Mean Time To Repair Research Articles

The application of the Bayesian linear regression model to optimize the maintenance of a programmable logic controller

In this paper, an optimization approach, which is based on the Bayesian Linear Inference (BLI) model, has been proposed for the maintenance of Programmable Logic Controllers (PLCs). The BLI model, which is implemented using historical data, incorporates maintenance indicators like the number of failures (NF), total downtime (TD), total unexpected intervals (TUI), mean time to repair (MTTR) and mean time between failures (MTBF). It offers a probabilistic framework for determining the influence of each predictor variable on PLC maintenance. The model produces posterior means, credible intervals, and standard deviations, which provide insights into the magnitude and uncertainty of these relationships. The results from the study show that factors like NF and TD are influenced by the magnitude and direction of the maintenance levels. Also, the R-squared score (0.85) also indicates how much of the variability in maintenance in the system. From the results obtained, the study can conclude that the BLI model can optimize PLC maintenance procedures by identifying essential components and their contributions. Also, it is able to estimate future maintenance requirements and helps with resource allocation and process optimization decisions.

Routing problem for reducing significant outages in optical networks and its system analysis

Optical communication networks are operated with great care to avoid outages. In particular, significant outages having a devastating impact on society must be avoided, and several countries specify “significance standards” for network outages. Although the impact of possible outages strongly depends on routing methods for requested connections, there has been no research specifically on these standards. This paper for the first time, to the best of our knowledge, introduces a routing problem to minimize the impact of outages on a given significance standard. We have shown this problem to be non-deterministic polynomial-time hardness, and an efficient heuristic method is proposed. This method searches for a good solution by reducing the problem to a shortest-path search, where the significance of a link failure is regarded as the link weight, and suppresses outage impact compared with other routing methods. In addition, the analysis of the system dependence among a network model, mean time to repair (MTTR) of failure elements, and significance standards suggests that the total system design considering the MTTR, significance standards, and routing method could dramatically reduce the impact of significant outages on optical networks.

Maintainability Assessment during the Design Phase: Integrating MTA and UNE 151001

The focus on maintenance actions in the early design phases has been a trend in recent years. The main sources of information during the design of the maintainability process include operator reports, maintainer experience, failure history, and manufacturer recommendations. During this process, an important aspect is related to the configuration of maintenance tasks and interventions, such as their main phases, activities, and durations. The allocation or estimation of maintainability involves identifying and/or estimating the mean time to repair (MTTR) for each component or system. The time of the maintenance tasks or the repair time are fundamental for companies, as the availability of equipment directly depends on this parameter. In this study, a new method for evaluating maintainability during the design phases is proposed. The method is based on the integration of the maintenance task analysis (MTA) principles and the UNE 151001 maintainability evaluation standard. A data structure is proposed that serves the application of the UNE151001 procedures, obtaining a data-based maintainability evaluation. As a validation procedure, an application of the proposed approach is presented using two overhead cranes. Comparisons and recommendations are made regarding the maintainability of both pieces of equipment. Finally, some managerial and engineering insights are presented.

EVALUATION OF MAINTENANCE EFFECTIVENESS AND HUMAN FACTORS: CASE STUDY OF A RESEARCH INSTITUTE

Maintenance management is an integral part of any organization’s overall commercial process that develops administrative operational value. Thus, the maintenance function is of immense importance to ensure the safety and effective operation of a facility and equipment in compliance with a statutory obligation, to maximize equipment life, and to reduce the risk of failure. However, for maintenance success work planning, the perception of human factors, and the interrelation of workers and groups in any organisation must be understood. This in turn would determine the allocation of maintenance tasks to team members. This research focused on the evaluation of key performance indicators in relation to the maintenance effectiveness of selected equipment, in a research institution. The impact of human factors on the maintenance was also investigated. Key performance indicators such as Mean Time to Repair (MTTR), Mean Time Between Failure (MTBF), downtime, repair time, and availability were used to quantitatively evaluate the maintenance effectiveness of the equipment, while the systematic approach to empirical research was used to analyse the effect of human factors on maintenance practices. This study was carried out over a period of 6 months on a 4-tonne hydraulic lift, hydraulic hoist jack lift, tire changing machine, fault diagnostic machine, and computerized alignment gauge. Data collected from the maintenance unit was analysed. A total of 11.92 hours was recorded as the MTTR of the five machines. The machines had a high level of availability and a total downtime of 12.01 hours out of operational time of 2369.8 hours over a period of 6 months. The level of motivation and competence among maintenance personnel was assessed to be very good. This is expected to have contributed largely to the effective maintenance practices of workers in the unit. From this study, it can be deduced that the level of motivation and competence is high among maintenance personnel. In terms of productivity, effectiveness, teamwork, decision-making, execution, safety, and responsiveness, the human factor has been shown to have thrived well. This in turn, stimulated the corresponding increase in the availability of machines over the 6 months- of study.

Remaining Useful Life Estimation (RUL) For Critical Mechanical Components In Tractor

The proposed framework utilizes an evolutionary mathematical approach to optimize data-related parameters for estimating the Remaining Useful Life (RUL) of critical mechanical components. By integrating advanced time window techniques and considering factors like downtime and failure occurrences, it offers a comprehensive solution for RUL prediction across various industrial contexts. Continuous monitoring systems play a pivotal role by detecting component degradation early, allowing for proactive maintenance and minimizing catastrophic failures. Combining Mean Time to Repair (MTTR) and Mean Time Between Failures (MTBF) offers valuable insights into system performance, enabling proactive maintenance strategies. This review explores mathematical methodologies for predicting RUL in tractors, crucial for enhancing maintenance planning and remanufacturing engineering.

Identifying critical components for railways rolling stock reliability: a case study for Iran.

Electrical railways constitute a vital component of transportation infrastructure worldwide, with rolling stock representing a key element of these systems. Given the extensive operational hours of such systems, effective maintenance scheduling and asset management are imperative to ensure reliability and safety while mitigating costs. This paper addresses the challenge of optimizing maintenance practices for railway rolling stock by introducing a novel implementation of reliability-centered maintenance (RCM) grounded in the reliability block diagram (RBD) framework. This methodology meticulously incorporates reliability parameters into maintenance strategies, aiming to enhance the operational efficiency of railway systems. Leveraging the criticality index, the study identifies components crucial for train reliability, facilitating cost-effective maintenance management. The proposed approach is applied and validated on the Tabriz line 1 metro in Iran, a system with over six years of operational history. Analysis reveals the bogie subsystem's criticality due to its interconnected components, with parts exhibiting significant mean time to repair (MTTR). Conversely, the brake system emerges as the most reliable subsystem. Additionally, sensitivity analysis demonstrates an inverse relationship between repair rates and component sensitivity, highlighting the pivotal role of efficient repair processes in bolstering system reliability. This research contributes a comprehensive and validated methodology for RCM in railway rolling stock, emphasizing cost reduction, system reliability, and strategic prioritization of maintenance efforts. As the approached method in this research is not limited to the specific case study and can be applied in any system by generating the RBD and reliability parameters of the system we want to study The findings hold significant implications for the global planning and execution of railway maintenance operations, setting a new standard for reliability-centered maintenance practices in the field.

Continuous quality improvement project to reduce the downtime of medical linear accelerators: A case study at Zhejiang Cancer Hospital

ObjectiveTo analyse and continually improve existing issues in the quality improvement process of medical linear accelerators (LINACs) and enhance the quality control management of LINACs. MethodsData were collected from eight LINACs (sourced from three manufacturers) at Zhejiang Cancer Hospital using Excel diaries between January 2019 and December 2020. The data description and analysis were performed using the analytic hierarchy process, SPSSAU and Excel software, and mean-time-to-repair (MTTR)/mean-time-between-failure (MTBF) metrics. Continuous quality improvement was executed using the quality control circle (QCC) quality management method. ResultsAfter quality improvement, the risk frequency of ‘LINAC down’ events decreased by 43.63% and downtime was reduced by 40.45%. The weight of downtime risk improved by 73.69%. The MTTR recovery value increased by 31.90%, and MTBF reliability increased by 2.97 h. The simulation results demonstrated that the proposed quality improvement measures could effectively decrease the frequency and duration of downtimes, consequently extending the normal operational time of LINACs. ConclusionTransitioning from instant repair to preventative maintenance can enhance the operational efficiency of equipment and yield economic benefits for hospitals. The QCC method and the event risk evaluation model are effective in reducing the downtime of LINACs and improving their quality control management.

Reliability Analysis of a Typical 33kV Distribution Network Using MATLAB (A Case Study of Ile-Oluji 33kV Distribution Line)

The significance of a dependable and sufficient electrical power supply in societal development cannot be overstated. The distribution of electrical power holds a crucial position in the power system chain, representing the final stage where it reaches consumers. At this phase, various types of losses, including technical loss and theft, among others are associated. The Ile-Oluji community is situated in Ondo State, a south-western part of Nigeria, A region which is host to federal, state, local and private institutions. This community grapples with inconsistent power supply experiencing intermittent trips without a clear originating cause from components in the Ile-Oluji injection substation and its feeder in Ondo. This study analyses the power distribution patterns in this locality focusing on three primary elements (transformer, switch gear and supply line) extracting data on; failures, outage time, numbers of customers and total hours for the year 2019 to 2022. Reliability indices were employed to assess its performance utilising the MATLAB software. Codes were crafted to extract these indices; Availability, Failure rates, Mean Time to Repair (MTTR), Mean Time Between failure (MTBF), System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), and Customer Average Interruption Duration Index (CAIDI). The results show that there is a strong relationship among the reliability indices. It is adduced that the lower the failure rate, the higher the availability of a system. It was concluded that the power system switchgear recorded the highest failure rate amongst the three elements with 0.037341 hrs/year followed by supply line with 0.022541 hrs/year and the transformer with 0.0148 hrs/year in 2022. In corollary, transformer has the highest availability of 0.94441 followed by supply line with 0.81681 and switchgear with 0.78004 in 2021, switchgear recorded the highest failure rates because it is responsible for executing both forced and planned outages.

Minimization of Root Causes of Declining Productivity in a Manufacturing Company: A Case Study of Jocalis Company

This presents a comprehensive methodology employed to identify and address declining productivity issues within the JOCALIS Aluminium Roofing Sheet Manufacturing Company Limited, situated in Onitsha, Nigeria. The study encompasses various manufacturing divisions, with a primary focus on aluminium roofing sheet production of three lines involve in production of red, black, and milk colour coated roofing sheet. The research aims to uncover the root causes of defects in the manufacturing process and develop effective strategies for improvement. Key methods include data collection through interviews, company records, library research, and internet sources, followed by data analysis and root cause identification. From the result obtained, the average availability of critical machine like the roller machine after root cause analysis is increased by 10.62%. Also, the average MTBF (mean time between failure) of the critical machine after root cause analysis is increased by 13.66% and MTTR (mean time to repair) is decreased to 46.42% respectively. The applications and general impact of this study includes enhancing machine availability, reducing downtime, increasing efficiency, optimizing maintenance practices, implementing preventive maintenance schedules, improving equipment diagnostics, and prioritizing root cause analysis to reduce breakdowns.

Maintenance Efficiency Optimization through Effective Leadership and Emotional Intelligence

This study examines the relationship between effective leadership, emotional intelligence (EI), and maintenance efficiency. It investigates the effects of leadership and EI initiatives on maintenance efficiency in three production facilities situated in Nigeria’s Niger Delta region. Important metrics such as Mean Time Between Failure (MTBF), Mean Time to Repair (MTTR), Downtime hours (DT), and Failure rate (λ) were employed in this study. Results reveal significant improvements in the employed metrics across all three facilities following the implementation of effective leadership and EI strategies. The case study facilities, EOC, OPP, and KGP experienced average improvement values of -73.25, -89.75, and -75.00 respectively. Despite variations in the level of improvement, the facilities witnessed enhancements in MTTR, MTBF, DT, and λ. The negative average improvement values signify improvements in maintenance efficiency post-implementation. The findings emphasize the positive impact of leadership and EI initiatives on maintenance practices and continuous improvement in organizations. By investing in leadership development and EI training, organizations can improve operational efficiency, optimal reliability, and cost reduction. The integration of these skills has significantly enhanced maintenance efficiency in the facilities studied, highlighting their significance in maintenance-intensive industries.

Analisis Preventive Maintenance pada Mesin Injection Molding dengan Metode Mean Time Between Failure dan Mean Time to Repair di PT. XZY

A series of predictable preventive activities to overcome machine failure during operation is very important because it produces good and stable spare parts. This research was carried out with the aim of preventing machine damage due to short circuits that occur in injection molding machines used for the sudden production of automotive component spare parts. The research was carried out using the Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR) approaches to calculate the time between the machine breaking down and the time it was repaired. The MTBF calculation results in this research were 72.9 hours or less than 4 days the engine would experience damage or every 72.9 hours the engine would experience damage again. The MTTR value obtained is 2 hours, where in 2 hours the work to overcome the damage can be done.

Cost effective maintenance strategy for centrifugal pumps using reliability centred maintenance

The aim of this work is to obtain a maintenance strategy that is cost effective for centrifugal pumps using reliability centred maintenance (RCM). The components of the centrifugal pump used for the study are the shaft, impeller, bearing, mechanical seal, coupling, shaft sleeve, wear-ring casing, suction flange and discharge flange. The motor that drives the pump was also considered. Two basic tools of the RCM strategy were applied. These are the failure modes, effects and criticality analysis (FMECA) and logic tree analysis (LTA). Failure data for a period of 10 years about a centrifugal pump was collected and the FMECA was applied to determine the failure modes, the effects of the failure, the criticality of the components, the mean time between failure (MTBF), the failure rate and the mean time to repair (MTTR). The risk priority numbers of all the components were estimated. The LTA was applied to come up with a new maintenance strategy for each of the components of the pump. The maintenance cost consisting of spare part cost and labour cost for the pump components were estimated for the current maintenance practice as well as the new or proposed maintenance practice. The most critical components observed were the motor, shaft, impeller and the mechanical seal. The motor has the highest MTTR value (58 hrs) while the mechanical seal has the highest failure rate (8.06E-04 per hr). The proposed maintenance strategy leads to reduction in spare part cost in a number of components, with the largest reduction of 19.88% occurring in mechanical seal. The proposed strategy also leads to reduction in labour cost in motor, shaft, impeller and the mechanical seal with the values 33%, 25%, 50% and 25% respectively. The methodology could be applied to other devices.

Preventive Maintenance Analysis Based on Mean Time Between Failure (MTBF) and Mean Time to Repair (MTTR)

A company's ability to meet its goals depends heavily on the smooth operation of its production process. To ensure that the production machinery operates smoothly and without any breakdowns, it is essential to implement effective maintenance that addresses any issues related to the equipment. This research aims to identify the root causes of equipment failure in the soda and dolomite bucket elevator by analyzing the Mean Time Between Failure (MTBF), Mean Time to Repair (MTTR), and conducting a qualitative evaluation using Failure Mode Effects Analysis (FMEA). The analysis reveals that the company performs preventive maintenance every 3 days, which is more frequent than the optimal schedule of once every 33 days. This suggests that the company may be addressing unforeseen failures. The FMEA analysis indicates that the highest Risk Priority Number is associated with suboptimal maintenance of the Staff. To address this issue, the company can use Radio-Frequency Identification (RFID) technology.

RELIABILITY, AVAILABILITY, AND MAINTAINABILITY (RAM) ANALYSIS FOR PERFORMANCE EVALUATION OF POWER GENERATION MACHINES

<p>This paper presents a reliability, availability, and maintenance (RAM) analysis framework to evaluate the performance of Power Generation Machines (MPL) as part of office infrastructure. MPL performance is evaluated by calculating maintenance performance, including Mean Time Between Failures (MTBF) to see reliability aspects, Mean Time to Repair (MTTR) to see maintainability aspects, and availability to see its operational availability. This research used data as maintenance log sheet data from three MPLs from January to September 2021. The results showed that MPL1 had the most optimal condition with an availability value of 93.48%, MPL2 was 82.19%, and MPL3 was 70.55%. Root cause analysis was carried out using Fishbone Diagram. It was able to identify the main problems in the machine's age and the procedure for submitting maintenance. Maintenance performance improvement is very much needed, especially the availability aspect for MPL2 and MPL3, as well as the valuation of existing preventive care. In addition, determining qualification standards for availability, MTBF, and machine MTTR needs to be carried out as a quantitative consideration in making MPL replacement decisions.<strong></strong></p><p> </p>

Condition Monitoring and Fault Detection of Wind Turbine Driveline With the Implementation of Deep Residual Long Short-Term Memory Network

In view of the wide application of wind power, it is critically essential to develop solutions to the high fault rate and the long mean time to repair (MTTR). Multi-sensor fusion methods have been widely applied in condition monitoring and anomaly detection of industrial installation. Aimed at the wind turbine driveline, a new method utilizing the deep residual LSTM network with Attention model (ResLSTM-AM) is proposed in this paper. Firstly, data from multi-sensor fusion systems like supervisory control and data acquisition (SCADA) is preprocessed by quartile data cleaning and corresponding selection using the calculation of maximal information coefficient (MIC) to enhance its reliability. Secondly, we establish the neural network by the deep residual network (ResNet), long short-term memory network (LSTM), and attention mechanism (AM) for time series forecasting of wind turbines. The newly added residual connecting architecture in LSTM layers ensures a higher learning rate in feature extraction and provides a more flexible data flow both in forward and backward signals. Additionally, the attention mechanism after a LSTM layer amplifies the influence of vital data. The model gets validated based on historical SCADA data from actual wind turbines containing healthy conditions and anomaly conditions, showing ResNet contributes more. Based on the root-mean-square error (RMSE), the alarming threshold is calculated by an exponential weighted moving average (EWMA). For two chosen variables, the proposed method is confirmed to be efficient and reliable with the accuracy of 0.9945 and 0.9880 in the results of trials and comparisons with other existing models.

MTTR reduction of FPGA scrubbing: Exploring SEU sensitivity

SRAM-based FPGAs are widely used for developing many critical systems due to their huge capacity, flexibility, and high performance. However, their applicability in the presence of the Single Event Upsets (SEUs) should be ensured using mitigation techniques for specific critical systems e.g. space applications or automotive industry. Among all existing mitigation techniques, the scrubbing scheme is considered the most reliable technique that particularly avoids SEU accumulation in Configuration Memory (CM) that is the most SEU vulnerable component in an SRAM-based FPGAs. In spite of that, the error repair time realized by Mean Time to Repair (MTTR) attained by scrubbing is a pressing concern for real-time systems. To reduce MTTR, the impact of SEU in CM bits on correct circuit operation is considered in state-of-the-art scrubbing methods. In this paper, we examine the MTTR reduction taking into account multiple proposed precision levels to identify CM sensitive bits; i.e. bits have an adverse impact on correct circuit operation when infected by SEU. Two scrubbing methods are proposed based on these precision levels. Experimental results show an average of 20% \\45% \\46.5% MTTR reduction if the proposed precision-aware scrubbing methods taking into account sensitive bits recognized with low \\medium \\high precision compared to with very low precision. Experiments also show that higher MTTR reduction was achievable for non uniform structure like FFT (i.e about 68%). Thus the cost of distinguishing sensitive bits with higher precision is worth only when the circuit has a non-uniform structure in CM.

AVIOBRIDGE MAINTENANCE MANAGEMENT: A SOLUTION FOR CABIN CURTAIN DAMAGE

This research raises the problem in one of the traction equipment after the absence during covid-19 with evidence that an occurrence causing trouble with the curtain results from a lack of monitoring and maintenance. As a result, the curtain gets out of the way of the cabin, which causes the following impacts passenger may be trapped, unstable curtain movement rotation, and the sound of the curtain becomes harsh. This research aims to prevent further damage to the cabin curtain, considering that aviobridge is one of the supporting flight operations at the airport. This research uses an exploratory study using literature, observations, and interviews. This research indicates that maintenance to aviobridge does not conduct appropriately during the pandemic causing problems for the aviobridge. The result is expected that maintenance will be carried out according to applicate standard operating procedures such as lubricants between the curtains using lubricants such as lubrication simultaneously with the movement of the cabin rotation to the right and left, checking the roller curtains at the top and bottom using shock lock 32, and adjusting the gear up and down if already loose. Intensive preventive maintenance is the key to establishing any facility and the sign of a good quality of production in the industry. The researcher calculated the Mean Time Between Failure (MTBF) and Mean Time To Repair (MTTR) with a sample to the parking stand N06 and proposed that the organization obeys the applicable standard operating procedures for maintenance.

Accessibility Study of mHealth Systems Based on The Internet of Things (IoT)

The Internet of Things (IoT) has become part of people's daily life, allowing physical and digital contact. The rise of mobile devices and scientific and technological advances in health have led to breakthroughs in meeting consumer needs. mHealth refers to using mobile devices to improve healthcare services, increase medical care, and reduce costs. Mobile cloud computing (MCC) lets users bypass mobile device limits on processing, storage, and battery life. A body area network uses implanted wireless sensors to remotely monitor patients (WBAN). These networks collect and distribute data for disease diagnosis and prevention. This mix of technologies allows hospitals and clinics new ways to treat and monitor patients. This research examines IoT availability in mHealth. The analysed architecture includes wireless sensors to monitor patients, an intra-BAN, a mobile device with a battery and communication interfaces, an extra-BAN with Wi-Fi and 4G connectivity, and a cloud environment to store data. Hierarchical models were developed using RBD and continuous-time Markov chains (CTMC). Each component's MTTF (mean time to failure) and MTTR (mean time to repair) values are used to quantify system or section availability. Experiments were conducted to test mHealth availability parameters. Intra-BAN, Zigbee, or Bluetooth protocols have no meaningful impact on system availability. For households, two small routers in the extra-BAN are more effective than one large router. Finally, backup batteries and power banks boost availability. The offered models can help developers and maintainers scale mHealth systems based on service needs.

Assessing repair and maintenance efficiency for water suppliers: a novel hybrid USBM-FIS framework

A metropolitan area's water supply is imperative to the city's development. One of the main goals of the water supply utilities is to ensure the availability of water, as a lack of water would cause many social, political, or health problems. Therefore, water supply facilities must be in good condition, efficient preventive maintenance plans must be implemented, and the performance of the maintenance team monitored. In this paper, efficiency indices of Tehran water utility maintenance teams are investigated using different Data Envelopment Analysis (DEA) models. The final scores were then used as inputs to a Fuzzy Inference System (FIS) to assess the efficiency of these maintenance units. Two performance indicators based on DEA, "Availability efficiency" and "Repair time efficiency" are introduced for performance assessment. The Mean Time Between Failure (MTBF) and the Ready To Operate (RTO) are two desirable outputs that are considered in addition to one undesirable output: the Mean Time To Repair (MTTR). In addition, we suggest a new index named MRRW by combining the DEA efficiency with the RRW index. We introduce a novel approach based on DEA combined with FIS methods and the new factors for evaluating water supply maintenance systems, while most previous studies on measuring the efficiency of maintenance teams consider only limited aspects of performance measurement. Based on the results of our study, it became clear that the MRRW measures efficiency better than the traditional RRW measures. We present future improvement strategies based on the output of the FIS.

A stochastic Petri Net‐based approach for operational performance estimation of quay cranes

AbstractReliability, availability, and maintainability (RAM) of quay cranes (QCs) are essential for an effective port operation. This study estimates operational RAM of QCs using the Stochastic Petri Net (SPN) modelling. Asset Performance Assessment (APA) was performed to conduct the study by a SPN model. Based on the operation and maintenance data, probability distributions of Mean Time Between Failure (MTBF) and Mean Time to Repair (MTTR) is determined by Goodness of Fit Test of Anderson Darling. Subsequently, these distributions are applied in APA to determine the frequencies of failure/breakdowns, duration of downtimes, availability, and reliability with the assistance of the SPN model and Monte Carlo Simulation. The availability of the analysed QC is 0.97 or 97%. The results of this work are verified by the comparison with historical data. The outcomes of this paper contribute to reliability, availability, maintainability, and risk management of QCs.