Mean Time To Failure Research Articles

AI-powered Predictive Maintenance for Solar Energy Systems: A Case Study

This article presents the implementation of an AI-powered predictive maintenance system at SolarTech Solutions' 75MW solar installation in Arizona, examining the transformation from traditional reactive maintenance to an advanced predictive maintenance framework utilizing IoT sensors and artificial intelligence. The implementation, spanning 18 months, integrated 12,000 distributed sensors with a sophisticated AI/ML infrastructure, achieving remarkable results across multiple dimensions: 94.3% accuracy in anomaly detection, 98.2% precision in fault localization, and a 47% reduction in unplanned downtime, generating annual savings of $425,000. Key operational improvements include a 64% increase in Mean Time Between Failures (MTBF), reduction in maintenance response time from 72 to 4 hours, and a 3.2% improvement in panel efficiency. The system's environmental impact is equally significant, contributing to a reduction of 1,960 metric tons of CO2 emissions annually and conserving 1.2 million gallons of water per year. The article demonstrates the viability and effectiveness of AI-driven maintenance solutions in utility-scale solar operations, providing a comprehensive framework for similar implementations across the renewable energy sector, while addressing critical challenges in deployment, integration, and optimization of these advanced systems.

Prediction of the Mean Time to Failures of a Complex System Using the Monte Carlo Simulation Method

This paper presents a Monte Carlo-based algorithm for predicting the Mean Time to Failure (MTTF) of complex structures, specifically a “Bridge-type network” with five elements exhibiting various failure distributions. The proposed algorithm involves generating element lifetimes through the inverse of their failure distribution functions, providing a robust approach to evaluating MTTF for systems beyond traditional series or parallel configurations. The approach was implemented in MATLAB, and the software underwent extensive testing across different scenarios, including both exponential and Weibull distributions. The results demonstrated the method’s accuracy and its capability to handle diverse failure distributions with minimal error. This tool offers reliability engineers a versatile solution for predicting and improving the reliability of complex systems. In summary, the proposed method and software significantly advance the reliability assessment of intricate structures and offer a solid foundation for further research and practical applications in the field of reliability engineering.

Comparison of Iridex MP3 and P3 probes on long-term intraocular pressure control after MicroPulse Transscleral-Cyclophotocoagulation

PurposeMicropulse transscleral cyclophotocoagulation (MP-TSCPC) is a glaucoma procedure that delivers energy to the ciliary body with minimal collateral-damage. We aim to assess the long-term effectiveness of MP-TSCPC using the original MP3 and revised P3 probe. Methods and designBaseline Retrospective cohort study was performed to obtain baseline characteristics, intraocular pressure (IOP), number of IOP-lowering medications, and best-corrected visual acuity (BCVA) were obtained from pre- and post-operative visits of patients treated with MP-TSCPC. Failure was defined as IOP>21 mmHg, reduced IOP<20% below baseline, or IOP<5 mmHg on 2 consecutive follow-up visits after post-operative month 1, reoperation for glaucoma, or loss of vision to no light perception. ResultsA total of 113 eyes were enrolled, 44 using the MP3 probe and 69 with the revised P3 probe. Survival analysis found a 75% failure rate in patients treated with the revised P3 probe by the final visit with a mean time to failure (MTTF) of 4.8 ± 0.7 months which was higher than the original probe at 66% failure rate with a MTTF of 6.1 ± 1.1 months (p = 0.04). All causes of failure were due to high IOP and 67% required repeat treatment with MP-TSCPC or incisional surgery. Prior to censure of data from failure, both probes had significant reduction in IOP compared to pre-operative IOP at all time points. No significant changes in BCVA were found. Both groups were associated with low rates of complications. ConclusionBoth probes had a very low rate of visually significant complications when performed in a standardized method in accordance with surgical procedure and power settings recommended by Iridex; however, were associated with a high rate of single procedure failure due to uncontrolled IOP over the long term. The revised P3 probe was associated with a higher rate of failure. Patients experienced a reduction in IOP; however, a large proportion required additional treatment compared to the original MP3 probe.

Risk analysis of urea manufacturing plant using fuzzy logic approach

The potential hazard element of chemical process industries has become severe with technological developments over the past few years. Fertilizer industries like the urea industry deal with highly toxic components in nature, causing severe consequences. This paper implements the fuzzy lambda-tau (λ-τ) approach with the Petri net (PN) model on a urea manufacturing plant. The PN model helps to reduce the complexity of the problem, whereas fuzzy logic helps to deal with the uncertainty and vagueness of data. The urea plant has been divided into three major units, that is, the reacting unit (RU), purifying unit (PU), and concentrating unit (CU). The different reliability parameters, such as failure rate, repair time, reliability, and mean time between failures (MTBF), are determined for all these units with different spread rates (±15%, ±25%, and ±35%). A significant change of around 75% and 9% was observed for overall repair time and MTBF, respectively, for a ±35% spread rate. The reliability parameters are found as a function of the square of the spread rate. These studies suggest that the PU of the urea plant is the most vulnerable one and thus requires timely maintenance to avoid any accidents in the future. Furthermore, a detailed qualitative study is done utilizing failure mode and effect analysis (FMEA) to outline the numerous failure causes in order to enhance the system's availability and maintainability. The limitations of conventional FMEA are addressed in this study by obtaining fuzzy RPN values after application of the fuzzy FMEA technique. On comparison of the RPN and FRPN values, the FRPN values are found to be more reliable while prioritizing the failure causes in process industries.

Improving Fault Tolerance for FPGA SoCs through Post-Radiation Design Analysis

FPGAs have been shown to operate reliably within harsh radiation environments by employing single-event upset (SEU) mitigation techniques, such as configuration scrubbing, triple-modular redundancy, error correction coding, and radiation aware implementation techniques. The effectiveness of these techniques, however, is limited when using complex system-level designs that employ complex I/O interfaces with single-point failures. In previous work, a complex SoC system running Linux applied several of these techniques only to obtain an improvement of 14 \(\times\) in mean time to failure (MTTF). A detailed post-radiation fault analysis found that the limitations in reliability were due to the DDR interface, the global clock network, and interconnect. This article applied a number of design-specific SEU mitigation techniques to address the limitations in reliability of this design. These changes include triplicating the global clock, optimizing the placement of the reduction output voters and input flip-flops, and employing a mapping technique called “striping.” The application of these techniques improved MTTF of the mitigated design by a factor of 1.54 \(\times\) and thus provides a 22.8X \(\times\) MTTF improvement over the unmitigated design. A post-radiation fault analysis using BFAT was also performed to find the remaining design vulnerabilities.

Assessing the Bridge Structure’s System Reliability Utilizing the Generalized Unit Half Logistic Geometric Distribution

Reliability is now widely recognized across various industries, including manufacturing. This study investigates a system composed of five components, one of which is a bridge network. The components are assumed to follow the generalized unit half logistic geometric distribution (GUHLGD) with equal failure rates over time. The following three improvement methods are considered: reduction, cold duplication, and hot duplication. The reliability function and mean time to failure (MTTF) are employers liability equivalence factors (REFs). Additionally, the λ fractiles of both the original and enhanced systems are obtained. Numerical results illustrate the effectiveness of these techniques, with cold duplication shown to be the most effective, offering higher reliability and MTTF compared to hot duplication. The enhanced system outperforms the original system overall.

Methodology of Shipboard Spare Parts Requirements Based on Whole Part Repair Strategy

This paper introduces an assessment method for shipboard spare parts requirements based on a whole-part repair strategy, aimed at enhancing the availability and combat effectiveness of naval equipment. Addressing the shortcomings of traditional repair strategies, this study innovatively adopts a whole-part rotation repair approach to reduce repair times and improve the rapid response capability of equipment. An evaluation model for support probability and fill rate is established, and Monte Carlo simulation techniques are applied to simulate the impact of different maintenance strategies on spare parts demand and equipment availability. This study also conducts a sensitivity analysis of key parameters, including Mean Time Between Failures (MTBF), repair demand probability, and faulty part repair cycle, to assess their influence on spare parts requirements and equipment availability. The results indicate that the whole-part repair strategy can effectively reduce spare parts demand and enhance equipment availability. In conclusion, the whole-part repair strategy demonstrates a distinct advantage in shipboard spare parts management, optimizing inventory management while ensuring combat readiness. This research provides a novel analytical approach for naval logistics and maintenance planning.

Analyzing component failures in series-parallel systems with dependent components

This paper investigates a series-parallel system comprising N independent subsystems with interchangeable dependent components, a prevalent reliability structure in engineering and network design. The primary aim of this research is to derive the joint probability distribution of the number of failed components within these configurations, considering component dependence and varying distributions across subsystems. This approach reflects a more realistic scenario than previously explored in the literature. Initially, the analysis is conducted for systems with two subsystems and subsequently extended to encompass configurations with N subsystems. The study also evaluates key reliability metrics including the average number of failed components and the mean time to failure (MTTF) of the entire system, theoretically proving that the system’s MTTF increases with the number of components under certain sufficient conditions. In addition to probabilistic analysis, an optimization problem is addressed to determine the optimal allocation of components within each subsystem. The objective is to minimize the average cost associated with corrective maintenance, thereby enhancing the cost-effectiveness of system operation.

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.

A cumulative damage model for tubes in nuclear power plants subject to continuous degradation and multi-effect shocks under dynamic environments

Continuous degradation and shocks are two vital damage mechanisms for tubes under dynamic environments in nuclear power plants (NPPs). In this work, we presented a cumulative damage model to evaluate the reliability of tubes in NPPs, which accounts for continuous degradation and random shocks. The dynamic environments were assumed to obey a homogenous Markov process. Continuous degradation was described as a general stochastic process with independent increments. A multinomial distribution was used to depict the multiple effects of Poisson-distributed shocks on tubes. The influence of dynamic environments on the cumulative damage process of tubes was also explicitly modeled. Closed-form reliability measures were derived, such as reliability functions and mean time to failure (MTTF) of tubes. Drawing upon a case study from the existing literature, the analytical solution was validated through a comparative analysis with the outcomes of Monte Carlo simulation (MCS) and other methodologies referenced in the literature. As a practical engineering application, the reliability of steam generator tubes made of Alloy 690 within pressurized water reactors (PWRs), which are susceptible to primary side stress corrosion cracking (PWSCC) and the effects of water hammer, was calculated. The findings indicate that the probabilities of steam generator tube rupture (SGTR) as estimated by the model and those inferred from operational experience have the same order of magnitude.

Effective rare-earth doping on semiconductor behavior for BaTiO3-based automotive MLCCs

Highly accelerated lifetime test (HALT) for lifetime evaluation was performed on the state-of-art automotive MLCC prototypes by varying Dy/Mg (donor/acceptor) ratio added to BaTiO3. The results clearly showed that the mean time to failure (MTTF) more than 280 times as the Dy/Mg ratio increased from 1.0 to 10.0. Since oxygen vacancies typically form in-gap state/defect at the donor level, the activation energy value under thermal activation process as a function of voltage was calculated and compared by non-destructive testing (I–V curve) for an accurate evaluation of the extrinsic behavior. It was found that barrier height at the Ni/BT interface decreases as the voltage increases, resulting in a decrease in activation energy. As the Dy/Mg ratio increases, the density of defects/in-gap states formed at the donor level in the bandgap by oxygen vacancies decreases, which may lead to a decrease in the number of electrons excited by the external voltage. Furthermore, it was verified that the calculated Schottky barrier height of the 10.0 Dy/Mg ratio under voltage has higher value than that of the 2.6 Dy/Mg ratio. Based on the results of this study, we propose a new indicator for the design of automotive MLCCs with high lifetime reliability that can be used for comparative analysis of additive compositions through non-destructive test (I–V curve) with low evaluation time/cost.

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.

Highly accelerated life testing (HALT): A review from a statistical perspective

AbstractDespite its use in one form or another for at least four decades, HALT and related techniques [e.g., highly accelerated‐stress screening (HASS) and stress audits (HASA)] are not well understood within the statistical community and remain controversial. This largely reflects a conflict in motivation between engineers, testing under harsh conditions to discover and eliminate failure modes, and statisticians, taking a more cautious approach to develop quantitative estimates of parameters such as mean time between failures (MTBF). This review article will clarify HALT concepts and methods and explain where it fits within the universe of methods that involve the application of accelerating factors to compress the time required to evaluate or enhance product reliability. A major distinction is between methods such as HALT, a high‐stress test‐analyze‐fix‐test iterative process directed at improving reliability by discovering and fixing weak points in a design, and quantitative accelerated life testing (QALT), whose goal is the estimation of product life for a fixed design. We discuss methods such as physics of failure that offer some hope of bridging the gap between the qualitative nature of HALT, and purely quantitative statistical methods. We present a variety of engineering applications of HALT including metal fatigue, piping and pressure vessels, structural damage, radiation damage, and rotating machinery. We also discuss potential synergies between HALT and QALT, such as rapid identification, through HALT, of failure modes requiring quantitative analysis. For further study, extensive references to the applicable literature are provided as well as an appendix that describes related methods.This article is categorized under: Statistical and Graphical Methods of Data Analysis &gt; Reliability, Survivability, and Quality Control

Electromigration Effects in Overcurrent PVC-Insulated Copper Wire: Failure and Deformation Impacts

Electromigration is a critical issue in materials science and electrical engineering, significantly impacting the reliability and efficiency of electrical systems. This study investigates the electromigration behavior of PVC-insulated copper wires under various overcurrent conditions, focusing on material degradation and electrical performance. Copper cables, identified as 046620.3 Eterna CU/PVC 1.5 mm2, were subjected to currents ranging from 0 to 110 A. The mean time to failure (MTTF) was calculated using Black’s equation, revealing a sharp decline in MTTF with increasing current density. Surface morphology analysis using SEM showed the formation of voids and hillocks at higher currents, indicating severe electromigration damage. XRF analysis demonstrated significant changes in the elemental composition, particularly a reduction in copper content and an increase in chlorine and other elements, suggesting degradation of the PVC insulation. FTIR spectroscopy revealed substantial chemical changes in the PVC material, especially under extreme overcurrent conditions, highlighting dehydrochlorination and carbonyl group formation. There is a clear relationship between overcurrent conditions and electromigration phenomena, as evidenced by the observed damage to surface morphology, changes in elemental composition, and alterations in the chemical structure of PVC. The mechanisms and causes of electromigration are explained comprehensively in this work, illustrating how increased overcurrent accelerates the electromigration process, leading to the formation of voids and hillocks in the copper conductor. This damage is accompanied by a significant reduction in copper content and an increase in chlorine levels, indicating the degradation of PVC insulation. FTIR spectra further confirmed these findings by showing chemical changes such as dehydrochlorination and carbonyl group formation under high current stress. The MTTF values reflect the severity of these impacts, with samples exposed to higher currents showing drastically reduced lifespans. For instance, samples subjected to 100 A and 110 A currents exhibited MTTF values of 0.2 minutes and 0.004 minutes, respectively.

Penjadwalan Pemeliharaan Mesin Produksi Menggunakan Reliability Centered Maintenance

CV. ABC is a furniture business that produces home equipment which includes all items, namely chairs, tables, cupboards, beds, nightstands, wooden stairs, etc. The aim of this research was to determine maintenance scheduling for production machines, namely wood cutting machines and drilling machines, and provide suggestions for improving the maintenance scheduling of production machines, namely wood cutting machines and drilling machines, which are more effective. The advantage of the Reliability Centered Maintenance (RCM) method is that it prioritizes the main action of preventive maintenance, namely preventing and minimizing the consequences of failures that arise, so that it can increase the reliability and safety of the equipment used. Failure Modes and Effects Analysis (FMEA) is a technique for evaluating the level of reliability of a system to determine the effects of failure of the system. Based on the results of data processing and research discussion regarding production machine maintenance scheduling using RCM at CV. ABC, a conclusion can be drawn, namely, the Cutting Machine has a breakdown frequency of 26 times with the Bearing component having the most breakdown frequency, namely 8 times, the highest Risk Priority Number (RPN) calculation is Solenoid component with a value of 105, Mean Time to Failure (MTTF) calculation with a value of 111.5385 and Mean Time to Recovery (MTTR) with a value of 0.769231. The drilling machine has a breakdown frequency of 24 times with the Drill Bit component having the highest breakdown frequency, namely 6 times, the highest RPN calculation is the Drill Bit component with a value of 105, MTTF calculation with a value of 120.8333 and MTTR with a value of 0.8333.

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.

The Robust Classical MTBF Test

For the reliability demonstration of a repairable system, testers expediently assume that the rate of occurrence of failures (ROCOF) can be modeled by a homogenous Poisson process (HPP), also called an ordinary renewal process (ORP), and therefore the times between failure, or interarrival times, are exponentially distributed with a constant mean time between failures (MTBF). This often-used test is colloquially called the “classical MTBF test” because it has been widely used by both government and industry for the past 50 years. The test is simple; operate a system for a period and the sample estimate for MTBF is the operating time divided by the number of failures. A confidence interval is applied to the sample estimate to determine the test result. This paper considers the validity of the test result if the repairable system has an increasing or decreasing MTBF, that is, a nonhomogeneous Poisson process (NHPP). Specifically, the change in consumer’s risk is analyzed if the repairable system has a decreasing MTBF (increasing ROCOF). A simple risk mitigation strategy consisting of a burn-in period equal to the threshold MTBF is also considered and found to be effective. The classical MTBF test is shown to be a robust test and a burn-in period prior to testing makes the classical MTBF test even more robust.

Assessment of reliability measures and cost optimization of a solar seed sowing machine

PurposeIn this research work, the general form of reliability measures, which include availability, mean time to failure (MTTF), and sensitivity analysis, are investigated with their graphical representation, which would help designers and engineers improve the reliability of the system. Along with reliability assessment, a mathematical model is developed and solved to achieve the minimum cost of the system as well as maximum reliability.Design/methodology/approachIn the proposed work, a general model of a solar seed sowing machine is considered for reliability evaluation using the Markov model process. The proposed system is a series-parallel arrangement of components where three components, namely the solar panel, batteries and direct current (DC) motor, are connected in series while all the operators are connected in parallel. The implemented Markov model approach assesses several parameters of reliability, which opens the scope for improvement in reliability and other measures like mean time to failure (MTTF) and sensitivity of the proposed system. So that the machine can deliver the desired output on the field. Also, the particle swarm optimization (PSO) algorithm is applied to optimize the cost of the system with the desired level of reliability.FindingsImplementation of PSO provides the optimal cost for the proposed system with a predetermined level of reliability, which shows the relationship between reliability and cost of the system. Also, the Markov process approach provides the availability function, reliability function, reliability at different time values, MTTF and sensitivity of the proposed system.Originality/valueThis work evaluates the crucial characteristics of reliability for the proposed solar seed sowing machine using the Markov model which is a stochastic model. The assessment of reliability measures such as MTTF, availability and sensitivity plays a vital role in measuring and improving the performance of the machine in the actual environment. Examining the data obtained in this research is of great importance for the manufactures and system designers. Also, the powerful optimization technique PSO is implemented to solve a non-linear mixed-integer programming problem that provides the optimal cost for the system with desired reliability.

Reliability Performance Improvement of the Storage System by Using Weibull and Linear-Exponential Distributions

This research paper delves into the theory of reliability and its applications in various fields, particularly in engineering manufacturing. The study aims to address the problem of enhancing the reliability and performance of storage systems by incorporating two types of components with different distributions. These components are assumed to be identical, but their failure rates vary over time. Type (a) components follow a Weibull distribution, while type (b) components have a Linear-exponential distribution. The research examines different characteristics of the system, including the mean time to failure (MTTF), [Formula: see text]-fractiles, and equivalence factor of reliability (EFR). To improve the storage system’s performance, the reduction, warm duplication and cold duplication techniques are implemented. Further, the cold duplication approach outperforms warm duplication and reduction methods. Before the improvement, the original system generally showed the lowest level of reliability. The results indicate that these techniques are effective, and the study provides numerical data to support this finding.

Influence of Sn grain orientation on mean-time-to-failure equation for microbumps in 3D IC technology

The mean-time-to-failure (MTTF) associated with microbumps is a pivotal factor influencing the reliability of cutting-edge consumer electronic devices. Within Sn-based solder joints, the orientation of Sn grains significantly impacts the diffusion velocity of Cu and Ni atoms, thereby impacting MTTF. While a large solder joint encompassing numerous Sn grains can be treated as isotropic, the scenario shifts in a small microbump with just one Sn grain, rendering it anisotropic. In such cases, the Sn grain orientation emerges as a dominant factor dictating the microbump's longevity. Herein, we investigated the influence of Sn grain orientation on MTTF of electromigration, thermomigration, and thermal aging. A new parameter, α, was introduced, which signifies the angle between the c-axis of the Sn grain and the direction of external stressing.