Mean Time To Repair Research Articles

Developing a 3D decision-making grid based on failure modes and effects analysis with a case study in the steel industry

PurposeThe aim of this study is to develop a 3D model of decision- making grid (DMG) considering failure detection rate.Design/methodology/approachIn a comparison between DMG and failure modes and effects analysis (FMEA), severity has been assumed as time to repair and occurrence as the frequency of failure. Detection rate has been added as the third dimension of DMG. Nine months data of 21 equipment of casting unit of Mobarakeh Steel Company (MSC) has been analyzed. Then, appropriate condition monitoring (CM) techniques and maintenance tactics have been suggested. While in 2D DMG, CM is used when downtime is high and frequency is low; its application has been developed for other maintenance tactics in a 3D DMG.FindingsFindings indicate that the results obtained from the developed DMG are different from conventional grid results, and it is more capable in suggesting maintenance tactics according to the operating conditions of equipment.Research limitations/implicationsIn failure detection, the influence of CM techniques is different. In this paper, CM techniques have been suggested based on their maximum influence on failure detection.Originality/valueIn conventional DMG, failure detection rate is not included. The developed 3D DMG provides this advantage by considering a new axis of detection rate in addition to mean time to repair (MTTR) and failure frequency, and it enhances maintenance decision-making by simultaneous selection of suitable maintenance tactics and condition-monitoring techniques.

Rank order clustering and imperialist competitive optimization based cost and RAM analysis on different industrial sectors

The competitive global scenario faces the corresponding impacts such as difficulties in scheduling and loading, high tooling and equipment investment, enormous scrap availability, complex to control the quality, and extensive setup time. So, a higher level of connectivity is required in between the design and manufacturing activities to raise the profitability of the firms and enrich the product support design. In any manufacturing firm, poor reliability causes failure availability in all stages, namely, design, construction, planning, and maintenance, etc. In this research work, RAM is analyzed from the Weibull distribution based Mean time to repair (MTTR) and mean time to failure rates (MTTF) for the ten different industries. The measured RAM performances are optimized by the Imperialist competitive algorithm (ICA) by the application of the rank order clustering (ROC) method. The failure rates are clustered and ranked by using the rank order clustering method. Besides, the cost and RAM performance values are predicted by ICA and hybrid ROC method, and such a proposed algorithm is mathematically modeled in the Mat Lab platform. Hence from the evaluation, the proposed method scores better industrial performances than the actual and other implemented methods

Manajemen Perawatan Menggunakan Metode RCM Pada Mesin Produksi Kertas

One of the leading manufacturers in packaging paper in Indonesia has six production machines. To keep the production machine always in the best condition, then the role of maintenance is significant. One of the production machines, namely paper production machine 2 (PM2), has experienced increasing the number of breakdowns significantly, resulting in increased downtimes. Sudden failure causes a lengthy repair time and results in considerable production loss. The corrective maintenance method that is running at this time still not effectively reduce downtime. To minimize the number of downtimes, preventive maintenance is needed, and therefore the Reliability Centered Maintenance (RCM) method is selected. Besides applying quantitative analysis such as Mean Time To Repair (MTTR), Mean Time To Failure (MTTF), reliability, and preventive cost calculations. This method also conducts qualitative analysis such as Functional Block Diagrams (FBD), critical engine analysis, Fault Tree Analysis (FTA), and Failure and Mode Effect Analysis (FMEA). After implementing the preventive maintenance, there is an increase in reliability in the sub-system of the press section of the paper production machine 2 from only 43% to 56%, while the repair cost has been reduced by Rp 393,258,670 from Rp 5,724,825,736 to Rp 5,331,567,066 each time replacement of components in the press section. This decrease in costs will contribute to the profits earned by the company because it saves maintenance costs.

Performance of merging lines with uneven buffer capacity allocation: the effects of unreliability under different inventory-related costs

This simulation study investigates whether machine efficiency, mean time to failure (MTTF) and mean time to repair (MTTR) significantly affect the performance of uneven buffer capacity allocation patterns for merging lines. Also studied is the trade-off between increasing throughput via bigger buffers and their associated inventory-related costs, since previous studies have shown that higher overall buffer capacity and higher average inventory content result in higher throughput. Results suggest that an ascending buffer allocation pattern (concentrating buffer capacity towards the end of the line) produces higher throughput in shorter, more unreliable lines; whereas the balanced pattern shows better performance in longer, more reliable lines. Increasing average buffer capacity per station and/or having higher average buffer content was found to be more cost-effective in lines with lower machine inefficiency, shorter MTTF and MTTR, and longer lines. Results differed between reliable and unreliable lines since reliable lines were particularly penalised by buffer capacity investiment/maintenance costs due to a relatively low increase in throughput resulting from the addition of extra buffer capacity.

A Framework for Automatic Generation of Augmented Reality Maintenance & Repair Instructions based on Convolutional Neural Networks

In modern manufacturing world, MRO (Maintenance and Repair Operations) are the cornerstone for keeping industrial equipment in near-optimum condition. Successful completion of MRO has been benefited from Augmented Reality (AR), by considerably decreasing MTTR (Mean Time to Repair). To that end, AR delivers the digital tools that help on-the-field technicians to perform MRO easily and intuitively, however intense development is required for generating AR instructions. The latest advances in computer technologies, concretely in Convolutional Neural Networks, have enabled advanced computer vision. This research paper presents a framework for generating AR maintenance instructions, based on advanced computer vision and Convolutional Neural Networks (CNN). The applicability of the framework is tested in-vitro in a lab-based machine shop.

Implementación de la metodología RCM en máquina plisadora

The objective of this project is to reduce the downtime of a machine that performs the pleating of materials in a company dedicated to the manufacture of automotive parts. The development of this project aims to increase the availability of the machine and contribute to the use of material and humnan resources, achieving an increase in production to ensure permanence and competitiveness in the Market. Reliability Centered Maintenance (RCM) will be the methodology used to achieve the goal. The implementation of this methodology contributes to the updating of maintenance programs to achieve an increase of the MTBF indicator (Average Time Between Failures) and the decrease of the MTTR (Mean Time to Repair). The application of maintenance methodologies that exist today plays an important role within the industrial sector when it is necessary to meet the goals set by companies to meet the needs of customers.

Program Designing the Improvement of Excavator’s Maintenance Team Competencies (Case Study in a Sandstone Mine Company PT XYZ)

The heavy equipment maintenance system cannot be separated from the problems of Human Resources (HR). Some literature has defined HR as a success factor for the successful implementation of maintenance systems. The Asset Maintenance Division as an organization which responsible to maintain the Company's assets routinely, including the Doosan S500’s 50-tonne class excavator. But in fact, excavators that are currently still operated often breakdown and its physical condition are in a bad condition. From the observation of 9 (nine) units excavators, the Key Performance Indicator (KPI) target was still not achieved, especially on the items Mean Time To Repair (MTTR) and Mean Time Between Failure (MTBF). Mechanic’s technical competencies needed in achieving KPI are divided into four sub competencies: Maintenance, Repair & trouble shooting, Remove & install and Overhaul. This study aims: analyzing mechanic’s competencies against company‘s standards, analyzing the influence of mechanic’s competencies on achieving KPI targets, providing recommendations on the influence of mechanic’s competencies for achieving KPI targets. The primary data used in the study were obtained through interviews and the questionnaires as well as secondary data obtained from various literatures, research results and supporting books. The method of processing and analyzing data uses the method of gap analysis and the Non-Parametric Statistics (Multiple Regression). The results of the gap analysis are known that there are still gaps in the actual competence of employees with competency standards set by the company. Factors that cause the competency gap are generally known because of a lack of understanding of the basic skills required, mastery of work techniques and consistency in applying work quality standards. Recommendations from this study as an effort to improve the skills and knowledge of mechanics to improve work effectiveness that affects MTTR and the quality of work that affects MTBF so that the KPI are consistently achieved

Evaluation of Signal-in-Space Continuity and Availability for BeiDou Satellite Considering Failures

Signal-in-space (SIS) continuity and availability are important indicators of performance assessment for Global Navigation Satellite Systems (GNSSs). The BeiDou Navigation Satellite System (BDS) Open Service Performance Standard (BDS-OS-PS-1.0) has been released, and the corresponding public performance indicators have been provided, but the actual SIS performance is uncertain to users. SIS continuity and availability are primarily related to unscheduled outages (failures). Therefore, based on the existing failure classification system and actual operation modes, four types of failure modes are first analysed: long-term failure related to satellite service period, maintenance failure related to satellite manoeuvring, short-term failure associated with random repairable anomalies and equivalent failure corresponding to a combination of the above three types of failures. Second, based on the failure classification and selected precise and broadcast ephemerides from 2015–2016, the Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR) of each failure type are obtained using appropriate detection methods. Finally, using a corresponding assessment model, the SIS continuity and availability of BeiDou are calculated for individual and equivalent failure cases, and these are compared with the provided index in the BDS Open Service Performance Standard.

System availability assessment using a parametric Bayesian approach: a case study of balling drums

Assessment of system availability usually uses either an analytical (e.g., Markov/semi-Markov) or a simulation approach (e.g., Monte Carlo simulation-based). However, the former cannot handle complicated state changes and the latter is computationally expensive. Traditional Bayesian approaches may solve these problems; however, because of their computational difficulties, they are not widely applied. The recent proliferation of Markov Chain Monte Carlo (MCMC) approaches have led to the use of the Bayesian inference in a wide variety of fields. This study proposes a new approach to system availability assessment: a parametric Bayesian approach using MCMC, an approach that takes advantages of the analytical and simulation methods. By using this approach, mean time to failure (MTTF) and mean time to repair (MTTR) are treated as distributions instead of being “averaged”, which better reflects reality and compensates for the limitations of simulation data sample size. To demonstrate the approach, the paper considers a case study of a balling drum system in a mining company. In this system, MTTF and MTTR are determined in a Bayesian Weibull model and a Bayesian lognormal model respectively. The results show that the proposed approach can integrate the analytical and simulation methods to assess system availability and could be applied to other technical problems in asset management (e.g., other industries, other systems).

Effective Maintenance of Gas Turbine Power Plant to Improve Productivity

This research work identified strategies to effectively maintain the gas turbine power plant as to improve on productivity. The main aim is to determine the effect of a good maintenance culture on the reliability of gas turbine power plant operation, to evaluate the loss of revenue due to high rate of intervention (or failure) in the power plant. Materials were collected from the manufacturers manual, maintenance report log sheet, production report log book/sheets, intervention schedule programmes, mechanical vibration analyzer machines, machine lubrication schedule/plan, Instrumentation calibration manuals and plants simulation model log books/sheets, human machine interface system (HMI) as well as the equipment control and monitoring machine (ECM), self administered questionnaire and internet system. The collated data were analyzed with the aid of number of intervention, mean time between failures (MTBF), mean time to repair (MTTR), production yield, loss of revenue equations as well as responses from returned questionnaires to determine the effect of adequate maintenance of the power plant on productivity. The analysis revealed a negative growth rate in productivity resulting from poor performance of the gas turbine power plant in the year (2013-2017) due to poor maintenance culture. There has been colossal loss of revenue due to incessant shutdown of power plant which also affects the process plants. From the result of five years average yield of Notore historical products cost per day for two major products namely; ammonia and urea amounted to N0.1104B. In other words, for one production year, revenue of N36.4 Billion would have been received but due to high plant down-time for maintenance intervention, the company lost about N0.851Billion in a year. This is a huge loss to Notore Chemical Industries Plc.

Effective Maintenance of Gas Turbine Power Plant to Improve Productivity

This research work identified strategies to effectively maintain the gas turbine power plant as to improve on productivity. The main aim is to determine the effect of a good maintenance culture on the reliability of gas turbine power plant operation, to evaluate the loss of revenue due to high rate of intervention (or failure) in the power plant. Materials were collected from the manufacturers manual, maintenance report log sheet, production report log book/sheets, intervention schedule programmes, mechanical vibration analyzer machines, machine lubrication schedule/plan, Instrumentation calibration manuals and plants simulation model log books/sheets, human machine interface system (HMI) as well as the equipment control and monitoring machine (ECM), self administered questionnaire and internet system. The collated data were analyzed with the aid of number of intervention, mean time between failures (MTBF), mean time to repair (MTTR), production yield, loss of revenue equations as well as responses from returned questionnaires to determine the effect of adequate maintenance of the power plant on productivity. The analysis revealed a negative growth rate in productivity resulting from poor performance of the gas turbine power plant in the year (2013-2017) due to poor maintenance culture. There has been colossal loss of revenue due to incessant shutdown of power plant which also affects the process plants. From the result of five years average yield of Notore historical products cost per day for two major products namely; ammonia and urea amounted to N0.1104B. In other words, for one production year, revenue of N36.4 Billion would have been received but due to high plant down-time for maintenance intervention, the company lost about N0.851Billion in a year. This is a huge loss to Notore Chemical Industries Plc.

Energy Regulator Supply Restoration Time

In conventional reliability analysis, the duration of interruptions relied on the input parameter of mean time to repair (MTTR) values in the network components. For certain criteria without network automation, reconfiguration functionalities and/or energy regulator requirements to protect customers from long excessive duration of interruptions, the use of MTTR input seems reasonable. Since modern distribution networks are shifting towards smart grid, some factors must be considered in the reliability assessment process. For networks that apply reconfiguration functionalities and/or network automation, the duration of interruptions experienced by a customer due to faulty network components should be addressed with an automation switch or manual action time that does not exceed the regulator supply restoration time. Hence, this paper introduces a comprehensive methodology of substituting MTTR with maximum action time required to replace/repair a network component and to restore customer duration of interruption with maximum network reconfiguration time based on energy regulator supply requirements. The Monte Carlo simulation (MCS) technique was applied to medium voltage (MV) suburban networks to estimate system-related reliability indices. In this analysis, the purposed method substitutes all MTTR values with time to supply (TTS), which correspond with the UK Guaranteed Standard of Performance (GSP-UK), by the condition of the MTTR value being higher than TTS value. It is nearly impossible for all components to have a quick repairing time, only components on the main feeder were selected for time substitution. Various scenarios were analysed, and the outcomes reflected the applicability of reconfiguration and the replace/repair time of network component. Theoretically, the network reconfiguration (option 1) and component replacement (option 2) with the same amount of repair time should produce exactly the same outputs. However, in simulation, these two options yield different outputs in terms of number and duration of interruptions. Each scenario has its advantages and disadvantages, in which the distribution network operators (DNOs) were selected based on their operating conditions and requirements. The regulator reliability-based network operation is more applicable than power loss-based network operation in counties that employed energy regulator requirements (e.g., GSP-UK) or areas with many factories that required a reliable continuous supply.

Optimal availability for determining choice and repair policy of system components

Purpose The purpose of this paper is to establish the regression model by a simulation method that was obtained by using the system response at unit cost as the response value. The unit availability was maximized, while the unit cost was minimized. Design/methodology/approach In this study, the Monte Carlo simulation method was used to simulate an operational system, and the regression model was obtained by using response surface methodology with the experimental matrix and different levels of experimental combinations. Findings The optimal value of mean time between failure (MTBF) and mean time to repair (MTTR) of each component was then obtained by using the system response at unit cost as the response value. Practical implications Due to the upgrading of industrial technology and the maturity of electronic technology, product development technology has become highly sophisticated with complex designs. Reliability engineering has become a key procedure of high-tech industry. Social implications Based on the system availability of unit cost as the response value, it can maximize the availability to help decision makers to formulate the best selection strategy components and repair strategy. Originality/value Previous works regarding the parameter settings of reliability values never mention the simulation methodology. However, this study aims to achieve the above goals in finding the relationship of MTBF and MTTR simultaneously.

A quadri-dimensional approach for poor performance prioritization in mobile networks using Big Data

The Management of mobile networks has become so complex due to a huge number of devices, technologies and services involved. Network optimization and incidents management in mobile networks determine the level of the quality of service provided by the communication service providers (CSPs). Generally, the down time of a system and the time taken to repair [mean time to repair (MTTR)] has a direct impact on the revenue, especially on the operational expenditure (OPEX). A fast root cause analysis (RCA) mechanism is therefore crucial to improve the efficiency of the operational team within the CSPs. This paper proposes a quadri-dimensional approach (i.e. services, subscribers, handsets and cells) to build a service quality management (SQM) tree in a Big Data platform. This is meant to speed up the root cause analysis and prioritize the elements impacting the performance of the network. Two algorithms have been proposed; the first one, to normalize the performance indicators and the second one to build the SQM tree by aggregating the performance indicators for different dimensions to allow ranking and detection of tree paths with the worst performance. Additionally, the proposed approach will allow CSPs to detect the mobile network dimensions causing network issues in a faster way and protect their revenue while improving the quality of the service delivered.

Implementing TPM supported by 5S to improve the availability of an automotive production line

The maintenance management is a topic of strategic importance for automotive manufacturers. In fact, an effective maintenance process and a preventive maintenance (PM) procedure can significantly reduce the risk of equipment failures that can lead to downtime on the production lines. However, due to the complexity of an automotive production system, the risk of failure on a crucial piece of the production equipment cannot be entirely avoided. The study made in this paper aims at improving the availability of a critical production line through the total productive maintenance (TPM) methodology and supported by Lean Maintenance tools. An analysis is made of the initial condition of the line where the main problems are identified by employing several tools for this purpose, such as Mean Time between Failures (MTBF), Mean Time to Repair (MTTR), Overall Equipment Efficiency (OEE) and Availability (A). In response to the identified problems, an action plan is developed and implemented in order to find the root cause of the high number of malfunctions and faults in one of the line’s equipment with the use of 5S tools, visual management, and maintenance progress, as well as the development of a training program to increase operators’ skills. The results of such actions were positive as the line became more organized, the value of the MTBF increased, the MTTR value decreased, and consequently the overall availability increased.

Applying Machine Learning Technology to Optimize the Operational Cost of the Egyptian Optical Network

The Optical Network is considered an important asset to any telecom operator. One of the most critical issues to the operators is how they can maximize the Return of Investments (ROI) by optimizing the operational costs in the optical network. In this paper for the first time we propose an Intelligent Universal Platform (IUF) to manage and optimize the operational tasks in the optical network. the proposed platform drives the supervision of the network to the automation, and is formed by the integrations between four models, the 1’st one is the energy model and built according to the variations in the power consumptions and it proposes the recommended actions to optimize the energy consumption by swapping the cooling system between two system types (air-conditions and fan units) according to the temperature degrees, also the model learns from the variations in the heat dissipation according to the busy and the idle modules and it can transform the unused hardware to the sleeping mode, the 2’nd model is the fault localizations and is built by the practical experiments of the faults recovery and forms its expert system to predict the mean time to repair (MTTR), the 3’rd model is the network performance and is built by monitoring the optical signal to noise ratio (OSNR) and provides the recommended corrective actions to keep the same level of the performance, the 4’th model is the configuration management and it suggests the best routes in the network. The paper studied four use cases in two situations As-Is and To-Be, the cases are about the energy consumptions, the fault locations, the circuit creations and finally the variation in the signal to noise ratio. the results shows that by using the machine learning in our platform the time of the fault location is reduced from 40 min to 3 min, the efforts to create one circuit is reduced by 30.87%, the number of the complaints are reduced by 30% per year, and the response time to the complains is decreased from 55 min to 5 min. This indicates that in the near future the machine techniques will play a significant role in monitor, detect, localize faults, and finally optimize the resources of optical network, all without human intervention.

Perencanaan Perawatan Mesin Pump 107 Dengan Metode Reability Centered Maintenance (RCM) di PT. Petrokimia Gresik

PT. Petrokimia Gresik is one of the largest fertilizer companies in Indonesia, locatedin Gresik, East Java. The pump 107 engine on the Ammonia unit is often damaged due to itslargest size compared to other types of pumps, as well as the result of carrying thick orconcentrated fluid (bluish black) so that it is heavy for the drainage process. The purpose ofthis study is to plan and recommend the proposed treatment method on the pump enginebased on the method used to improve the working efficiency of the pumping machine 107and determine the failure mode and diagnosis of the effects of failure modes that occur in thecomponent. The method used is the Reability Centered Maintenance (RCM) method whichis expected to be able to produce maintenance or maintenance scheduling that is increasinglydirected so that it can improve the performance and efficiency of the engine, reduce repaircosts, and extend the service life of the machine itself. From this study, the results are in theform of Faliure Model And Effect Analysis (FMEA), FMEA table preparation is carried outbased on component function data and maintenance reports which can then be determined byvarious failures resulting in malfunction. From the compilation of FMEA, it can be seenwhat the causes of failure are and what impacts they have caused. Next, the value of MeanTime Between Failure (MTBF) pump 107-JA is 15,829 hours, pump 107-JB is 43,764 hoursand pump 107-JCM is 19,578 hours. Maintainability M (t) or Mean Time to Repair (MTTR)value on pump 107-JA is 2,914 hours, pump 107-JB is 3,411 hours, and pump 107-JCM is3,1 hours, Availability A (t) value is pump 107-JA at 84.44%, pump 107-JB at 92.76% andpump 107-JCM at 86.31%. The last one is found that the failure rate of pump 107-JA is0.063172, pump 107-JB is 0.02284 and pump 107-JCM is 0.051.

Improving Decision-Making Grid based on interdependence among failures with a case study in the steel industry

Purpose Decision-Making Grid (DMG) is used for determining maintenance tactics and is associated with the reliability and risk management of assets. In this grid, decision making is performed based on two indicators of Mean Time to Repair (MTTR) and frequency of failures. The purpose of this paper is to improve DMG by recognizing interdependence among failures. Design/methodology/approach Fault Tree Analysis and Reliability Block Diagram have been applied for improving DMG. The proposed approach has been examined on eight equipment of the steel making and continuous casting plant of Mobarakeh Steel Company. Findings Findings indicate different positions of equipment in the cells of the new grid compared to the basic grid. Research limitations/implications DMG is limited to two criteria of frequency of failures and MTTR values. In both basic and new DMGs, cost analysis has not been performed. The application of the proposed approach will help the reliability/maintenance engineers/analysts/managers to allocate more suitable maintenance tactics to equipment. This, in turn, will enhance the equipment life cycle and availability as the main objectives of physical asset management. Originality/value A major limitation of basic DMG is that the determined tactic based on these two indicators might not be an appropriate solution in all conditions, particularly when failures are interdependent. This has been resolved in this paper.

Improving Reliability through Condition Monitoring of Lubricating Oil in Hydrocarbon Compressor – A Case Study

The present case study highlights the approach on application of condition monitoring practices towards improving safety and reliability of a reciprocating gas compressor. Laboratory oil analysis reports on lubricant condition and equipment condition of the Recipak Ethylene compressor were utilized to get pertinent information about the reduction in flash point, viscosity, and total acid number (TAN) due to mixing of hydrocarbon with oil. This monitoring report aided in planning of replacement of the packing, the root source of problem at appropriate time, maintaining oil properties and running of machine to its optimum level. Such extended period of operation under known condition of machine health has resulted enhancement of the Mean Time between Failure (MTBF) and reduction of Mean Time to Repair (MTTR), resulting improved machine reliability. In addition, it has averted the operation of explosion valve in crankcase and risk of fire. Last but not least, the approach has boosted the moral of the working team.

Integrated Fault Detection Framework for Classifying Rotating Machine Faults Using Frequency Domain Data Fusion and Artificial Neural Networks

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.