A Study on the Key Performance Indicators for Biomedical Equipment Management

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 relation between technical debt and corrective maintenance in PHP web applications

The need for renewable energy has led to a fast increase of number of the wind turbines constructed each year. To monitor wind turbines farms, operating and maintenance managers need new effective and automatic tools compatible with a large number of wind turbines. This monitoring task is usually completed by Condition Monitoring System, but researches have been conducted on the utilization of SCADA (Supervisory Control And Data Acquisition) data for condition and predictive maintenance. This paper explains the difficulty of using this new source of information, and introduces the different techniques presented in the literature form 2001 up to 2014. Two classes of approaches can be identified: “internal” approaches using only data from one turbine, and “external” approaches relying on the comparison of one turbine to the other within the same farm. Both approaches have different pros and cons: internal approaches make use of the link between the components in the same turbine and so reduce the influence of operating conditions on fault indicators; external approaches make use of the correlation between SCADA variables measured on different turbines and can thus reduce the influence of wind conditions on the fault indicators. This paper sums up the latest available techniques and it shows that new areas of research can be explored with SCADA data. The obtained fault indicators still remain sensitive to the operating conditions and stochastic variations of the wind load. Combining advantages of the two approaches could reduce both influences.

Questionnaires sent to maintenance managers is a frequently used approach to collect data on software maintenance. This paper reports findings from two studies, carried out at a large Norwegian maintenance organisation, investigating the quality of questionnaire based software maintenance studies.Interesting findings were, among others, that:- The definition of essential terms, for example of 'software maintenance', at the beginning of a questionnaire did not assure a consistent use of the terms by the questionnaire respondents.- Manager estimates of the proportion of effort spent on corrective maintenance were biased when based on best guesses instead of good data. For this reason, the frequently referred studies of Lientz and Swanson (1980) and Nosek and Palvia (1990) may have reported a too high proportion of effort spent on corrective maintenance.

Health institutions have vital departments such as engineering central hospital, in this paper highlights the main aspects to be evaluated as indicators of quality and others. Taking parameters from different authors that highlight the quality that is provided when using this type of tool, these results allow the analysis of the performance of each of the officers and of the processes developed in the institutions. From consolidated generated in periods established proceed to take actions to improve that software through the respective monitoring is performed and evaluated. The above processes are executed in software maintenance, which allows the creation of benchmarks such as the indicator man-hours, schedule preventive maintenance, corrective maintenance and indicator of requests made, discussed generating monthly reports and statistics for the administration department.

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.

The Roundup

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.

Maintenance management has become one of the most important functions in an organisation. The main objective of maintenance is to keep machine resources running by increasing the availability through repair activities as in corrective maintenance or some proactive activities as in preventive maintenance. According to the literature, maintenance management is explored in several areas. A comprehensive review of some researches in the area of maintenance and quality is presented in this article.

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.

Basically, scheduling concept for mixture policy of preventive with corrective maintenance is scheduled under the least assigned workers whose have high utilization of their maintenance responsibilities. Whereas, scheduling algorithm will be exploited and applied for determining the cyclic shift work and workers scheduling in according to entirely response to the activities of preventive and corrective maintenance. Additionally, every worker will have similar of the amount day-work and day-off with the equivalency vacancies on their weekend. The thinking on the basis of probabilistic failure rate concept is the main objective of this research study, whilst the algorithm is thought on basis of mixture policy of preventive and corrective which is to be useful for all industries to do their maintenance actions.

Maintenance management in large companies is essential to ensure the continuous, safe and efficient operation of their complex systems and assets. The implementation of reliability cells plays a crucial role in this context. These cells are multidisciplinary teams dedicated to improving equipment reliability through data analysis, performance monitoring, predictive maintenance, and preventive actions. By adopting this approach, companies can proactively identify potential failures, minimize unplanned downtime, and extend asset life. Furthermore, reliability cells contribute to worker safety, reduced operating costs and increased market competitiveness, making them a crucial tool in the management of large companies. In this context, the implementation of the security cell aims to improve and solve problems in a simplified way, through strategic planning. Through data validation, performance, and disclosure with their pre-determined deadlines for each subsystem of the unit, corrective maintenance was minimized, and preventive and predictive maintenance were improved, generating maximum equipment availability.

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.

Optimizing energy efficiency in data center cooling towers is crucial for reducing operational costs and environmental impact. This review explores the integration of predictive maintenance and project management to achieve this goal. By leveraging predictive maintenance techniques, data center operators can anticipate and address potential issues before they lead to costly downtime or inefficiencies. Project management plays a key role in coordinating these efforts, ensuring that maintenance activities are carried out efficiently and effectively. Predictive maintenance relies on data analytics and machine learning algorithms to monitor the condition of cooling towers in real-time. By analyzing data such as temperature, pressure, and flow rates, these algorithms can detect anomalies and predict potential failures. This proactive approach allows data center operators to schedule maintenance activities during planned downtime, minimizing disruptions to operations. Project management practices, such as Agile or Waterfall methodologies, are essential for coordinating predictive maintenance efforts. Project managers oversee the planning, execution, and monitoring of maintenance activities, ensuring that they are completed on time and within budget. They also facilitate communication between stakeholders, including maintenance teams, data analysts, and management, to ensure that everyone is aligned and working towards the same goals. By integrating predictive maintenance with project management, data center operators can achieve significant improvements in energy efficiency. By addressing maintenance issues proactively, operators can reduce energy consumption, extend the lifespan of cooling equipment, and minimize downtime. Additionally, project management practices ensure that these efforts are coordinated and effective, maximizing the benefits of predictive maintenance. In conclusion, optimizing energy efficiency in data center cooling towers requires a holistic approach that combines predictive maintenance and project management. By leveraging predictive maintenance techniques and project management practices, data center operators can achieve significant improvements in energy efficiency, reduce operational costs, and enhance environmental sustainability.

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.