower performance in the executing process and results. In this article, we propose a comprehensive model to cover all possible states a transformer can experience, covering Preventive Maintenance (PM), the Corrective Maintenance (CM), and their required processes. Using discrete-time Markov-chain, we estimate the amount of time a transformer spends in each state, and therefore, calculate the required crews for each maintenance type. Using the calculated PM and CM crews, we define utility companies' cost function, reflecting the ENS (Energy not supplied), PM, and CM cost. Using this cost function and Genetic-Algorithm we try to allocate the PM and CM actions to minimize the total cost of utilities; this estimation can give Planners an upper hand in their cost-benefit and resource evaluation analysis.

The maintenance of any system can be categorized in two ways: failure based maintenance (corrective maintenance) and life based maintenance (preventive maintenance). The time interval at which the preventive maintenance could be scheduled is dependent on both the life distribution of the subsystems/components and the total cost involved in the maintenance activity. However, the corrective maintenance cannot be avoided when a random failure of a component occurs. The total cost of the maintenance depends on the number of components replaced during the entire operating period of the system and the respective cost involved in maintenance actions. In this paper the preventive maintenance schedule (t/sub 0/) is calculated taking the failure distribution of a component as Weibull. The variation in (t/sub 0/) with respect to the shape parameter (/spl beta/) and characteristic life (/spl alpha/) of the Weibull distribution was studied by the author for a given preventive maintenance cost and corrective maintenance cost. The ratio of the corrective maintenance cost to the preventive maintenance cost (R/sub cp/) has a significant effect on the maintenance schedule (t/sub 0/). Thus the variation of (t/sub 0/) with (R/sub cp/) is addressed in this paper. The ratio is varied between 10 and 1.5. It is found that the (t/sub 0/) value increases as shape parameter increases when the ratio is 10 whereas the (to) value decreases when the ratio is 1.5. This shows that as we decrease the cost ratio (R/sub cp/) the frequency of preventive maintenance decreases with shape parameter. This in turn implies that the frequency of the preventive maintenance schedule can be kept at minimum by bringing down the cost of corrective maintenance. Sensitivity analysis carried out on the (t/sub 0/) values indicates that the variation in the cost of maintenance is about 5 to 8 percent :in all the cases even when the value of (t/sub 0/) is increased. by 50% over the optimum value. This indicates, while the optimum value of (to) obtained may be of academic interest, one can 'in practice' choose a convenient value near the optimum without appreciably affecting the advantage in terms of cost.

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Jet engines are critical assets in aircraft, and their availability is crucial in the modern aircraft industry. Therefore, their maintenance scheduling is one of the major tasks an airline has to make during an engine’s lifetime. A proper engine maintenance schedule can significantly reduce maintenance costs without compromising the aircraft's reliability and safety. Different maintenance scheduling approaches have been used for jet engines, such as corrective, preventive, and predictive maintenance strategies. Regarding the safety demands in aircraft industries, preventive maintenance is a frequent maintenance method for jet engines. However, preventive maintenance schedules are often use fixed maintenance intervals, which is usually suboptimal. This paper focuses on minimizing a jet engine's overall maintenance cost by optimizing its preventive maintenance schedule based on an engine’s comprehensive reliability model. A hierarchical optimization framework including the golden section search and genetic algorithms is applied to find the optimal set of preventive maintenance number and their times and the components to be replaced at those times during the jet engine's overall lifetime. The Monte Carlo simulation is used to estimate the engine’s failure times using their lifetime distributions from the reliability model. The estimated failure times are then used to determine the engine's overall corrective and preventive maintenance costs during its lifetime. Finally, an optimal preventive maintenance schedule is proposed for an RB 211 jet engine using the presented method. In the end, comparing the proposed method's overall maintenance cost with two other maintenance methods demonstrates the proposed schedule's effectiveness. The method presented in this paper is generic, and it can be used for other similar engineering systems.

Renewal-geometric process is used to describe such a non-homogeneous deteriorating process that a system will deteriorate after several consecutive repairs, not after each repair described by the geometric process. In the maintenance domain, the effect of corrective maintenance after failure is generally not repairable as new (e.g. geometrically deteriorating). Preventive maintenance is critical before a system failure, due to economic losses and security threats caused by a sudden shutdown. Therefore, this article assumes that a system is geometrically deteriorating after corrective maintenance, wherein preventive maintenances sequence in the same repair period form a renewal process since it can restore the system to the initial state of the period. Furthermore, a binary policy [Formula: see text] is utilized to minimize the long-run average cost rate, where [Formula: see text] represents the number corrective maintenances and [Formula: see text] denotes the time interval between two consecutive preventive maintenances. In particular, pseudo-age replacement model represents a special case of [Formula: see text], which is considered as a generalization of the traditional age-based replacement model. Subsequently, the optimal policy [Formula: see text] can be verified in theory and an asymptotic optimal policy [Formula: see text] can be obtained based on a heuristic grid search. Finally, numerical examples verify the effectiveness of this proposed model and show that implementation of preventive maintenance for some repairable systems is superior to no preventive maintenance in both economic and reliability aspects.

The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.