Mean Time To Failure Research Articles

Optimal periodic switching strategy for a two‐unit warm standby degradation system

AbstractIn the traditional switching policy of a standby system, the standby unit is switched to the normal operation state only after the active unit fails. In contrast, in satellite engineering, a new periodic switching strategy is used by technicians in the satellite gyroscope standby system. Due to the performance degradation of the gyroscope in the long‐term operation and standby process, satellites may fail on orbit. For this problem, an operation optimization model is established for a two‐unit warm standby degradation system considering the periodic switching strategy. First, the degradation process of the units is described by a multiphase Wiener process. Second, based on the first‐hitting time theory, the analytic expressions of reliability and mean time to failure (MTTF) of the degradation system are derived. Third, the optimal periodic switching interval is determined to maximize the system MTTF by using the particle swarm optimization algorithm (PSO). Finally, a numerical example of a gyroscope warm standby subsystem is provided to validate the proposed model.

Failure Prediction Approach in Agile Software Development

Software failure prediction is an important activity during agile software development as it can help managers to identify the failure modules. Thus, it can reduce the test time, cost and assign testing resources efficiently. RapidMiner Studio9.4 has been used to perform all the required steps from preparing the primary data to visualizing the results and evaluating the outputs, as well as verifying and improving them in a unified environment. Two datasets are used in this work, the results for the first one indicate that the percentage of failure to predict the time used in the test is for all 181 rows, for all test times recorded, is 3% for Mean time between failures (MTBF). Whereas, SVM achieved a 97% success in predicting compared to previous work whose results indicated that the use of Administrative Delay Time (ADT) achieved a statistically significant overall success rate of 93.5%. At the same time, the second dataset result indicates that the percentage of failure to predict the time used is 1.5% for MTBF, SVM achieved 98.5% prediction.

An Approach to Analyze Reliability Indices in Peer-To-Peer Communication Systems

In this work, the performance of a peer-to-peer (P2P) network has been discussed. As being one of the most promising technology in today’s world, P2P systems exploit computation, bandwidth and other resources at the edge of the Internet, with the lower cost of building and maintenance. In the past, a lot of research has been done to improve the reliability of P2P systems focusing on paradigms like- routing strategies, reliable file exchange, resource location and placement, security. However, not much effort has been focused on studying their overall reliability incorporating essential failure causes altogether. In this research work, the authors have designed a mathematical model for a P2P network using Markov Process to holistically determine the availability, reliability and MTTF (Mean time to failure) and examined the most vulnerable components in the system whose failure rates can be controlled to achieve a more reliable network. In such networks, performance is affected to some degree due to failure or disturbance in any component.

The operational readiness factor of satellite communication networks

Objectives . The most important distinguishing feature of satellite communication networks (SCNs) is topology, which consolidates the scheme for combining nodes and communication channels into a single structure and largely determines the main characteristics of communication systems. The following topologies are used in SCNs: fully connected, tree-like, ring-shaped, and radial (“star” type). The topology can be changed depending on the tasks being solved; for example, to ensure high reliability rates. The most frequently used indicator characterizing the reliability of communication networks is the readiness factor. Considering the SCN as a complex recoverable system, it is advisable to analyze the operational readiness factor along with the readiness factor. This paper investigates the influence of the network topology on the reliability of the SCN. Methods . Queuing theory was used to analyze the flow of events, that is, the flow of failures and recoveries. Results . Assuming that the exponential Mean Time Between Failures (MTBF) model can be used for a central node with a radial network topology, the time dependences of the operational readiness factor were obtained. The reliability of networks with ring and radial topology was compared in terms of the operational readiness factor. Conclusions . To achieve a higher reliability, it is necessary to use an SCN with a radial structure. For example, on a time interval of 12000 h, the operational readiness factor of a two-node SCN with a radial structure is 0.9, and for an SCN with a ring topology with the number of nodes 2, 3, 4, it is 0.7, 0.59, and 0.5, respectively. The study also showed that radial topology is more efficient even with less reliable nodes, that is, with higher failure rates. The advantage of a radial network topology increases as the number of nodes increases. However, in an SCN with a radial topology, failure of the central unit leads to complete degradation of the entire system.

AM&FT: An Aging Mitigation and Fault Tolerance Framework for SRAM-Based FPGA in Space Applications

Field programmable gate arrays (FPGAs) have become very widely used devices in space applications, and their runtime reconfigurable architecture allows for the area and power acceleration for complex applications. However, FPGAs are increasingly susceptible to aging effects and failures due to harsh space environments and long operation cycles, which reduce the reliability and lifetime of such devices. Although offline aging-aware layout-based methods are effective in aging mitigation, existing studies ignore the fault tolerance needs of the task and the layout strategy will completely fail after a hard failure occurs. This paper presents a reliability framework AM&FT for SRAM-based FPGAs in space applications to support on-chip aging mitigation and fault tolerance. We use an Integer Linear Programming (ILP) model to solve mapping relationships between tasks and reconfigurable blocks (Rbs) in the offline phase to achieve the aging and reliability-aware layout strategy. Second, the ILP model is incorporated into the Design Space Exploration (DSE) to generate a set of layout strategies to tolerate hard faults. Moreover, the state model is used for runtime fault management to handle the impact of different types of faults on the device. Experimental results demonstrate that our framework achieves FPGA on-chip aging mitigation and fault tolerance. Compared with the existing methods, AM&FT can guarantee the fault tolerance requirements of tasks and give priority to guarantee the Quality of Service (QoS) of critical tasks under the condition of hard faults accumulation. In addition, our framework delivers up to [Formula: see text] mean time to failure (MTTF) than the baseline.

Digital Twin for Training Bayesian Networks for Fault Diagnostics of Manufacturing Systems.

Smart manufacturing systems are being advocated to leverage technological advances that enable them to be more resilient to faults through rapid diagnosis for performance assurance. In this paper, we propose a co-simulation approach for engineering digital twins (DTs) that are used to train Bayesian Networks (BNs) for fault diagnostics at equipment and factory levels. Specifically, the co-simulation model is engineered by using cyber–physical system (CPS) consisting of networked sensors, high-fidelity simulation model of each equipment, and a detailed discrete-event simulation (DES) model of the factory. The proposed DT approach enables injection of faults in the virtual system, thereby alleviating the need for expensive factory-floor experimentation. It should be emphasized that this approach of injecting faults eliminates the need for obtaining balanced data that include faulty and normal factory operations. We propose a Structural Intervention Algorithm (SIA) in this paper to first detect all possible directed edges and then distinguish between a parent and an ancestor node of the BN. We engineered a DT research test-bed in our laboratory consisting of four industrial robots configured into an assembly cell where each robot has an industrial Internet-of-Things sensor that can monitor vibrations in two-axes. A detailed equipment-level simulator of these robots was integrated with a detailed DES model of the robotic assembly cell. The resulting DT was used to carry out interventions to learn a BN model structure for fault diagnostics. Laboratory experiments validated the efficacy of the proposed approach by accurately learning the BN structure, and in the experiments, the accuracy obtained by the proposed approach (measured using Structural Hamming Distance) was found to be significantly better than traditional methods. Furthermore, the BN structure learned was found to be robust to variations in parameters, such as mean time to failure (MTTF).

Estimation of Mean Time between Failures of a Two Tandem Mill of a Sugar Plant

Abstract: When minutes of down-time can negatively impact the bottom line of a business, it is crucial that the physical infrastructure supporting be reliable. The equipment reliability can be achieved with a solid understanding of mean time between failures. Mean time between failures (MTBF) has been used for years as a basis for various maintenance decisions supported by various methods and procedures for lifecycle predictions. To quantifying a maintainable system or reliability we can use MTBF. For developing the mean time between failures model we can use make use of Poisson distribution, Weibull model and Bayesian model. In this paper we will be talking about complexities and misconceptions of MTBF and clarify criteria that need to be consider in estimating MTBF in a sequential manner. This paper sheds light on MTBF using examples throughout in an effort to simplify complexity. Keywords: MTBF, Two Tandem Mill, Sugar Mill, Reliability, Maintenance

RELIABILITY ANALYSIS OF MAIN ROTOR EC 155B1 ON PIN BLADE AND ATTACH BEAMS COMPONENTS USING MARKOV ANALYSIS

Reliability is used to estimate the conditions of systems or components in the future based on previous conditions. The objects of this research are EC 155B1 aircrafts owned by Indonesia Air Transport & Infrastrucure (IAT), and the method used for analysis is markov analysis. Markov analysis is widely used as an analyzing technique the states of the system that has limited information (memoryless). From this research, ATA chapter 62 Main Rotor that meets the requirements of reliability analysis. The constituent components of main rotor states are Pin Blade, Lower Attach Beam, and Upper Attach Beam. The results of the markov analysis of main rotor system, the states that worked properly are state 1, state 3, and state 4. The probability of the state 1 showed that state has decreased from initial condition, while the state 3 and state 4 have increased from initial condition and subsequently have decreased. The reliability of main rotor system has decreased along with increasing of flight hours with the Mean Time To Failure (MTTF) is 2340.22 flight hours.

AMROFloor: An Efficient Aging Mitigation and Resource Optimization Floorplanner for Virtual Coarse-Grained Runtime Reconfigurable FPGAs

With the rapid reduction of CMOS process size, the FPGAs with high-silicon accumulation technology are becoming more sensitive to aging effects. This reduces the reliability and service life of the device. The offline aging-aware layout planning based on balance stress is an effective solution. However, the existing methods need to take a long time to solve the floorplanner, and the corresponding layout solutions occupy many on-chip resources. To this end, we proposed an efficient Aging Mitigation and Resource Optimization Floorplanner (AMROFloor) for FPGAs. First, the layout solution is implemented on the Virtual Coarse-Grained Runtime Reconfigurable Architecture, which contributes to avoiding rule constraints for placement and routing. Second, the Maximize Reconfigurable Regions Algorithm (MRRA) is proposed to quickly determine the RRs’ number and size to save the solving time and ensure an effective solution. Furthermore, the Resource Combination Algorithm (RCA) is proposed to optimize the on-chip resources, reducing the on-Chip Resource Utilization (CRU) while achieving the same aging relief effect. Experiments were simulated and implemented on Xilinx FPGA. The results demonstrate that the AMROFloor method designed in this paper can extend the Mean Time to Failure (MTTF) by 13.8% and optimize the resource overhead by 19.2% on average compared to the existing aging-aware layout solutions.

Distribution characterisation of spring durability for road excitations using maximum likelihood estimation

This paper presents the determination of a suitable probability function for fatigue-related reliability assessment of automotive coil springs under various road excitations using maximum likelihood estimation (MLE). The fatigue-related reliability assessment is a critical element for determining the component warranty period. The fatigue lives of the coil spring under various road excitations were obtained from experimental analysis using strain life approaches. The parameters for various distribution approach were estimated through maximum likelihood estimation (MLE). The Akaike information criterion (AIC) was used to determine the most suitable approach for fitting. As the outcome, the Nakagami distribution has shown the lowest AIC value at 330 which indicating as the most suitable fitting among other distributions. For reliability assessment, the mean time to failure (MTTF), reliability function and hazard rate was analysed. Based on these results, the suitable distribution for fatigue-related reliability characterisation were determined for improving spring life cycle assessment.

Quantitative Reliability Evaluation of Silicon Carbide-Based Inverters for Multiphase Electric Drives for Electric Vehicles

Abstract Reliability of power converter in the electric drivetrain of a vehicle can be a criterion for the comparison of various converter topologies, cooling system designs and control strategies. Therefore, reliability prediction is important for the design and control of vehicles. This paper presents an approach for quantitative evaluation of the reliability of converters for multiphase motor drives for electric vehicles (EVs) after considering the driving cycle. This paper provides a good background of reliability quantification so that it is easy to extend the presented approach to other applications. The models of subsystems have been selected to have excellent computational efficiency with good accuracy which is necessary for simulating long driving cycles. A simple vehicle model is used to obtain the traction motor torque demand for various points of the driving cycle. A multiphase interior permanent magnet synchronous motor has been used for traction motor. The operating voltage and currents of the motor are found using maximum torque per ampere (MTPA) control of IPMSM. The analytical loss calculation has been used to find the losses of switching devices of the converter. A thermal model of silicon carbide (SiC) MOSFET has been used to calculate junction temperature from the losses. The model developed here gives the failure rate and mean time between failures (MTBF) of switching devices of the inverter, which can be used to determine the failure rate. The model has been used to find the transition probabilities of a Markov model which can be used to quantify the reliability of converters of multiphase electric drives.

Порівняння надійності термоелектричних генераторних модулів з різними схемами сполучення термоелементів

The study compares reliability indicators of thermoelectric generator modules using the nonmonotonic-diffusion law of failure time distribution. The authors use the law to propose for the first time an analytical expression of the failure time distribution of an individual thermoelement, which fundamentally differs from the traditional simple exponential law with a constant failure intensity. The law is used to calculate the mean time between failures (MTBF), the 95% resource and equivalent failure rate of thermoelectric generator modules with series-parallel and parallel-series connection of thermocouples. Considered are the variants of schemes with different number of elements in rows and different number of rows for serial-parallel circuit and with different number of sequential groups and elements in a group for parallel-serial circuit. Coefficients of increase of the average failure time, 95% resource and coefficients of decrease of the equivalent failure intensity for a purely series connection of thermocouples are determined for the considered thermocouple schemes. It is established that these indicators can be improved by more than three orders of magnitude.

Construction and Simulation of Failure Distribution Model for Cycloidal Gears Grinding Machine

To increase reliability, reduce machine tool failure, and shorten maintenance time, serval failure distribution models are discussed based on the cycloidal gears form grinding. A fault data expansion algorithm model based on the radial basis function (RBF) neural network is proposed to accurately evaluate the reliability of cycloidal gears form grinding machines. The model uses a self-organizing clustering learning algorithm to determine the RBF centers and expansion constants of the neural network. It trains the neural network using the learning algorithm’s output and imports the randomly generated cumulative failure distribution function to obtain simulation data. A numerical example of researching failure distribution is presented which shows that the estimated value of mean time between failures (MTBF) is 909.20h, and the estimated value of reliability is 0.4874. Finally, the fault tree analysis-analytic hierarchy process (FTA-AHP) is used to analyze the fault tree of the cycloidal gear grinding machine. The reliability evaluation indexes are obtained by analyzing the corresponding reliability characteristic function, which proves the failure distribution model is valid. It has important guiding significance in evaluating the reliability and performance of large computer numerical control (CNC) form gear grinding machines.

ReAD: Reliability aware data

As transistor technology scales beyond 32 nm, the reliability of the processors and servers reduces at an exponential rate. In recent times, the qualification of reliability of servers has reduced from 5 years to 3 years. With this current trend, the lifetime of servers can be expected to shrink further. In a cloud data center, with hundreds and hundreds of servers, the ill effect of shorter lifetime reliability will be magnified. Reliability of servers influences the performance, cost and power efficiency of a cloud data center. In this paper, a cost-effective technique to continuously monitor the reliability of servers and scheduling tasks based on these reliability values is proposed. The proposed technique monitors the hardware parameters for the current executing workload to calculate the reliability metrics. The reliability metric used in ReAD is MTTF (Mean Time to Fail) and this is calculated for individual servers. The load balancer reads the MTTF of individual servers and performs the load balance, considering reliability as one of the parameters. The proposed technique is non-invasive in nature and does not require the server to go into a special test mode. This saves the testing time. The experimental analysis shows on an average 25% improvement in Server Usage Metric with the minimum improvement around 5% and the maximum is around 50%.

Operational failure assessment of Remotely Operated Vehicle (ROV) in harsh offshore environments

For an effective integrity assessment of marine robotic in offshore environments, the elements’ failure characteristics need to be understood. A structured probabilistic methodology is proposed for the operational failure assessment (OFA) characteristics of ROV. The first step is to assess the likely failure mode of the ROV system and its support systems. This captures the interaction and failure induced events during operation. The identified potential failure modes are further developed into logical connectivity based on the cause-effect relationship. The logical framework is modeled using the fault tree analysis technique to predict the ROV operational failure probability in an uncertain harsh environment. The fault tree analysis captured the logical relationship between the primary, intermediate, and top events probability. The importance measure criteria were adopted to identify the most probable events, links, and their importance on the failure propagation. The model was demonstrated with an ROV for deep arctic water subsea operations. The result identified the control system, communication linkages, human factor, among others, as most critical in the ROV operational failure. The methodology’s application provides core information on the Mean time between failure (MTBF) of the ROV system that could aid integrity management and provides a guide on early remedial action against total failure.

Picking the right surgeon: Quantitative approach

There is an abundance of useful verbal information and recommendations on how to make the best choice when referring a patient for surgery or when seeking the right surgeon for your own operation. A quantitative approach is suggested here on how this could be done through assessing the probability of success and/or the Mean-Time-To-Failure (MTTF) of the planned operation by considering and comparing the skills of two highly qualified candidates. Then the chooser could continue this effort by comparing the background and the probability of success of the best one of these two candidates with the next suitable candidate, and then go on with the process for as many candidates as he/she would like to evaluate. The approach suggests using the double-exponential highly flexible and highly physically meaningful probability Distribution Function (DEPDF) as a suitable model. This function was introduced about a decade ago in the reliability physics to quantify, on the probabilistic basis, the outcome of a particular engineering, ergonomics or medical undertaking of importance. The surgeon’s qualifications are identified in our approach as Human Capacity Factor (HCF). Figures of Merit (FoM) of this factor consider many relevant human qualities, as well as the durations and the outcomes of the surgeon’s previous, both successful and failed, operations. The mental (cognitive) workload (MWL) reflects the complexity of the operation and, in the present analysis, is assumed to be the same for the two surgeon’s considered. The role of an anesthesiologist is not taken into account directly in our approach: it is the surgeon who decides on his/hers partner, and the surgeon’s choice is viewed as part of his/hers HCF. The general concepts are illustrated by a numerical example.

Reliability and Performance Analysis of a Series-Parallel System Using Gumbel–Hougaard Family Copula

This research looks into the reliability metrics that are used to assess the strength of a solar system’s serial system, which is made up of four subsystems. Each subsystem consists of two parallel active components: two out of two photo-voltaic panels, one of two charge controllers, two of two batteries, and one of two inverters. Both the charge controller and the inverter have two human operators or switches. The Gumbel–Hougaard copula family was used to produce formulations of system dependability metrics such as reliability, mean time to failure (MTTF), availability, and profit function. Numerical examples are presented to show the obtained results and to investigate the impact of various system characteristics. The new study might help homes overcome some of the problems experienced by electric generation systems operating in hostile locations or under adverse weather conditions. A new model was developed, and solar photovoltaic system’s subsystems were analyzed in order to identify the most essential component. It was also indicated how to improve the system.

Internal Damage Detection of Composite Structures Using Passive RFID Tag Antenna Deformation Method: Basic Research

This manuscript deals with the detection of internal cracks and defects in aeronautical fibreglass structures. In technical practice, it is problematic to accurately determine the service life or MTBF (Mean Time Between Failure) of composite materials by the methods used in metallic materials. The problem is mainly the inhomogeneous and anisotropic structure of composites, possibly due to the differences in the macrostructure during production, production processes, etc. Diagnostic methods for detecting internal cracks and damage are slightly different, and in practice, it is more difficult to detect defects using non-destructive testing (NDT). The article deals with the use of Radio frequency identification (RFID) technology integrated in the fibreglass laminates of aircraft structures to detect internal defects based on deformation behaviour of passive RFID tag antenna. The experiments proved the potential of using RFID technology in fibreglass composite laminates when using tensile tests applied on specimens with different structural properties. Therefore, the implementation of passive RFID tags into fibreglass composite structures presents the possibilities of detecting internal cracks and structural health monitoring. The result and conclusion of the basic research is determination of the application conditions for our proposed technology in practice. Moreover, the basic research provides recommendations for the applied research in terms of the use in real composite airframe structures.

Application of integrated factor evaluation–analytic hierarchy process–T-S fuzzy fault tree analysis in reliability allocation of industrial robot systems

Aiming at the defects (only two kinds of state, i.e., normal or fault) of existed reliability allocation methods without considering the intermediate degradation process, a methodology named integrated factor evaluation–analytic hierarchy process–T-S fuzzy fault tree analysis (IFE-AHP-T-S fuzzy FTA) is proposed to allocate the reliability index of industrial robot systems (IRSs) which have multiple fault states. Firstly, the reliability model of IRSs is established and the allocation principle of reliability index is discussed. Secondly, two-layer IFE model is established considering the degradation of mechanical structure and multi-state fault of IRSs to evaluate the technical merit of different subsystems. The hesitant fuzzy language set is adopted to reduce the subjectivity of expert evaluation process, which have the ability of dealing with uncertain information. Then, the AHP is proposed to allocate different weights for influence factors, and the T-S fuzzy FTA is presented to calculate fault probability and mean time between failures (MTBF) in the process of weight allocation of reliability index for IRSs and six subsystems. Finally, multi-state reliability index allocation of IRSs is completed. This investigation has a significance for reducing the fault probability and unsafe factors, and provides a theoretical basis for the whole life cycle design of IRSs.

Investigation of Mean-Time-to-Failure Measurements from AlGaN/GaN High-Electron-Mobility Transistors Using Eyring Model

We present the mean-time-to-failure (MTTF) calculations for AlGaN/GaN high-electron-mobility transistors (HEMTs) using two independent acceleration factors. MTTF predictions are generally calculated through the Arrhenius relationship, based on channel temperature and acceleration, depend only on one parameter. Although the failure modes of the AlGaN/GaN HEMTs depend largely on the applied electric fields, the Eyring model is introduced to investigate both voltage and temperature dependent degradation of AlGaN/GaN devices. In anticipation of adequate MTTF values, studies were conducted on non-commercial devices. Further, we distinguished the cumulative failure percentages through the Weibull and log-normal distributions. We also explored the increase in gate leakage current at high temperatures for early device deterioration.