Smart Maintenance Planning For Distribution Transformers Using Discrete-time Markov Chain

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.

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.

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.

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

The background of this research is an analysis of equipment disturbances that occur in distributing electrical energy. The disturbance is a blackout. Power outages cause a decrease in the continuity of service to customers and result in a lot of loss of electrical energy that is not distributed to customers. The research objectives are to find out the optimization technique of distribution network maintenance, how to reduce the value of energy not supplied (ENS), find out the losses due to ENS, and find out the total electrical energy distributed after preventive maintenance is carried out. This research uses a preventive maintenance method, which is maintenance that can prevent unexpected damage to distribution system networks and equipment. This research optimized preventive maintenance actions by reducing disturbances after preventive maintenance from 7 disturbances in February to 2 disturbances in March. Before preventive maintenance, the loss due to ENS amounted to Rp. 36,192,017.626, and the total electrical energy not distributed was 25,025.76 kWh. After preventive maintenance, there was a significant decrease in the total loss due to ENS of Rp. 953,516,447, and the amount of electrical energy not distributed was 659.28 kWh. This research can increase maintenance efficiency due to electrical energy losses by 97.36% at the time interval when this research data is managed when data collection is carried out in the field.

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

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.

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.

The purpose of this study is to determine the optimization of machine maintenance which is applied by the company (companies), the problems that arise in the maintenance system, and the cost of both preventive maintenance and subsequent corrective maintenance activities, to determine the alternatives that is most optimal. The case study will be conducted in the PT Great Giant Pineapple by focusing the research on Ridger Palir machine. The selection will done in this machine, that is to say, considering terms of maintenance, and the high price of the spare parts. Based on the research results that have been done on PT Great Giant Pineapple, the engine maintenance activities, the general implementation of the engine maintenance, shows the company has been running pretty well, but not yet optimal. Out of the problems found in these, the author tried to find a solution by taking into account the cost efficient in carrying out maintenance and engine solutions to the problem of spare part procurement. Preventive maintenance system may be optimal for the efficiency of the company if the determination of the machine preventative maintenance period is predominant. Machine preventative maintenance period can be optimized with the average, that is, the average treatment which cab conducted once every 6 months. Keywords: maintenance, preventive maintenance, corrective maintenance

An oil and gas company has 2,268 oil and gas wells. Well Barrier Element (WBE) is installed in a well to protect human, prevent asset damage and minimize harm to the environment. The primary WBE component is Surface Controlled Subsurface Safety Valve (SCSSV). The secondary WBE component is Christmas Tree Valves that consist of four valves i.e. Lower Master Valve (LMV), Upper Master Valve (UMV), Swab Valve (SV) and Wing Valve (WV). Current practice on WBE Preventive Maintenance (PM) program is conducted by considering the suggested schedule as stated on manual. Corrective Maintenance (CM) program is conducted when the component fails unexpectedly. Both PM and CM need cost and may cause production loss. This paper attempts to analyze the failure data and reliability based on historical data. Optimal PM interval is determined in order to minimize the total cost of maintenance per unit time. The optimal PM interval for SCSSV is 730 days, LMV is 985 days, UMV is 910 days, SV is 900 days and WV is 780 days. In average of all components, the cost reduction by implementing the suggested interval is 52%, while the reliability is improved by 4% and the availability is increased by 5%.

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.

Equipment maintenance and system reliability are important factors affecting the organisation’s ability to provide quality and timely services to customer. While maintenance remains an important function to manufacturing, it is only recently that attempts have been made to quantify its impact on equipment performance. In this research, an approach of linking maintenance with equipment performance is developed using simulation modelling. The modelling approach involves defining probabilistic models and assumptions affecting system performance, such as: the probabilistic model for the initial failure rate/intensity of the equipment; the probabilistic model for the system deterioration and corresponding effect; the probabilistic model for the random times of corrective maintenance (CM) and preventive maintenance (PM) that takes into the account the types of maintenance plans/policies and the potential dependency between CM and PM times; and the probabilistic model for the random effects of CM and PM on the reliability of the equipment. Using a continuous manufacturing equipment, the model is used to analyse the impact of deterioration, failures and maintenance (policies, timing and efficiency) on equipment performance. It is shown that modelling the effect maintenance provides a basis of evaluating maintenance efforts with the potential application in performance evaluation and decision support while investigating opportunities for manufacturing equipment performance improvement.

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.

Nowadays, almost every firm focuses to beat the global competition across the worldwide. In order to deal with such situation, companies are undertaking efforts to improve the productivity of their products but at the minimum possible cost. Asset management is one of the ways to enhance the productivity under cost constraint which may also be seen as the management strategy for different the phases of asset life cycle. Operations and maintenance is one of the important phases of asset life cycle that can be focussed to improve the productivity. This phase may extend the equipment life, improves availability and retains them in healthy positions. But at the same time, frequent maintenance actions may increase the maintenance cost thereby increase the life cycle cost of a product. The maintenance cost only includes the preventive and corrective maintenance cost and which may in-turn depend upon the scheduled maintenance interval. Thus, a trade-off between maintenance actions and operational objectives (i.e. availability, etc.) is required to minimize the maintenance cost. In this paper, the genetic algorithm is applied to optimize the maintenance cost for higher performance (i.e. availability). A case study is taken into consideration for implementing the GA to optimize the objective function. The three different cases are presented, in the first case, subassemblies are repaired during maintenance action(s); in the second case subassemblies are repaired in preventive maintenance action and while replaced in corrective maintenance action; in the last case, the subassemblies are replaced in both kind of maintenance. In order to check the robustness of the solution, the sensitivity analysis is also performs and that validates the strength of the solution methodology.

Maintenance Support for a weapon system involves both preventive and corrective maintenance actions. Preventive maintenance is normally associated with mechanical equipment, and requires prognostic maintenance techniques to predict failures before they occur. Corrective maintenance is more closely associated with electronic equipment, and requires diagnostic maintenance techniques to detect and isolate failures after they occur. To pursue a systems approach to weapon system maintenance using both prognostics and diagnostics, the Air Force has initiated a program entitled, ``Generic Integrated Maintenance Diagnostics (GIMADs)''. This paper provides insight into the GIMADS program at Aeronautical Systems Division.