Reliability Analysis Framework Research Articles

Reliability analysis for multi-component systems considering stochastic dependency based on factor analysis

Reliability analysis for engineering systems with multiple components has gained increasing interest in recent. Most existing works assume that components follow identical degradation models and preset joint distributions or link functions to characterize the degradation interactions. However, distinct degradation characteristics of components are commonly observed in practice. Besides, the degradation interactions are usually complex and diverse, making the preset dependency structures not applicable. Confronted with the diverse degradation characteristics and complex degradation interactions, this paper offers a flexible reliability analysis framework for multi-component systems. First, a stochastic process-based general degradation model combining the Wiener process, Gamma process, and inverse Gaussian process is adopted to describe component degradation processes, and the factor analysis is employed to characterize the degradation interactions by seeking the latent common factors that dominate their interdependency. Thus the assumption of the identical degradation process and preset dependency structure can be relaxed, which enhances the robustness of the method. On this basis, we derive the explicit form of the system reliability function. An efficient Expectation-Maximization algorithm is then utilized for statistical inference to enable a fast computation. Finally, the superiority of the proposed method is demonstrated by two real case studies on lithium-ion battery packs.

Kriging Model for Reliability Analysis of the Offshore Steel Trestle Subjected to Wave and Current Loads

Offshore steel trestles (OSTs) are exposed to severe marine environments with stochastic wave and current loads, making structural safety assessment challenging and difficult. Reliability analysis is a suitable way to consider both wave and current loading intensity uncertainties, but the implicit and complex limit state functions of the reliability analysis usually imply huge computational costs. This paper proposes an efficient reliability analysis framework for OST using the kriging model of optimal linear unbiased estimation. The surrogate model is built with stochastic waves, current parameters, and the corresponding load factors. The framework is then used to evaluate the reliability of an example OST subjected to wave and current loads at three limit states of OST, including first yield (FY), full plastic (FP), and collapse initiation (CI). Three different distributions are used for comparison of the results of failure probability and reliability index. The results and the computational cost by the proposed framework are compared with that from the Monte Carlo sampling (MCS) and Latin hypercube sampling (LHS) method. The influences of sample number on the prediction accuracy and reliability index are investigated. The influence of marine growth on the reliability analysis of the OST is discussed using MCS and the kriging model. The results show that the reliability analysis based on the kriging model can obtain the reliability index for the OST efficiently with less calculation time but similar results compared with MCS and LHS. With the increase of the number of samples, the prediction accuracy of the kriging model increases, and the corresponding failure probability fluctuates greatly at first and then tends to be stable. The reliability of the example OST is reduced with the increase of marine growth, regardless of the limit state.

Mechanical Equipment Reliability Analysis: Case Study

In civil and mining industries, Wheel loaders are an important component and their cost capability at effective operation. The environmental and operational factors dramatically affect the performance of loaders. In many cases, failure data are often collected from multiple and distributed units in different operational conditions, which can introduce heterogeneity into the data. Part of such heterogeneity can be explained and isolated by the observable covariates, whose values and the way they can affect the item's reliability are known. However, some factors that may affect the item's reliability are typically unknown and lead to unobserved heterogeneity. These factors are categorized as unobserved covariates. In most reliability studies, the effect of unobserved covariates is neglected. This may lead to erroneous model selection for the time to failure of the item, as well as wrong conclusions and decisions. There is a lack of sufficient knowledge, theoretical background, and a systematic approach to model the unobserved covariate in reliability analysis. This paper aims to present a framework for reliability analysis in the presence of unobserved and observed covariates. The unobserved covariates will be analyzed using frailty models (Such as Mixed Proportional Hazard).A case will illustrate the application of the framework.

Dynamic reliability analysis framework using fault tree and dynamic Bayesian network: A case study of NPP

The Emergency Diesel Generator (EDG) is a critical and essential part of the Nuclear Power Plant (NPP). Due to past catastrophic disasters, critical systems of NPP like EDG are designed to meet high dependability requirements. Therefore, we propose a framework for the dynamic reliability assessment using the Fault Tree and the Dynamic Bayesian Network. In this framework, the information of the component's failure probability is updated based on observed data. The framework is powerful to perform qualitative as well as quantitative analysis of the system. The validity of the framework is done by applying it on several NPP systems.

LSTM-augmented deep networks for time-variant reliability assessment of dynamic systems

This paper presents a long short-term memory (LSTM)-augmented deep learning framework for time-dependent reliability analysis of dynamic systems. To capture the behavior of dynamic systems under time-dependent uncertainties, multiple LSTMs are trained to generate local surrogate models of dynamic systems in the time-independent system input space. With these local surrogate models, the time-dependent responses of dynamic systems at specific input configurations can be predicted as an augmented dataset accordingly. Then feedforward neural networks (FNN) can be trained as global surrogate models of dynamic systems based on the augmented data. To further enhance the performance of the global surrogate models, the Gaussian process regression technique is utilized to optimize the architecture of the FNNs by minimizing a validation loss. With the global surrogates, the time-dependent system reliability can be directly approximated by the Monte Carlo simulation (MCS). Three case studies are used to demonstrate the effectiveness of the proposed approach.

Nonstationary Extreme Value Analysis of Nearshore Sea-State Parameters under the Effects of Climate Change: Application to the Greek Coastal Zone and Port Structures

In the present work, a methodological framework, based on nonstationary extreme value analysis of nearshore sea-state parameters, is proposed for the identification of climate change impacts on coastal zone and port defense structures. The applications refer to the estimation of coastal hazards on characteristic Mediterranean microtidal littoral zones and the calculation of failure probabilities of typical rubble mound breakwaters in Greek ports. The proposed methodology hinges on the extraction of extreme wave characteristics and sea levels due to storm events affecting the coast, a nonstationary extreme value analysis of sea-state parameters and coastal responses using moving time windows, a fitting of parametric trends to nonstationary parameter estimates of the extreme value models, and an assessment of nonstationary failure probabilities on engineered port protection. The analysis includes estimation of extreme total water level (TWL) on several Greek coasts to approximate the projected coastal flooding hazard under climate change conditions in the 21st century. The TWL calculation considers the wave characteristics, sea level height due to storm surges, mean sea level (MSL) rise, and astronomical tidal ranges of the study areas. Moreover, the failure probabilities of a typical coastal defense structure are assessed for several failure mechanisms, considering variations in MSL, extreme wave climates, and storm surges in the vicinity of ports, within the framework of reliability analysis based on the nonstationary generalized extreme value (GEV) distribution. The methodology supports the investigation of future safety levels and possible periods of increased vulnerability of the studied structure to different ultimate limit states under extreme marine weather conditions associated with climate change, aiming at the development of appropriate upgrading solutions. The analysis suggests that the assumption of stationarity might underestimate the total failure probability of coastal structures under future extreme marine conditions.

Stakeholders and Risks in Liquified Natural Gas Bunkering Projects: The Hidden Link

The importance of stakeholders’ analysis for the effective management of risks in any business sector has been widely recognized and depicted in International Organization for Standardization (ISO) standards. This kind of analysis is even more necessary in businesses and organizations dealing with significant technological and market changes, such as the provision and usage of Liquefied Natural Gas (LNG) as a marine fuel. In the LNG bunkering industry, several methods have been proposed to support risk management. However, they all suffer from an important drawback: they guide risk management mainly to the identification, analysis, and control of potential accidental events within a health and safety or a technical reliability analysis framework, failing to structure the correlation of risks with the actual actors, i.e., the numerous stakeholders whose decisions may influence directly or indirectly the organization’s objectives. This paper presents a method to systematically analyze the role of stakeholders and their ability to pose threats and/or opportunities to an organization. The proposed approach employs the Social Network Analysis (SNA) methodology to model and analyze stakeholder interests, interactions, and relationships that are important to the organization’s objectives. The method is applied in a small-scale LNG bunkering project at a Greek port.

An Importance Sampling Framework for Time-Variant Reliability Analysis Involving Stochastic Processes

In recent years, methods were proposed so as to efficiently perform time-variant reliability analysis. However, importance sampling (IS) for time-variant reliability analysis is barely studied in the literature. In this paper, an IS framework is proposed. A multi-dimensional integral is first derived to define the time-variant cumulative probability of failure, which has the similar expression to the classical definition of time-invariant failure probability. An IS framework is then developed according to the fact that time-invariant random variables are commonly involved in time-variant reliability analysis. The basic idea of the proposed framework is to simultaneously apply time-invariant IS and crude Monte Carlo simulation on time-invariant random variables and stochastic processes, respectively. Thus, the probability of acquiring failure trajectories of time-variant performance function is increased. Two auxiliary probability density functions are proposed to implement the IS framework. However, auxiliary PDFs available for the framework are not limited to the proposed two. Three examples are studied in order to validate the effectiveness of the proposed IS framework.

Observed and unobserved heterogeneity in failure data analysis

In reality, failure data are often collected under diffract operational conditions (covariates), leading to heterogeneity among the data. Heterogeneity can be classified as observed and unobserved heterogeneity. Un-observed heterogeneity is the effect of unknown, unrecorded, or missing covariates. In most reliability studies, the effect of unobserved covariates is neglected. This may lead to inaccurate reliability modeling, and consequently, wrong operation and maintenance decisions. There is a lack of a systematic approach to model the unobserved covariate in reliability analysis. This paper aims to present a framework for reliability analysis in the presence of unobserved and observed covariates. Here, the unobserved covariates will be analyzed using frailty models. A case study will illustrate the application of the framework.

Efficient reliability analysis considering uncertainty in random field parameters: Trained neural networks as surrogate models

This paper presents an efficient reliability analysis framework, by using trained artificial neural networks (ANNs) as surrogate models, for geotechnical problems where the random field parameters like the mean and standard deviation are themselves uncertain. Random field theory has been extensively used to model soil uncertainty and spatial variability. However, due to limited availability of data, random field parameters can rarely be estimated accurately, often estimated in confidence intervals (uncertain parameters). Monte Carlo based reliability analysis is computationally extremely demanding because the function to map outcomes of random fields to structural response can only be calculated via numerical simulations. The authors have used trained ANNs as surrogate models in reliability analysis. However, these ANNs are specific for random fields with deterministic parameters. This paper presents a new framework in which trained ANN models are for random fields with variable parameters. A key component is the design of experiments – generating representative outcomes. In the prediction of the bearing capacity for strip footings, the efficiency and accuracy of this framework are successfully demonstrated. This framework is also efficient in reliability sensitivity studies. One main finding is that ignoring random field parameter uncertainty could lead to underestimated failure probability and hence unsafe design.

Reliability Analysis of Networked Control Systems with the Consideration of Operating Frequency

The operating frequency of the networked control system (NCS) determines both its control performance and heat flux density. Considering the significant effect of thermal stress on the reliability of electronics, a quantitative reliability analysis framework is proposed for NCSs with the consideration of its operating frequency. It includes 5 main steps: 1) compute the power consumption of all its electronic components using circuit simulation; 2) obtain the thermal stress of these components by thermal simulation; 3) apply the thermal stress results to assess the reliability of components using the part stress analysis method; 4) calculate the reliability of the modules using reliability block diagrams; and 5) quantify the reliability of the NCS by combining the graph reduction method and other network reliability algorithms together. A double loop networked control system is used as an example to verify the effectiveness of our method.

An Integrated Reliability Analysis Model of Sheet Pile Wharfs Based on Virtual Support Beam Model and Artificial Intelligence Algorithm

Sheet pile wharfs are widely used coastal structures with good adaptability for geological conditions. A reliability evaluation is required for safety. Challenges are raised from the complicated solution in both the structural response and reliability. To provide a flexible and efficient approach, an integrated reliability analysis model was proposed: The virtual simply support beam (VSSB) model was developed to estimate the structural response; Based on probabilistic methodologies and artificial intelligence (AI) models, the limit state surface was fitted to search reliability solutions. A case study was used to illustrate the applicability of the proposed model and compare the prediction performance of AI models. An extensive parametric study was performed to investigate the influential factors on structural reliability. Compared with finite element analysis, the VSSB model presented reasonable estimations on structural response. The genetic programming performed best in predicting structural response with an average relative absolute error (RAE) of 0.018. The corresponding integrated models gave the reliability solutions with an average RAE of less than 0.014. Soil parameters in different positions significantly affected the structural stability, that the material qualities should be addressed. This study provides references in engineering safety design and guidelines the reliability analysis framework for complicated structures.

A Reliability Analysis Framework for Space Modulation Techniques

Space modulation techniques (SMTs) have emerged as a family of multiple-input multiple-output (MIMO) transmitter designs that require a maximum of a single RF-chain. SMTs have been analyzed in the context of cost, power, and error performance in past studies and have shown to exert favorable advantages over traditional implementations such as spatial multiplexing (SMX). However, given future application requirements, analysis of the hardware also in the context of reliability becomes essential. In this work, an SMT hardware reliability analysis framework is devised using two quantitative analysis tools consisting of reliability block diagrams (RBDs) and Markov Chains. In addition, a comparative study using commercial component failure rates is performed to analyze how SMTs compare to each other.

Progressive Failure of Slopes: Stochastic Simulation Based on Transition Probabilities and Markov Chains

A reliability analysis framework for the stochastic simulation of slope progressive failure is proposed in the current work. Progressive failure is addressed as a local failure propagation process that takes place along individual segments upon a critical slip surface. The probabilities of progressive failure are evaluated by transition probabilities and Markov chains theory. Slope stability computations are performed by elastoplastic finite element models. The point estimate method is used for the direct integration of geotechnical uncertainty within stability computations. For the various safety states during local failure advancement, progressive failure probabilities are expressed through a transition probability matrix. The effect of certain factors, such as the cross-correlation between shear strength components, the performance function’s probability distribution, and the initial stress ratio at rest, are also investigated and discussed.

A unified analysis framework of static and dynamic structural reliabilities based on direct probability integral method

Generally, the static and dynamic reliabilities of structures are addressed separately in the existing methods except the computationally expensive stochastic sampling-based approaches. This study establishes a unified framework of reliability analysis for static and dynamic structures based on the direct probability integral method (DPIM). Firstly, the probability density integral equations (PDIEs) of performance functions for static and dynamic structures are presented based on the principle of probability conservation. The DPIM decouples the physical mapping (i.e., performance function) of structure and PDIE, and involves the partition of probability space and the smoothing of Dirac delta function. This study proposes a new adaptive formula of smoothing parameter based on kernel density estimation. Then, the improved DPIM is utilized to obtain the probability density function (PDF) of performance functions by solving the corresponding representative values and the PDIE successively. Furthermore, the reliability of static structure is calculated by integrating the PDF of performance function within safety domain. To overcome the difficulty of evaluating first passage dynamic reliability, the two approaches, namely the DPIM-based absorbing condition (DPIM-AC) and the DPIM-based extreme value distribution (DPIM-EVD), are also proposed. Finally, three engineering examples with stochastic parameters and random excitation indicate the desired efficiency and accuracy of the established framework for unified reliability analysis. Specifically, the challenging issue of dynamic reliability assessment for nonlinear structural system is attacked based on DPIM rather than Monte Carlo simulation or other sampling-based method. The proposed method is beneficial for propagation analysis of aleatory or/and epistemic uncertainties, as well as for stochastic model updating.

Reliability analysis of structures by iterative sequential sampling based response surface

In the metamodelling-based Monte Carlo simulation (MCS) framework for reliability analysis of structures, the training samples used to construct the response surface should be as close to the failure plane as possible to ensure sufficient accuracy in reliability estimates. For this, an algorithm based on the maximin distance criterion combined with an error norm based on leave-one-out cross-validation (CV) is proposed to construct a moving least-squares based response surface for improved reliability estimate. The algorithm hinges on the fact that the MCS points having predicted responses less than the maximum absolute error obtained by the leave-one-out CV approach are likely to have the maximum effect on the accuracy of the reliability estimate. Relying on this, two points are added at each iteration, ensuring that the new data points are sufficiently close to the actual limit state and adequately far from existing data points. The improved reliability estimation capability of the proposed algorithm considering the direct MCS-based results as the benchmark is elucidated numerically by considering two example problems.

Reliability analysis of planar steel trusses based on p-box models

Introduction. The development of probabilistic approaches to the assessment of mechanical safety of bearing structural elements is one of the most relevant areas of research in the construction industry. In this research, probabilistic methods are developed to perform the reliability analysis of steel truss elements using the p-box (probability box) approach. This approach ensures a more conservative (interval-based) reliability assessment made within the framework of attaining practical objectives of the reliability analysis of planar trusses and their elements. The truss is analyzed as a provisional sequential mechanical system (in the language of the theory of reliability) consisting of elements that represent reliability values for each individual bar and truss node in terms of all criteria of limit states.
 Materials and methods. The co-authors suggest using p-blocks consisting of two boundary distribution functions designated for modeling random variables in the mathematical models of limit states performed within the framework of the truss reliability analysis instead of independent true functions of the probability distribution of random variables. Boundary distribution functions produce a probability distribution domain in which a true distribution function of a random variable is located. However this function is unknown in advance due to the aleatory and epistemic uncertainty. The choice of a p-block for modeling a random variable will depend on the type and amount of statistical information about the random variable.
 Results. The probabilistic snow load model and the numerical simulation of tests of steel samples of truss rods are employed to show that p-box models are optimal for modeling random variables to solve numerous practical problems of the probabilistic assessment of reliability of structural elements. The proposed p-box snow load model is based on the Gumbel distribution. The mathematical model used to perform the reliability analysis of planar steel truss elements is proposed. The co-authors provide calculation formulas to assess the reliability of a truss element for different types of p-blocks used to describe random variables depending on the amount of statistical data available.
 Conclusions. The application of statistically unsubstantiated hypotheses for choosing the probability distribution law or assessing the parameters of the probability distribution of a random variable leads to erroneous assessments of the reliability of structural elements, including trusses. P-boxes ensure a more careful reliability assessment of a structural element, but at the same time this assessment is less informative, as it is presented in the form of an interval. A more accurate reliability interval requires interval-based assessments of distribution parameters or types of p-boxes applied to mathematical models of the limit state, which entails an increase in the economic and labor costs of the statistical data.

Unmanned Aerial Drones for Inspection of Offshore Wind Turbines: A Mission-Critical Failure Analysis

With increasing global investment in offshore wind energy and rapid deployment of wind power technologies in deep water hazardous environments, the in-service inspection of wind turbines and their related infrastructure plays an important role in the safe and efficient operation of wind farm fleets. The use of unmanned aerial vehicle (UAV) and remotely piloted aircraft (RPA)—commonly known as “drones”—for remote inspection of wind energy infrastructure has received a great deal of attention in recent years. Drones have significant potential to reduce not only the number of times that personnel will need to travel to and climb up the wind turbines, but also the amount of heavy lifting equipment required to carry out the dangerous inspection works. Drones can also shorten the duration of downtime needed to detect defects and collect diagnostic information from the entire wind farm. Despite all these potential benefits, the drone-based inspection technology in the offshore wind industry is still at an early stage of development and its reliability has yet to be proven. Any unforeseen failure of the drone system during its mission may cause an interruption in inspection operations, and thereby, significant reduction in the electricity generated by wind turbines. In this paper, we propose a semiquantitative reliability analysis framework to identify and evaluate the criticality of mission failures—at both system and component levels—in inspection drones, with the goal of lowering the operation and maintenance (O&M) costs as well as improving personnel safety in offshore wind farms. Our framework is built based upon two well-established failure analysis methodologies, namely, fault tree analysis (FTA) and failure mode and effects analysis (FMEA). It is then tested and verified on a drone prototype, which was developed in the laboratory for taking aerial photography and video of both onshore and offshore wind turbines. The most significant failure modes and underlying root causes within the drone system are identified, and the effects of the failures on the system’s operation are analysed. Finally, some innovative solutions are proposed on how to minimize the risks associated with mission failures in inspection drones.

An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi (π) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved π model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.

RAM analysis and availability optimization of thermal power plant water circulation system using PSO

This paper presents reliability, availability, and maintainability (RAM) analysis framework for evaluating the performance of a circulation system of water (WCS) used in a coal-fired power plant (CFPP). The performance of WCS is evaluated using a reliability block diagram (RBD), fault tree analysis (FTA), and Markov birth–death probabilistic approach. In this work, the system under study consists of five subsystems connected in series and parallel configuration namely condensate extraction pump (CEP), low-pressure feed water heater (LPH), deaerator (DR), boiler feed pump (BFP), high-pressure feed water heater (HPH). The reliability block diagram (RBD) and fault tree approach (FTA) have been employed for the performance evaluation of WCS. The Markov probabilistic approach based simulation model is developed. The transition diagram of the proposed model represented several states with full working capacity, reduced capacity, and failed state. The ranking of critical equipment is decided on the basis of criticality level of equipment. The study results revealed that the boiler feed pump affects the system availability at most, while the failure of deaerator affects it least. The availability of the system is optimized using the particle swarm optimization method. The optimized availability parameter (TBF, TTR) based modified maintenance strategy is recommended to enhance the availability of the plant system. The optimized failure rate and repair rate parameters of the subsystem are used to suggest a suitable maintenance strategy for the water circulation system of the thermal power plant. The proposed RAM framework helps the decision-makers to plan the maintenance activity as per the criticality level of subsystems and allocate the resources accordingly.