Mean Time To Failure Research Articles

Выбор задаваемых параметров для оценки соответствия восстановленных погружных электродвигателей требованиям на капитальный ремонт

The main characteristic of the performance of submersible electric motors after overhauls is their reliability. To assess the compliance of reliability indicators of restored submersible electric motors with technical requirements for overhauls, various types of tests are used. The most acceptable of them is the bench test method based on a sequential failure analysis of the studied electric motors. To implement it, the specified parameters should be set. In this study, based on the use of technical requirements for the manufacture and repair of submersible electric motors, the authors determined the specified average operating times for failure of new electric motors (before the first overhaul) and after it. Bench tests were carried out for two batches of submersible electric motors: overhauled and non-overhauled. In each batch, 20 electric motors were studied. The experiment determined the standard deviation of the mean time between failures (MTBF) of new electric motors, which amounted to 3.54 thousand hours, after the overhaul - 2.37 thousand hours. The coefficients of variation were 0.3 and 0.34, respectively. The study results determined the values of the set parameters of the mathematical model, enabling the use of the method of successive failure analysis. The values included the set average MTBF before the first overhaul - 16 thousand hours; the set average MTBF after the overhaul - 12.8 thousand hours; the risks of the repair provider and the customer - 0.05; the set mean square deviation of the MTBF of electric motors before the first overhaul - 4.8 thousand hours; the set mean square deviation of the MTBF after the first overhaul - 4.8 thousand hours; the set mean square deviation of the MTBF after the overhaul - 2.37 thousand hours.

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.

Performance of load sharing repairable k-out-of-N: G systems

The aim of this research is to explore the performance of load-sharing k-out-of-n: G systems. In such structures, the system operates if at least k components function. Furthermore, in load sharing systems, the load is automatically transferred to the functional components that are still in place whenever one component fails. In this article, asymptotic availability and mean time to failure (MTTF) are used for measuring the system performance assuming exponentially distributed repair times and times between failures. Results indicate that in selecting any configuration over others, we should not only consider the system's performance but also the total cost. For illustrations, the article exhibits a real-life numerical example.

LIFETIME PREDICTION OF SINGLE-STAGE LED DRIVER CIRCUIT USING BAYESIAN BELIEF NETWORK

In light-emitting diode (LED) lighting, the driver commands the lifetime and the LED bulb. It is essential to predict the driver's lifetime during its design stage. This paper proposes a viable method to predict the lifetime of the LED driver based on its failure rate. A novel single-ended primary inductor converter (SEPIC) integrated parallel ripple cancellation circuit (PRC) topology of 30 W is considered to estimate the lifetime of the driver circuit. The failure rate model is regarded as a base to predict the lifetime of LED drivers using Bayesian belief network (BBN) analysis. The weakest links are the prime components to estimate the lifetime, and they are active devices and capacitors employed in the driver circuit. The output capacitor is considered a degree of importance as per the existing literature, and the proposed course has been designed without using any electrolytic capacitor (EC) to enhance the reliability of the driver circuit. This approach ensures an effective way to access the mean time to failure (MTTF) or the lifetime of the LED driver in a lucrative manner.

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.

Integration of Industry 4.0 with reliability centered maintenance to enhance sustainable manufacturing

AbstractAchieving sustainability in manufacturing requires optimizing reliability and productivity while reducing environmental impact. This article introduces a novel approach by integrating Industry 4.0 technologies with reliability centered maintenance (RCM) to create a “smart RCM” model. In an era where Industry 4.0 leverages the Internet of Things (IoT), cyber physical systems (CPSs), cloud computing, and cognitive computing, this integration transforms conventional RCM into a data‐driven and efficient system. A case study conducted in a cement plant illustrates the benefits of smart RCM implementation. Key performance indicators (KPI), including increased production volume, reduced downtime, enhanced mean time between failures (MTBF), and improved overall equipment effectiveness (OEE), showcase the positive impact of smart RCM. Furthermore, specific environmental performance indicators, such as energy consumption, carbon footprint, and water consumption, exhibit favorable changes, contributing to sustainability. The implementation of smart RCM also presents an attractive payback period, making it an appealing approach for industries looking to optimize maintenance practices and environmental performance. Despite potential challenges in workforce adaptation and resistance, the integration of Industry 4.0 with RCM emerges as a transformative strategy.

Effect of Stress Voltage and Temperature on the Reliability of AlGaN/GaN HEMTs for RF and Microwave Application

The mean‐time‐to‐failure (MTTF) of AlGaN/GaN high electron mobility transistor is demonstrated by considering both voltage and temperature dependent electrical degradation in on‐wafer devices. The characteristics of threshold voltage shift (ΔVT) and gate leakage current (Ig_leak) after off‐state step and VDS = 0 step stress test are reported. The devices under test are being DC stressed (high operation life‐time test) in semi‐on‐state conditions at constant power dissipation of 2 W mm−1 for more or less than 200 h until the maximum drain current (Idmax) drops by 15%. Under low stress voltage (10 V), we find activation energies (Ea) and voltage acceleration factor (γ) to be 0.32 eV and 0.09 V−1, respectively, while increasing stress voltage up to 20 V results in increased activation energies of 0.68 and 0.16 eV. The MTTF is estimated to be 1.16 × 104 h at VDS = 20 V, while stressing at low bias gives a high MTTF of 2.20 × 104 h at VDS = 10 V.

Comparison between the multi-shaft and the single-shaft combined cycle power plants

<span lang="EN-US">A multi-shaft system has been adopted by power plants in Thailand for decades; however, for newer units, single-shaft systems have become more popular. This paper compared reliability measures—the system means failure rate, system mean repair rate, and system unavailability indices—between multi-shaft and single-shaft combined cycle power plants (CCPPs). The results showed that the long-term system availability of one multi-shaft CCPP was higher than each of the single-shaft units by 40.70%. In addition, the multi-shaft unit had a 42.14% longer mean time to failure (MTTF) than the single-shaft unit. Finally, the multi-shaft CCPP had 34.59% higher expected capacity than that of the single-shaft system.</span>

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.

Formula omitted]-Coverage Reliability for Wireless Multihop Network incorporating Boundary Effect

Coverage is one of the crucial performance measures for Wireless Multihop Networks (WMN) and is widely explored by researchers. The coverage performance of the network indicates how effectively the deployed network is able to provide the required coverage for a given period of time. The coverage performance of a finite network is influenced by different factors such as the presence of obstacles in the signal propagation path, the shape and size of the network deployment region, sensor node characteristics, and many others. Therefore, it is necessary to compute the performance of the network before its actual deployment in the desired region. The existing paper proposes an analytical solution for the network coverage considering sensor node failure and boundary effect using three different sensing models i.e., Boolean, Shadow Fading, and Elfes, separately. The Boolean Sensing Model (BSM) is deterministic and ignores the channel randomness, whereas the other two are probabilistic sensing models and are more realistic as they also consider the channel randomness caused by environmental factors. In the existing research, coverage estimation is determined separately for each individual model. However, coverage estimation for all three models and coverage reliability under boundary conditions have not been explored in the literature yet. Coverage reliability is another crucial metric determining how long a network can provide the required coverage. This paper aims to compute the Network Coverage Reliability (NCR) of a WMN deployed in given environments and with sensor node failure conditions. We have proposed an analytical solution to estimate the NCR of a WMN deployed in a finite circular region considering boundary effect using all three models viz., BSM, Shadowing & Multipath Fading (SMF), and Elfes Sensing Model (ESM) assuming sensor node failure condition. The Mean Time to Failure (MTTF) parameter has been determined for all three models and based on that the value of NCR is estimated. The proposed work considers κ-coverage reliability with 1≤κ≤3 where κ is the number of distinct sensor node required to cover the target and render desired NCR for different path fading and non-fading sensing models. The analytical results are validated through simulation runs and found to be consistent. Further, we have analyzed the impact of different parameters, such as the number of sensor node, sensing range, SMF parameters, sensor failure rate, and required reliability on the κ-coverage and κ-coverage reliability, referred to as NCR.

Time dependent performance analysis of a Smart Trash bin using state-based Markov model and Reliability approach

In this paper, the authors have developed a quantitative mathematical model to assess the performance of a smart trash bin. This model takes into account six hardware components of the trash bin, including Arduino Uno, ultrasonic sensor, servo motor, switch, battery, jumper wires utilizing Markov modelling and reliability-based approach. The objective of this model is to facilitate timely planning of repair and maintenance activities, ensuring prolonged availability of the smart trash bin after identifying the weakest component/components of the system.The efficient functioning of all components in the smart trash bin is imperative for the timely transmission of the garbage level data to the municipal corporation. To achieve this, the paper focuses on evaluating key reliability metric for smart trash bin system, specifically reliability, unreliability, and Mean time to Failure (MTTF). The modelling approach employs a state-based Markov model, from which Kolmogorov differential equations are derived and subsequently solved using Laplace transformation. After collecting data from organization and experts of the field explicit expression for system reliability, unreliability and MTTF have been obtained.Furthermore, this study includes sensitivity analysis of the system to pinpoint critical components which is switch and servo motor, requiring attention for proper maintenance. The research finding indicates that reliability and MTTF of the system diminish as the failure rate of the components increase under certain conditions.

Fuzzy Evaluation Model of Machining Process Loss

In facing the many negative impacts of global warming on the earth’s environment, the machining industry must reduce the rates of product rework and scrap in the manufacturing process by enhancing the process quality of the processed product. According to the concept of the Taguchi loss function, the closer the measured value of the processed product is to the target value T, then the longer the mean time between failures (MTBF) of the product. Clearly, raising the process quality of the processed product can effect energy saving and waste reduction during production and sales, which can help enterprises fulfill their corporate social responsibilities. On the basis of the Taguchi loss function, this study used the process expected loss to evaluate the process loss. Next, the process expected loss was used as an evaluation index, in which the accuracy index and the precision index can help the machining industry find the direction for improvement. Additionally, this study first derived a confidence interval of the process expected loss. Then, it was built on the confidence interval, and a confidence interval-based fuzzy test was developed for the process expected loss. Finally, an empirical example was adopted to explain the application of the fuzzy evaluation model of the machining process proposed in this paper.

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 Analysis of 3 Phase Generator Set as An Emergency Power Supply If There are Electricity Outages at PT. Intracawood Manufacturing

The need for electricity is a problem that occurs every year, especially in environments that really need a very high supply of electricity, such as companies, industries, offices, and lecture buildings. To maintain the continuity of the supply of electrical energy in the event of a disturbance, it is necessary to back up the generating system. As with the operation of a power generation system, it is also necessary to maintain the continuity and reliability of this backup power plant. To determine the level of reliability and availability, equipment data, operational data and damage data are needed. This study calculates the specified operating time (SOT), the total number of damages and the number of generators that are not operated due to routine or scheduled maintenance needs. calculating maintenance hours (S) and total maintenance time for each generator within a predetermined period. From SOT minus the total maintenance time, you will get the actual operating time (AOT) value of each generator. SOT and AOT data are needed to calculate the level of availability of generators individually and the level of availability of generators as a backup power supply. Furthermore, this research calculates the mean time between failure (MTBF) for each generator. The results of the study show that in 2022 the reliability level of PT. Intracawood Manufacturing by 99%. The generator set reliability level is included in the group that rarely experiences disturbances or damage (R≥ 90%) and the average generator set availability rate in 2022 is 98.5%. This study shows that the operating target of the power generation system at PT. Intracawood Manufacturing rarely experiences interruptions.

Reliability analysis of floating offshore wind turbine generator based on failure prediction and preventive maintenance

Aiming at the failure problems of a floating offshore wind turbine (FOWT) generator, a preventive maintenance model based on failure prediction is established. Firstly, the fault tree method is adopted to calculate the generator reliability value under run or failure states. Secondly, quantitative analysis of the generator in a maintainable state is carried out by the Markov method. Finally, a system optimization model is established based on the failure prediction and preventive maintenance (FPPM) strategy. The single-objective teaching-learning-based optimization (TLBO) algorithm and multi-objective genetic algorithm (MOGA) are respectively used to solve the optimization problem. The results show that FPPM strategy can greatly improve the availability and mean time between failure (MTBF) value, which increase the system working time and efficiency. Multi-objective analysis can get the best combination of the system maintenance cost, availability, and risk than single-objective. The time interval of failure prediction is calculated as 19788 h and the system is in a safe state if the remaining life is predicted to be more than 101 h. Sensitivity analysis of the model is carried out to determine that the optimization objectives are less sensitive to variables, which verifies the rationality and accuracy of the proposed model.

Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

In a mass customisation manufacturing system, the production schedule is tailored to the customer's specifications. However, the production system must be accompanied by an effective maintenance program to ensure that the production lines operate as intended. The purpose of this study is to optimise planned preventive maintenance across multiple production lines. An optimised Weibull distribution is proposed to model the machine's Mean Time Between Failures (MTBF), and the total expected maintenance cost is calculated using this distribution, taking into account the probability of the machines remaining operational and failing. Because the optimised Weibull distribution is a continuous distribution, in order to simulate the continuous time domain, it will be divided into several sub-systems and optimised using Bayesian optimisation during simulation. The maintenance scheduling is carried out by considering available time capacity after production scheduling was arranged. The study's findings indicate that the proposed method successfully optimised the planned maintenance schedule without interfering production activity with total cost for the proposed maintenance planning as low as IDR 50.017,75/maintenance unit time.

Digital Control and Management of Water Supply Infrastructure Using Embedded Systems and Machine Learning

Water supply infrastructure operational efficiency has a direct impact on the quantity of portable water available to end users. It is commonplace to find water supply infrastructure in a declining operational state in rural and some urban centers in developing countries. Maintenance issues result in unabated wastage and shortage of supply to users. This work proposes a cost-effective solution to the problem of water distribution losses using a Microcontroller-based digital control method and Machine Learning (ML) to forecast and manage portable water production and system maintenance. A fundamental concept of hydrostatic pressure equilibrium was used for the detection and control of leakages from pipeline segments. The results obtained from the analysis of collated data show a linear direct relationship between water distribution loss and production quantity; an inverse relationship between Mean Time Between Failure (MTBF) and yearly failure rates, which are the key problem factors affecting water supply efficiency and availability. Results from the prototype system test show water supply efficiency of 99% as distribution loss was reduced to 1% due to Line Control Unit (LCU) installed on the prototype pipeline. Hydrostatic pressure equilibrium being used as the logic criteria for leak detection and control indeed proved potent for significant efficiency improvement in the water supply infrastructure.

Multi-objective optimization of two-stage AC–DC power supply for reliability and efficiency using NSGA-II and meta-heuristic honey bee algorithms

This paper introduces a novel approach to simultaneously optimize the reliability and efficiency of a switching power supply (SPS) using the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Honey Bee Algorithm (HBA). The proposed method uses a Markov model to evaluate the reliability of the converter and maximizes objective functions, including efficiency and reliability, using optimization algorithms to identify the optimum values of the converter parameters. The optimization algorithm determines the optimal values for converter parameters, such as switching frequency, transformer turn ratio, and DC-link voltage. It also fine-tunes circuit components, such as the power factor correction inductor, DC-link capacitor, and output filter components. A sensitivity analysis is carried out to assess the impact of various parameters on the multi-objective functions of the converter. The converter’s reliability and mean time to failure (MTTF) are evaluated using the Markov reliability model, considering all components’ short and open circuit faults. The failure rates of converter components are calculated using the MIL-HDBK-217 methodology. The results obtained from the NSGA-II and HBA are compared to identify the superior algorithm with the best-optimized parameters. Simulation results indicate that the proposed optimization technique enhances the converter’s reliability performance while maintaining its efficiency. Furthermore, this paper presents comprehensive analyses, comparisons, and experimental findings to demonstrate the converter’s fault-tolerant capability. The multi-objective optimization approach offers a practical and effective strategy for optimizing switching power supplies for efficiency and reliability

Quality assessment and lifetime prediction of base metal electrode multilayer ceramic capacitors: Challenges and opportunities

Base metal electrode (BME) multilayer ceramic capacitors (MLCCs) are widely used in aerospace, medical, military, and communication applications, emphasizing the need for high reliability. The ongoing advancements in BaTiO3-based MLCC technology have facilitated further miniaturization and improved capacitive volumetric density for both low and high voltage devices. However, concerns persist regarding infant mortality failures and long-term reliability under higher fields and temperatures. To address these concerns, a comprehensive understanding of the mechanisms underlying insulation resistance degradation is crucial. Furthermore, there is a need to develop effective screening procedures during MLCC production and improve the accuracy of mean time to failure (MTTF) predictions. This article reviews our findings on the effect of the burn-in test, a common quality control process, on the dynamics of oxygen vacancies within BME MLCCs. These findings reveal the burn-in test has a negative impact on the lifetime and reliability of BME MLCCS. Moreover, the limitations of existing lifetime prediction models for BME MLCCs are discussed, emphasizing the need for improved MTTF predictions by employing a physics-based machine learning model to overcome the existing models’ limitations. The article also discusses the new physical-based machine learning model that has been developed. While data limitations remain a challenge, the physics-based machine learning approach offers promising results for MTTF prediction in MLCCs, contributing to improved lifetime predictions. Furthermore, the article acknowledges the limitations of relying solely on MTTF to predict MLCCs’ lifetime and emphasizes the importance of developing comprehensive prediction models that predict the entire distribution of failures.

Impact of aging on the SEU immunity of FinFET-based embedded memory systems

Due to the rapid technology evolution, embedded computing systems have become a pervasive part of daily life, used for tasks ranging from cost-driven edge computing in the Internet of Things (IoT), up to high-end critical applications (such as aerospace and defense). However, no matter which electronic system the memories are embedded in, they tend to occupy up to 90 % of the total area of the silicon wafer. On the other hand, accelerated aging (bias-temperature instability: BTI) due to technology scaling has become a major factor in degrading memory immunity to transient faults (single-event upsets: SEUs). In this scenario, memory reliability concerns have been brought to the forefront. In the light of the above mentioned, this paper analyses the impact of aging on the SEU immunity of 14 nm FinFET SRAM cell-based embedded memory systems. Experimental results based on HSpice simulations have shown that under certain operating conditions, for 10-year life span, the 6 T SRAM cell SEU immunity can degrade by 58.9 % with respect to the fresh cell (not aged). Moreover, cell aging is strongly dependent on parameters like the temperature in which the cell is operating, as well as the frequency by which the content stored in the cell is inverted during its lifetime. A 256MByte memory system was designed. Simulations indicate that the Mean Time Between Failures (MTBF) of this memory system can degrade up to 23.4 % after a period of 10 years.