Explore the vast range of titles available.

Failure mode and effects analysis (FMEA) is known to be a proactive reliability analysis model broadly utilized to recognize and evaluate potential failure modes in various industries. The normal risk priority number (RPN) method, however, has suffers from a lot of criticisms, such as requirement of precise risk estimation, lack of scientific basis in computing RPN, and neglecting the weights of risk factors. Therefore, this paper devises a new FMEA model to evaluate and prioritize the risk of failure modes by integrating probabilistic linguistic term sets and TODIM (an acronym in Portuguese for interactive multi-criteria decision making) method. The probabilistic linguistic term sets are utilized to handle the intrinsic ambiguity existed in the risk assessments of FMEA team members, whilst an extended TODIM method is employed for determining the priority ranking of the individuated failure modes. Further, based on the technique for order of preference by similarity to ideal solution (TOPSIS), an objective weighting method is presented to derive the relative weights of risk factors. Finally, two illustrative examples are implemented and comparisons with other existing methods are performed to demonstrate the rationality and superiority of our proposed FMEA model.

The interactions and dependencies between software and hardware are often neglected in modeling system reliability in the past few decades due to the mathematical complexity. However, many system failures occurred from the interactions or simultaneous occurrences of software and hardware. This paper first proposes a new diagram of categorizing system-level failures and further incorporates such a diagram into the development of complex system reliability framework. System-level failures result from software subsystem, hardware subsystem, and the interactions of software and hardware subsystems. The focus of this study is on the investigation of the interactions failures generated from the interactions of software and hardware subsystems. In addition to the considerations of total hardware failures, software-induced hardware failures, and hardware-induced software failures introduced by Zhu and Pham (Mathematics 7(11):1049, 2019), we further introduce the partial hardware failures that can be respectively induced by hardware and software to explicitly demonstrate the dependencies and interactions between software and hardware. Hence, a new complex system reliability framework is developed based on such system-level failure categorization with the Markov process. Furthermore, the numerical examples are studied to illustrate the impacts on system reliability with the changes of state transition parameters that modeling the interactions of software and hardware subsystems. Finally, we have studied two maintenance policies of the proposed complex system reliability model.

The factors that influence the inherent reliability of machine tool spindle are a mixture of various uncertainties and it leads that the reliability modeling and analysis of machine tool spindle can’t be dealt with by one mathematical theory. Meanwhile, the reliability data of the machine tool spindle for reliability modeling and analysis is often insufficient, and data of different types such as accumulated historical data, expert opinions, simulation data, etc. are used to make up for the lack of data. Thus, the unified quantification of mixed uncertainties and the data characterization of different types are the major premises for reliability modeling and analysis of machine tool spindle. By considering this, this paper makes use of the advantage of imprecise probability theory in quantizing the multiple types of data and the advantage of the Bayes theory in data fusion, and proposes a new inherent reliability modeling and analysis method based on imprecise Dirichlet model. In the proposed method, imprecise probability theory is used to quantify mixed uncertainties, imprecise Dirichlet model is built to characterize the different types of reliability data. After analyzing the inherent reliability variation regularity, an inherent reliability model is built, and the proposed method is verified by the inherent reliability calculation of a certain heavy-duty CNC machine tool’s milling spindle. This study can provide new method, theory and reference for reliability modeling and analysis when there are various uncertainties mixed and multiple data existed.

Multi-state components, common cause failures (CCFs) and data uncertainty are the general problems for reliability analysis of complex engineering systems. In this paper, a method incorporating fuzzy probability and Bayesian network (BN) into multi-state systems (MSSs) with CCFs is proposed. In particular, basic theories of multi-state BN and fuzzy probability are developed. Moreover, a model integrating CCFs with BN has also been illustrated. In order to incorporate fuzzy probability into MSSs reliability evaluation considering common parent node generated by CCFs, fuzzy probability has to be translated into accurate probability through defuzzification and normalization methods which are both elaborated. In addition, quantitative analysis based on BN is carried out. In this paper, feed system of boring spindle in computer numerical control machine is analyzed as an example to validate the feasibility of the proposed method. It can improve the ability of BN on reliability evaluation of complex system with uncertainty issues.

•Low reliability of the inherently noisy rs-fMRI limits the discovery of associated traits.•A measurement model can reveal the “true” associations in the absence of random error.•We show that reliability modelling can benefit behavioral and genetic studies.•The split-session approach can be applied to new and existing rs-fMRI datasets. Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliability of FC and may bias its association with other traits. Low reliability also limits heritability estimates. Classical test theory can be used to obtain a true correlation estimate free of random measurement error from parallel tests, such as split-half sessions of a rs-fMRI scan. We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of FC in association with traits from various domains. We evaluated the efficiency of the measurement model on extracting a stable and reliable component of FC and its association with several traits for various sample sizes and scan durations. In addition, we aimed to replicate our previous findings of increased heritability estimates when using a measurement model in a longitudinal adolescent twin cohort. The split-half measurement model improved test-retest reliability of FC on average with +0.33 points (from +0.49 to +0.82), improved strength of associations between FC and various traits on average 1.2-fold (range 1.09–1.35), and increased heritability estimates on average with +20% points (from 39% to 59%) for the full HCP dataset. On average, about half of the variance in split-session FC estimates was attributed to the stable and reliable component of FC. Shorter scan durations showed greater benefit of reliability modelling (up to 1.6-fold improvement), with an additional gain for smaller sample sizes (up to 1.8-fold improvement). Reliability modelling of FC based on a split-half using a measurement model can benefit genetic and behavioral studies by extracting a stable and reliable component of FC that is free from random measurement error and improves genetic and behavioral associations.

As wind energy continues to expand, ensuring the reliability of wind turbines is critical for optimising operational efficiency and minimising downtime. Based on maintenance data from over 1,000 onshore and offshore wind turbines covering more than 4200 operating years, this study presents an analysis of wind turbine failure behaviour over time and identifies key factors influencing reliability. Failure trends are assessed using Nelson–Aalen plots, whereas non‐homogeneous Poisson process regression models are developed to quantify the effect of design and technological development, incorporating a range of covariates. Results reveal that whereas some subsystems exhibit failure intensities following a classical bathtub curve, others transition directly from early failures to deterioration or are monotonically increasing throughout time. The regression modelling results indicate that reliability generally improves with later commissioning years, highlighting the effectiveness of technological advancements. Rated power negatively affects reliability, with larger turbines experiencing higher failure intensities. Additionally, offshore turbines are generally found to be more reliable than onshore ones, except for the yaw subsystem, which exhibited higher failure rates in offshore environments. Subsystem‐specific findings further underscore the influence of design choices: Hydraulic pitch systems outperform electrical ones in reliability, and direct‐drive turbines demonstrate lower failure intensities in both the drive train and power generation subsystems compared to geared alternatives.

This article presents a Bayesian reliability modelling approach for wind turbines that incorporates the effect of time‐dependent variables. Namely, the technique is used to explore the effect of annual services on wind turbine failure intensity through time for turbines within a currently operational wind farm. In the operator's experience, turbines seemed to fail more frequently after scheduled maintenance was performed; however, this is an unexplored effect in the literature. Additionally, the effects of seasonality, year of operation and position in the array on failure intensity are explored. These features were included in a Cox‐like model formulation which allows for time‐dependent covariates. Inference was performed via Bayes rule. Results show a spike in failure intensity reaching 1.57 times the baseline in the six days directly proceeding annual servicing, after which failure intensity is reduced compared to baseline. Also observed is a significant year‐on‐year reduction of failure intensity since the introduction of the site's data management system in 2018, a clear preference for modelling time to failure via a Weibull distribution and a dependence on location in the array with respect to the prominent wind direction. Results also show the benefit of employing a Bayesian regime, which provides easily interpretable uncertainty quantification.

This paper analyses the disruptions occurring in a production system determining the operating states of a single machine. A system with a convergent production character, in which both single flows (streams) and multi-stream flows occur, was considered. In this paper, a two-level formalisation of the production system (PS) was made according to complex systems theory. The continuity analysis was performed at the operational level (manufacturing machine level). The definition of the kth survival value and the quasi-coherence property defined on chains of synchronous relations were used to determine the impact of interruption of the processed material flow on uninterrupted machine operation. The developed methodology is presented in terms of shaping the energy efficiency of technical objects with the highest power demand (the furnace of an automatic paint shop and the furnace of a glass tempering line were taken into consideration). The proposed methodology is used to optimise energy consumption in complex production structures. The model presented is utilitarian in nature—it can be applied to any technical system where there is randomness of task execution times and randomness of unplanned events. This paper considers the case in which two mutually independent random variables determining the duration of correct operation TP and the duration of breakdown TB are determined by a given distribution: Gaussian and Gamma family distributions (including combinations of exponential and Erlang distributions). A formalised methodology is also developed to determine the stability of system operation, as well as to assess the potential risk for arbitrary system evaluation parameters.

Correlation can seriously degrade reliability and capacity due to the simultaneous failure of multiple components, which lowers the probability that a system can execute its required functions with acceptable levels of confidence. The high cost of fault in time-critical systems necessitates methods to explicitly consider the influence of correlation on reliability. This paper constructs a network-structured model, namely time-constrained multi-state network (TCMSN), to investigate the capacity of a computer network. In the TCMSN, the physical lines comprising the edges of the computer network experience correlated faults. Our approach quantifies the probability that d units of data can be sent from source to sink in no more than T units of time. This probability that the computer network delivers a specified level of data before the deadline is referred to as the system reliability. Experimental results indicate that the negative influence of correlation on reliability could be significant, especially when the data amount is close to network bandwidth and the time constraint is tight. The modeling approach will subsequently promote design and optimization studies to mitigate the vulnerability of networks to correlated faults.

The main drawback of any Design for Reliability methodology is lack of easy accessible reliability models, prepared individually for each critical component. In this paper, a reliability model for SiC power MOSFET in SOT – 227 B housing, subjected to power cycling, is presented. Discussion covers preparation of Accelerated Lifetime Test required to develop such reliability model, analysis of semiconductor degradation progress, samples post-failure analysis and identification of reliability model parameters. Such model may be further used for failure prognostics or useful lifetime estimation of High Performance Power Supplies.