Mean Time To Failure Research Articles

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.

Reliability Assessment of On-Wafer AlGaN/GaN HEMTs: The Impact of Electric Field Stress on the Mean Time to Failure.

We present the mean time to failure (MTTF) of on-wafer AlGaN/GaN HEMTs under two distinct electric field stress conditions. The channel temperature (Tch) of the devices exhibits variability contingent upon the stress voltage and power dissipation, thereby influencing the long-term reliability of the devices. The accuracy of the channel temperature assumes a pivotal role in MTTF determination, a parameter measured and simulated through TCAD Silvaco device simulation. Under low electric field stress, a gradual degradation of IDSS is noted, accompanied by a negative shift in threshold voltage (ΔVT) and a substantial increase in gate leakage current (IG). Conversely, the high electric field stress condition induces a sudden decrease in IDSS without any observed shift in threshold voltage. For the low and high electric field conditions, MTTF values of 360 h and 160 h, respectively, were determined for on-wafer AlGaN/GaN HEMTs.

Modeling and estimation of the reliability of electrical components for autonomous driving

AbstractFrom automated to autonomous cars, a higher and higher reliability of the electrical components is required, while on the other hand the number of electrical components is continuously increasing in modern cars. Therefore for these components the estimation of a mean‐time‐to‐failure (MTTF) and a failure rate, a so‐called failure‐in‐time (FIT) rate, and the quantification of the corresponding uncertainties is crucial. The verification of the required FIT rates of less than one failure in one billion hours of lifetime is a challenge.Risk estimates have to be determined before and within the development process of future automated/autonomous cars. However, tests in real time take too long and are too expensive. As the next step, the uncertainty quantification of networks of components as in the vehicle electric system or a battery (with cells and management systems) are to be considered as well.This paper discusses several options for the determination of these failure rates. Following an overview we focus on the approach of experimental overstress tests and the combination of overstresses varying in time. Mathematically, the latter means a switching between different probability distributions in time. We state how to combine probability distributions that are typical for MTTF. Results for step functions in time as well as continuous changes in time of the probability distribution are presented. The main result is that the order of the temporal stresses is essential. This calls into question whether the common practice using temperature collectives is applicable here.

Effect of reduction method on the performance a software defined network system using Gumbel Hougaard family copula distribution

This study examines a system that consists of three subsystems 1, 2 and 3. These three subsystems are each linked together in series. Subsystem 1’s three units are wired in series, subsystem 2’s three units are wired in parallel and operating under the 2-out-of-3: G; policy, and subsystem 3’s three units are wired in parallel and operating under the 1-out-of-3: G; policy. Units and subsystems failure rates are constant and follow an exponential distribution. The repair rate follow two types of distributions, namely general and Gumbel Hougaard family copula distribution. The system was studied using Laplace transforms and supplementary variable methods. For specific values of the failure and repair rates, availability, reliability, mean time to failure (MTTF), and cost analysis have been assessed. As a means to improve the system’s overall effectiveness and availability, a reduction strategy is utilized. Tables and graphs are used to display computed results.

M2STaR: A Multimode Spatio-Temporal Redundancy Design for Fault-Tolerant Coarse-Grained Reconfigurable Architectures

Coarse-grained reconfigurable architectures (CGRAs) can provide both energy efficiency and performance for embedded systems, and thus they are increasingly deployed in the areas of aerospace, automotive engineering, and security where reliability is also a main criterion. However, the state-of-the-art fault-tolerant strategies for CGRAs apply either temporal or spatial scheme, including redundancy, periodic detection, workload balancing, and reconfiguration, failing to exploit the feature of dynamic and partial reconfiguration of CGRAs. Also, vulnerable judging circuits and inflexible mode shifting bottleneck the reliability design of fault-tolerant CGRAs. This paper proposes a novel multi-mode fault-tolerant framework for CGRAs, which combines spatial-redundant data-paths with temporal-redundant voters and thus reduces the vulnerable judging circuits while balancing the performance and reliability. This framework can also enable a changing reliability level at runtime via an online configuration transformation method based on pre-compiled patterns. Within the proposed framework, we systematically searched the design space spanning various combinations of the mainstream schemes with a Markov process model to compare the effectiveness and accordingly selected five points as available modes in our design after comprehensive consideration of fault tolerance and time overhead on CGRA. The framework is comprehensively evaluated on a cycle-accurate CGRA simulator, considering both permanent and transient faults. The experimental results show that the fault coverage rate of single transient faults or permanent faults has increased from 71.74% to 93.84%, which means the fault tolerance of the system has been increased by 31.03% compared with the state-of-the-art methods. There is also a great improvement in mean-time-to-failure (MTTF) and reconfiguration latency over baseline designs.

Investigation on reliability of classical industrial motor drives using GoldSim Monte Carlo reliability workbench

PurposeThe purpose of this study is to propose an extended reliability method for an industrial motor drive by integrating the physics of failure (PoF).Design/methodology/approachIndustrial motor drive systems (IMDS) are currently expected to perform beyond the desired operating conditions to meet the demand. The PoF of the subsystem affects its reliability under such harsh operating circumstances. It is crucial to estimate reliability by integrating PoF, which helps in understanding its impact and to develop a fault-tolerant design, particularly in such an integrated drive system. An integrated PoF extended reliability method for industrial drive system is proposed to address this issue. In research, the numerical failure rate of each component of industrial drive is obtained first with the help of the MIL-HDBK-217 military handbook. Furthermore, the mathematically deduced proposed approach is modeled in the GoldSim Monte Carlo reliability workbench.FindingsFrom the results, for a 15% rise in integrated PoF, the reliability and availability of the entire IMDS dropped by 23%, resulting in an impact on mean time to failure (MTTF).Originality/valueThe integrated PoF of the motor and motor controller affects industrial drive reliability, which falls to 0.18 with the least MTTF (2.27 years); whose overall reliability of industrial drive drops to 0.06 if it is additionally integrated with communication protocol.

Enhancing resilience in marine propulsion systems by adopting machine learning technology for predicting failures and prioritising maintenance activities

With the rapid transition towards Industry 4.0, many industries have recognised the need to develop intelligent maintenance systems and predictive analytics tools that are able to optimise the performance of complex assets throughout their entire lifecycle. This paper adopts a machine learning (ML) technology to enhance the resilience of marine propulsion systems by predicting mean-time between failures (MTBF) and prioritising condition-based maintenance (CBM) activities. The factors considered for the analysis include a series of attributes (in total 16 features) which directly or indirectly represent the state of the system subject to degradation. From the results obtained, it is found out that the feed-forward neural network (FFNN) has the least mean square errors (MSE) and, thus, it is the most suitable method for numerical and discretized estimations. The results from a greedy search algorithm also show that two attributes of ‘gas generator rate of revolutions’ and ‘gas turbine rate of revolutions’ are the most significant parameters influencing system reliability, and therefore, they require closer monitoring. Furthermore, a decision support system (DSS) dashboard is designed to help marine propulsion system operators make informed maintenance decisions and find an improved balance in their spare parts inventory.

Data-Driven Insights into Population Exposure Risks: Towards Sustainable and Safe Urban Airspace Utilization by Unmanned Aerial Systems

With the rapid increase in unmanned aerial vehicles (UAVs), ensuring the safety of airspace operations and promoting sustainable development of airspace systems have become paramount concerns. However, research dedicated to investigating the population exposure risks of UAV operations in urban areas and their spatial pattern is still missing. To address this gap, this study evenly divides the urban space into uniform grids and calculates critical areas for two UAV types within each grid. By integrating geospatial data, including buildings, land use, and population, data-driven risk maps are constructed to assess the spatial distribution patterns and potential population exposure risks of two UAV types and compare them with commonly used census units. The results indicate that the mean time between failures (MTBF) for the selected generic and rotary-type UAVs can be up to 9.04 × 108 h and 1.22 × 108 h, respectively, at acceptable risk levels, considering uncertainties. The spatial pattern of population exposure risk exhibits spatial heterogeneity and multi-scale effects in urban areas, aligning with population distribution. High-risk areas concentrate in regions characterized by high population mobility, such as transport hubs, commercial service areas, residential zones, and business districts. Additionally, the comparation emphasizes the potential bias introduced by using census units in risk assessment, especially in regions with significant urban build-up. This framework enables the evaluation of safety and acceptability across diverse urban land use areas and offers guidance for airspace management in megacities, ensuring the safe integration of UAVs in urban environments.

Applicability of Weibull distribution in Generating Model for Evaluating Lifespan Closed Circuit Television System (CCTV)

CCTV is an expedient tool for monitoring and investigating. Since it can record emergency and routine events in memory of the electronic circuit board. However, in the process of operating CCTV, it is necessary to carry out a sampling inspection on its lifetime to predicate its availability and reliability. According to previous literature, most of the parameter estimation methods used only one parameter estimation method, which is not sufficient for identifying two explicit parameters of Weibull distribution. Moreover, the bad parameter estimation method cannot achieve reliable analysis results. Therefore, this paper presented the four methods of estimation in both graphical and analytical methods for estimating parameters of Weibull distribution. These methods have measured the accuracy by using mean square error (MSE). In addition, the real data set confirmed that there is the Weibull distribution by using the Kolmogorov-Smirnov test. The simulation results found that the maximum likelihood method (MLM) can outperform the other estimator because it can provide a minimum MSE, there is an optimal parameter in which the shape is 4.3881 and the scale is 86.8207. Finally, the two optimal parameters are applied to real data sets of the CCTV. As a result, the optimal parameter fits with real data sets, and it can evaluate the lifespan of CCTV by using the mean time to failure (MTTF) as 6.6 years.

A Q-Learning-Based Two-Layer Cooperative Intrusion Detection for Internet of Drones System

The integration of unmanned aerial vehicles (UAVs) and the Internet of Things (IoT) has opened up new possibilities in various industries. However, with the increasing number of Internet of Drones (IoD) networks, the risk of network attacks is also rising, making it increasingly difficult to identify malicious attacks on IoD systems. To improve the accuracy of intrusion detection for IoD and reduce the probability of false positives and false negatives, this paper proposes a Q-learning-based two-layer cooperative intrusion detection algorithm (Q-TCID). Specifically, Q-TCID employs an intelligent dynamic voting algorithm that optimizes multi-node collaborative intrusion detection strategies at the host level, effectively reducing the probability of false positives and false negatives in intrusion detection. Additionally, to further reduce energy consumption, an intelligent auditing algorithm is proposed to carry out system-level auditing of the host-level detections. Both algorithms employ Q-learning optimization strategies and interact with the external environment in their respective Markov decision processes, leading to close-to-optimal intrusion detection strategies. Simulation results demonstrate that the proposed Q-TCID algorithm optimizes the defense strategies of the IoD system, effectively prolongs the mean time to failure (MTTF) of the system, and significantly reduces the energy consumption of intrusion detection.

Reliability evaluation of carrier‐based pulse width modulated three‐level F‐type neutral point clamped inverter with power loss analysis

AbstractAn improved carrier‐based pulse‐width‐modulation (CBPWM) technique for a three‐level (3L) F‐type neutral point‐clamped (FNPC) inverter is presented in this paper. By using the proposed modulation scheme, the DC‐link capacitor voltage at the neutral point is balanced. For manufacturers, reliability is one of the major concerns for all power electronics devices and parts. This paper proposes an extensive reliability analysis of the 3L‐FNPC inverter. Considering the mean time to failure (MTTF) of each device, the reliability of the 3L‐FNPC inverter is analyzed in this present work by using two different methods, which are the approximate method and the exact method. To compute the failure rate of the equipment, only the number of components is considered in the approximate method. Alternatively, exact methods are utilized to evaluate the reliability of a system by knowing the different stress levels and various operating environments of a specific system. Because of the strong correlation between device failure and temperature stress factors, power loss analysis is used in the exact method. The MATLAB Simulink environment is used to determine the power loss of the switching devices. A solar off‐grid photovoltaic (PV) system is considered for reliability testing. Experimental mission profile data are used to estimate the lifetime of the 3L‐FNPC inverter by applying the rainflow counting algorithm, the Coffin‐Manson model, and Miner's rule. The competitiveness, effectiveness, and reliability of the 3L‐FNPC inverter with the proposed modulation technique are validated using a hardware prototype.

On noise-induced transient bit flips in subthreshold SRAM

We propose a rigorous SPICE simulation framework, compatible with industrial process design kits, to observe transient bit flips in CMOS SRAM due to the intrinsic transistor noise. The parameters of transient noise simulation are selected according to the noise behaviour and bandwidth of the inverter, however under CPU-time constraint. The methodology is illustrated in 28nm FD-SOI technology. We study the combined effects of the random telegraph noise, flicker and thermal noise sources, for the first time to our knowledge. The extracted mean times to failure (MTTF) are compared to those reported in the literature in extreme voltage, temperature and variability conditions. The MTTF spans from below 0.1ms to 108s, following a theoretical trend logMTTF∝VDD2.

Penerapan Preventive Maintenance dan Pemasangan Emergency Lamp Dalam Minimasi Product Defect Akibat Heater Terhadap Produk Kemasan

Plant printing is a company unit tasked with fulfilling product packaging needs. In the production of printing plants from July to December, it was found that an average of 4% of products had defects (defects) exceeding the allowable tolerance limit of 2% per month, the biggest cause of problems with one of the machine components, namely the heater component. In these components found downtime of 3900 minutes for 3 months. Based on these problems, it is necessary to take preventive measures with a preventive maintenance approach by determining the schedule which is obtained from the calculation of the mean time to failure (MTTF) that the recommended maintenance interval is once every 5 days. Plus another improvement with the installation of an emergency lamp if the heater is indicated to be experiencing a mismatch. After the two approaches and improvements were carried out, a decrease in the number of product defects was obtained where the defect in the previous 6 months averaged 4% and in the following month after implementation the number of defects decreased to only 1.83%.

Improved prediction for failure time of multilayer ceramic capacitors (MLCCs): A physics-based machine learning approach

Multilayer ceramic capacitors (MLCC) play a vital role in electronic systems, and their reliability is of critical importance. The ongoing advancement in MLCC manufacturing has improved capacitive volumetric density for both low and high voltage devices; however, concerns about long-term stability under higher fields and temperatures are always a concern, which impact their reliability and lifespan. Consequently, predicting the mean time to failure (MTTF) for MLCCs remains a challenge due to the limitations of existing models. In this study, we develop a physics-based machine learning approach using the eXtreme Gradient Boosting method to predict the MTTF of X7R MLCCs under various temperature and voltage conditions. We employ a transfer learning framework to improve prediction accuracy for test conditions with limited data and to provide predictions for test conditions where no experimental data exists. We compare our model with the conventional Eyring model (EM) and, more recently, the tipping point model (TPM) in terms of accuracy and performance. Our results show that the machine learning model consistently outperforms both the EM and TPM, demonstrating superior accuracy and stability across different conditions. Our model also exhibits a reliable performance for untested voltage and temperature conditions, making it a promising approach for predicting MTTF in MLCCs.

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>

Implementation and reliability analysis of a new non‐isolated quadratic buck–boost converter using improved Markov modelling

AbstractHere, a new buck–boost converter with reliability analysis is proposed. The converter has utilized a new voltage multiplier cell at the input side to boost the output voltage. It has a continuous input current with a wide range of duty cycle operations. The converter achieves unity gain at 26.8% duty cycle and can also operate in continuous and discontinuous conduction modes in buck and boost modes. The detailed reliability analysis of the proposed converter using improved Markov modelling is also presented by considering both open circuit and short circuit faults across the semiconductor devices. The variation of reliability and the mean time to failure (MTTF) with different converter parameters like the duty cycle and the input voltage is also discussed here. The hardware results of the developed prototype converter are also presented here at varying duty ratios. The power loss analysis and comparison of the converter with other newly proposed buck–boost topologies show the advantages of the proposed converter. For checking the stability of the converter, the state space model of the converter is also discussed in detail of the proposed converter.

Cost-effective memory protection and reliability evaluation based on machine error-tolerance: A case study on no-accuracy-loss YOLOv4 object detection model

In this paper, we investigate and present a cost-effective memory protection and reliability evaluation methodology for machine learning systems based on machine error-tolerance. We show that by well exploiting the inherent error-tolerability, the incurred cost of the typical memory protection methods including ECC (Error Correction Code) and TMR (Triple Modular Redundancy) can be greatly reduced, making these methods attractive to be adopted to implement a reliable machine learning system. In particular, we also target the up-to-date powerful object detection machine learning model YOLOv4 as a case study. To the best of our knowledge, there is no work in the literature addressing reliability evaluation and enhancement for YOLOv4. Based on identifying the set of error-sensitive (critical) memory blocks and protecting only this set, we develop more efficient ECC and TMR methods. Our ECC method does not require any additional memory cost, while the area cost of our TMR method can be reduced from 200 % to only 47.5 %. The reliability evaluation results show that the Mean Time to Failure (MTTF) of the YOLOv4 object detection system can be extended by about 12 times by the proposed ECC method, while the TMR method can even achieve 108 times longer than that of ECC. We also present a generic methodology to exploit machine error-tolerance for developing a cost-effective memory protection method.