Framework For Reliability Assessment Research Articles

Damage-driven framework for reliability assessment of steam turbine rotors operating under flexible conditions

The high-temperature rotating structures (HTRS), e.g., steam turbine rotors, often operate in extremely harsh environments with a flexible load condition during peak shaving of power system. In this work, a damage-driven framework for reliability assessment is developed in terms of the cumulative damage-damage threshold interference (CD-DT) principle, in which the cumulative damage and damage threshold are regarded as two random parameters to address uncertainties. The CD-DT principle is founded on the engineering damage theory and incorporates physics-of-failure into the probabilistic modeling of high-temperature structural reliability. Probabilistic damage analysis, correlation analysis of weak sites, system-level reliability analysis, and sensitivity analysis have been encompassed in this framework. Three numerical examples are used to verify the effectiveness and applicability of the proposed framework. Application to steam turbine rotor involving multiple weak sites with multi-damage modes illustrate the implementation procedures of the framework. Results show that the reliability-based design life of rotor decreases with the increases of start-stop frequency, the implementation of a two-shift operation would pose a threat to meeting the safety requirement of a 30-year design life. Furthermore, sensitivity analysis highlights the critical influences of initial rotor temperature and speed rising rate on rotor reliability, providing insights for operational maintenance and reliability optimization.

A data-driven underground gas storage production system string failure prediction model for time-varying reliability analysis

In underground gas storage (UGS) systems, the production tubing string system undergoes complex thermo-mechanical loading conditions, leading to fatigue damage accumulation and potential failure risks. To accurately assess the fatigue reliability and mitigate risks, it is crucial to develop a data-driven physics-informed uncertainty modeling approach that captures the coupled thermo-mechanical effects and accounts for various uncertainties. This study proposes a data-driven physics-informed modeling method based on thermo-mechanical coupling for fatigue reliability assessment and risk mitigation in critical UGS systems. A quantitative analysis method is introduced, which relies on small-scale multi-stage loading experiments and combines the stochastic degradation process with time-series reliability theory. To analyze the fatigue life of joints under complex loading conditions, a modified S-N curve model is developed. This model takes into account the influences of the dimension coefficient, stress concentration factors, surface finish coefficients, and thermal load. It considers the uncertainties associated with different temperature loads and surface finish on fatigue life. By integrating physics-informed modeling, uncertainty quantification, and fatigue damage accumulation analysis, this approach provides a comprehensive framework for fatigue reliability assessment and risk mitigation in critical UGS systems. It enables informed decision-making for safe and reliable operations, as well as optimized maintenance strategies, ultimately enhancing the overall system performance and mitigating potential failures.

Creep reliability assessment of structural components at elevated temperatures considering the time dependent feature of representative stress

Creep reliability assessment of structural components at elevated temperatures is essential to guarantee the long-term safe operation of the system. Current studies are limited to continuum damage mechanics methods at the material level, while the reliability assessment method for creep design at the component level is rarely reported. In this work, the framework for creep reliability assessment of structural components is extended, where the time dependent feature of the representative stress is included. The effect of the time dependent feature of the representative stress on creep reliability assessment is discussed. Sensitivity analyses of material parameters on creep reliability assessment results are conducted based on the Sobol and Morris global methods. Results indicate that for the same creep design life, the component presents a higher failure probability when the time dependent feature of the representative stress is considered. Parameters D and d in the creep rupture life equation have more significant effects on creep rupture life than other parameters for the case studied.

Global reliability assessment of coupled transmission tower-insulator-line systems considering soil-structure interaction subjected to multi-hazard of wind and ice

This study aims to assess the global reliability of coupled transmission tower-insulator-line systems considering soil-structure interaction (CTTILSs-SSI) subjected to multi-hazard of wind and ice. Firstly, two simplified models of CTTILSs-SSI under stochastic wind and ice loads are established, and their governing equations are derived through the Lagrange equation. On this basis, a global reliability assessment framework of CTTILSs-SSI is proposed based on the improved maximum entropy method, in which the global performance function of CTTILSs-SSI is defined via the state variable description method. Finally, a practical ultra-high voltage overhead transmission line located on the icing zone is chosen to illustrate the proposed framework. Moreover, to investigate the influence of soil-structure interaction (SSI) and soil types on the global reliability of transmission tower-insulator-line systems (TTILSs), the global reliability of CTTILSs-SSI with various soil types is compared with that of a fixed base system. The results indicate that under the in-plane wind and ice loads, the global failure probability of TTILSs is almost zero, and it is essentially not affected by the SSI effect and soil types. However, under the out-of-plane wind and ice loads, the global failure probability of CTTILSs-SSI is higher than that of the fixed base system, and the global failure probability of CTTILSs-SSI increases as the soil stiffness decreases. Accordingly, it may be more reasonable to consider the SSI effect into the global reliability analysis of TTILSs subjected to ice and out-of-plane wind loads, particularly when the soil is soft.

Buckling Instability of Monopiles in Liquefied Soil via Structural Reliability Assessment Framework

Buckling Instability of Monopiles in Liquefied Soil via Structural Reliability Assessment Framework

Distributed-collaborative surrogate modeling approach for creep-fatigue reliability assessment of turbine blades considering multi-source uncertainty

This paper proposes a substructure-based distributed-collaborative surrogate modeling approach for improving the accuracy and efficiency in the estimation of the creep-fatigue reliability of gas turbine blades with the integration of the Least Absolute Shrinkage and Selection Operator (LASSO), kriging, Distributed Collaborative (DC) strategy and substructure simulation method, called as the substructure-based DCLKM. Further, the creep-fatigue reliability assessment framework is set by combining the proposed substructure-based DCLKM with some theoretical models, which takes into account the uncertainties of structural sizes, applied loads and material properties and the nonlinearity of creep/fatigue damage interaction. Firstly, the total strain range, mean stress, absolute temperature and maximum stress at the critical location are regarded as the first-level output responses, and their required samples are predicted through the fitted surrogate models. Secondly, the creep life and Low-Cycle Fatigue (LCF) life are regarded as the second-level output responses and predicted. Finally, the creep-fatigue damage and reliability assessment are performed for several given applied cycles, respectively. The proposed substructure-based DCLKM is validated on a case study. The results show that the proposed substructure-based DCLKM is feasible with high accuracy and efficiency in the creep-fatigue reliability assessment of turbine blades.

Performance-based target reliability analysis of offshore wind turbine mooring lines subjected to the wind and wave

Reliability assessment is a crucial aspect of the design and operation of structures, particularly in balancing safety and cost considerations. This paper introduces a novel method for evaluating the performance-based target reliability of floating wind turbine platforms in offshore environments. The method focuses on the platform's motion modes and wave frequencies, which significantly influence the system's structural integrity and performance. An improved limit state function is proposed to enhance the accuracy of reliability calculations, specifically for steady-state conditions. The platform's six degrees of freedom motions are carefully analyzed to investigate their dependence on wave frequencies. By considering the time response of these motions and accounting for uncertainties in wave characteristics, wave impact directions, and wind effects, a comprehensive reliability analysis is conducted to assess the stability modes of the platform. This paper introduces the term 'Reliability Performance-Based' (RPB) analysis as a new concept to evaluate the system's reliability at a given performance level. Furthermore, an optimal target reliability index is defined to address the economic aspect of the design process. The proposed methodology's PEB analysis focuses on capturing uncertainties in wave characteristics and wind effects on floating wind turbine platforms. This includes a detailed examination of wave and wind-induced loads and their propagation through the system concerning its performance level. Statistical models were integrated to quantify these uncertainties, applying Monte Carlo simulations to assess their effects on the platform's reliability. This approach allows for a nuanced understanding of the interactions between environmental factors and structural responses, enhancing the precision of our reliability assessments. It enables the consideration of economic efficiency alongside safety, ensuring a balanced approach to the design and operation of the floating wind turbine platform. By providing a comprehensive reliability assessment framework, it aids in the optimization of design and decision-making processes for floating wind turbine platforms.

ParInfoGPT: An LLM-based two-stage framework for reliability assessment of rotating machine under partial information

Data-driven approaches on large-amount labelled data have been actively implemented for reliability assessment of rotating machine, i.e., with limited labelled data samples. Recently, the study of large language models (LLMs) has demonstrated outstanding performance in the field of natural language processing. Inspired by the LLM for addressing sequential data, this article investigates the implementation of LLM under partial information for reliability assessment of rotating machine. A novel LLM-based two-stage framework, called ParInfoGPT, is proposed by integrating a self-supervised reconstruction network and a weakly supervised classification network. Additionally, a mutual information (MI)-based informative masking strategy for pre-training and a parallel side-adapter (PSA) for fine-tuning are designed to effectively learn the proposed framework. Experiments are systematically conducted and evaluated on two real-world datasets of rotating machine. The experimental results of the proposed methodology demonstrate its superior performance on fault diagnosis under partial information for reliability assessment.

Evaluating Reliability and Economics of EV Charging Configurations and Deep Reinforcement Learning in Robotics and Autonomy

Growing EV popularity drives companies to focus on reliable charging station designs despite challenges in maintaining reliability. A proposed 36-ported design combines uniform and non-uniform port arrangements, tested with 50-350 kW systems. Failure rates are estimated using MILHDBK217F and MILHBK-338B standards, assessing port reliability and station success rates through binomial distribution and cost analysis. This design improves voltage stability and reduces maintenance costs through enhanced port reliability. In robotics and autonomous systems, Deep Reinforcement Learning (DRL) excels but faces challenges from unsafe policies leading to hazardous decisions. This study introduces a reliability assessment framework for DRL-controlled systems, using formal neural network analysis. A two-level verification approach evaluates safety locally using reachability tools and globally by aggregating local safety metrics across tasks. Experimental validation confirms the framework's effectiveness in enhancing RAS safety.

A four-stage fast reliability assessment framework for renewables-dominated strong power systems with large-scale energy storage by temporal decoupling and contingencies filtering

Reliability assessment for renewables-dominated strong power systems presents significant challenges, primarily due to the overwhelming computational demands posed by large-scale renewable generation and energy storage. In response to the limitations and lack of scalability in traditional methods, this study introduces a novel four-stage fast reliability assessment framework tailored for renewables-dominated strong power systems. First and foremost, the pre-dispatch and temporal decoupling of energy storage serve as vital foundations for the fast assessment framework. Following the pre-dispatch model, an adaptive scenario clustering technique is proposed to eliminate redundant scenarios while retaining extreme ones. Consequently, a quick verification process for contingencies is established, employing DC optimal power flow in the worst-case scenario to identify potential load shedding. Then, all contingencies are filtered to only effective ones for reliability indices computation. Finally, a modified reliability calculation model with linear AC power flow is built using the filtered contingency set and reduced scenarios. The efficacy of this approach is validated through four numerical experiments, demonstrating impressive efficiency and computation feasibility. For accuracy, the proposed method shows a 3.7% average error in a modified IEEE RTS system, and for efficiency, the proposed method finishes N-2 analysis in 424 s for a 62-bus-212-line case as well as 4924 s for a 200-bus-490-line case.

Measurement error prediction-based reliability assessment framework for electric metering devices under harsh natural environments

Reliability assessment for electric metering devices (EMDs), which includes environmental stress analysis, measurement error (ME) prediction, and reliability estimation, can be utilized for predictive instrument maintenance, especially under harsh environmental stresses. Nevertheless, the actual evaluation process is limited by data noise and the unavailability of future environmental inputs for the degradation model. To this end, we first extract the main environmental factors affecting ME and then fuse them using the weighted principal component analysis (WPCA) method to provide future environmental input values for the prediction model. Next, a bi-directional (BD) outlier detection method based on MCD robust analysis and the Thompson tau method is proposed to detect outliers from horizontal and longitudinal perspectives. Then, an ME prediction method called multi-stress fusion nonlinear degradation (MFND) is put forward, which considers the cyclic variation of ME over time and the effect of environmental stresses on ME. Real-world datasets from a high-dry-hot area manifest that our proposed BD-based MFND reliability assessment framework enjoys pleasurable prediction performance. Compared to some well- known methods, our framework excels in outlier detection and ME prediction.

Generic fully coupled framework for reliability assessment of offshore wind turbines under typical limit states

Offshore wind turbines (OWTs) generally experience harsh marine environments with uncertainties in marine loads, soil properties, and material properties; therefore, the structural integrity assessment of OWTs is a challenging task. Most of the previously proposed methods for reliability assessment are time-consuming and neglect the coupling effects of aerodynamics, hydrodynamics, and OWT structural dynamics under complex environmental loads. In this study, a fully coupled framework for the reliability assessment of OWTs is proposed, in which a dynamic simulation tool in the time domain is combined with surrogate models. The established Kriging, multivariate regression, and support vector regression surrogate models are compared to determine the most feasible model. Furthermore, the influence of different solving methods, such as first-order reliability method (FORM), importance sampling (IS), and subset simulation (SS), on the estimation of structural reliability indexes are discussed. The superiority of the estimated reliability indexes in the safety assessment of OWTs is proved using the SS method based on the Kriging surrogate model. Subsequently, the potential failure modes of an OWT under different operational states are revealed, indicating that excessive tower vibrations must be eliminated for stable power production from OWTs. Furthermore, the SS method is found to be feasible as it ensures the estimation accuracy of the reliability indexes of the parked OWT based on Kriging and the other surrogate models. Finally, the significance of soil–structure interactions in the reliability index estimation is investigated, and the equivalent coupled spring boundary is proposed from the perspectives of structural safety and simulation accuracy.

An adaptive hybrid deep learning-based reliability assessment framework for damping track system considering multi-random variables

Reliability assessment is essential in the design and management of rail transit system (RTS), with a heightened focus on damping tracks that feature low-stiffness structures and various uncertainties. However, existing research on RTS has notable gaps: one is the unexplored nonlinear track irregularities in surrogate model, while the other is the significant computational burden of current reliability analysis. Therefore, Firstly, based on a database acquired from simulations of rigid-flexible coupling dynamic models, a hybrid deep learning (DL) surrogate model is developed and trained. Notably, it accommodates both time-series and feature parameters data as inputs, and integrates a novel algorithm featuring slide window with variational decomposition mode-sample entropy, optimization algorithm and adaptive learning strategy (AS). Subsequently, an advanced reliability assessment framework is proposed, utilizing AS hybrid DL-based Probability Density Evolution Method (PDEM), which thoughtfully designed to analyze the two aforementioned categories of random variables and then applied to a train-steel spring floating slab track. Experimental studies show the proposed surrogate model effectively and robustly predicts four safety metrics, with each component's excellence confirmed. Furthermore, this framework shows higher accuracy than the Monte Carlo Method and enhances computational efficiency by 10 ∼ 30 times compared to traditional mechanism-based PDEM. Consequently, reliability assessments indicate values of 0.9312 and 0.9214 for wheel-load reduction rate and rail displacement, respectively, with other metrics showing zero safety hazards. Findings of this study have practical applicability in the field of RTS design and maintenance.

A Methodological Framework for Structural Reliability Assessment of Marine Structural Elements

The aim of this paper is to provide a robust framework to assist researchers in deciding which methods to use, depending on the problem at hand, in order to estimate the probability of failure of marine structural parts that are subjected to variable loads (both hull-girder and local pressure loads) that exhibit uncertainties in their material properties and that involve fabrication-related uncertainties. The limitations of analytical approaches both in deterministic mathematical modeling (strength formulas) and probabilistic estimation will be provided, and respective computational tools will be demonstrated (FEA and Monte Carlo simulation). The approach is showcased in flat and bow-defected rectangular plates through analytical and numerical approaches.

Simulation of non-stationary ground motions and its applications in high concrete faced rockfill dams via direct probability integral method

Many high concrete face rockfill dams (CFRDs) have been constructed in earthquake-prone areas in recent years, and it is essential to evaluate the seismic safety performance of these dams considering the randomness of seismic excitation. In this paper, the relationship between the stochastic ground motion generation method and the direct probability integral method (DPIM) is established by using the number theory point selection method as the bridge. The DPIM is introduced into the field of stochastic dynamic analysis and multi-index reliability evaluation of high CFRD for the first time. First, a non-stationary stochastic excitation model at five sites is established based on the latest standard for the seismic design of hydraulic structures. Then, taking a practical project as an example, stochastic dynamic response analysis and reliability assessment based on dam crest settlement deformation and dam slope cumulative slippage are conducted by using DIPM. Finally, the influence of these two indexes on reliability is explored by drawing the equal failure probability curve. The results show that the stochastic dynamic analysis and reliability assessment framework established in this paper can provide probabilistic seismic safety assessments for high CFRDs. Sliding damage of the dam slope is more likely than the deformation damage of the rockfill from the perspective of probability.

Reliability of inelastic wind excited structures by dynamic shakedown and adaptive fast nonlinear analysis (AFNA)

The ever-growing interest in performance-based wind engineering has created a need for assessment frameworks that can efficiently deal with inelasticity. The computationally efficient strain-driven dynamic shakedown approach has provided a solution that is not only capable of identifying failure mechanisms that are potentially critical during extreme winds, e.g., low cycle fatigue and ratcheting, but also allows direct estimation of inelastic deformations. This approach, however, can only solve problems at dynamic shakedown, i.e., with limited nonlinearity, and is not capable of providing response time histories. To address these limitations, this paper presents an efficient framework for reliability assessment of inelastic structures at dynamic shakedown and beyond. To this end, a novel step-by-step integration algorithm is developed for rapid response time history analysis within the setting of dynamic shakedown. The method is based on advancing fast nonlinear analysis through introducing schemes for enabling at each time step the adaptive selection of the step size, number of modes to be included, and number of potentially nonlinear elements. Inelasticity is modeled as distributed at the level of the stress resultants through a return mapping scheme based on the Haar–Kàrmàn principle, therefore enabling the integrated estimation of the state of dynamic shakedown. The scheme is seen to preserve the efficiency of recently developed strain-driven dynamic shakedown algorithms while providing a full range of response time histories at and beyond the state of dynamic shakedown. To enable reliability analysis, the scheme is embedded in a general uncertainty propagation framework.

Mixed Bayesian Network for reliability assessment of RC structures subjected to environmental actions

Under environmental action, reinforced concrete (RC) structures might suffer from reinforcement corrosion caused by the surrounding environment, dramatically reducing structural reliability and threatening social development. However, most of the existing reliability assessment methods for RC structures only focused on the structural performance at the design stage given the original unchanged environment, ignoring the effects of realistic exposure conditions and inspection results on reliability evaluation. Thus, this paper develops a general reliability assessment framework based on a Mixed Bayesian network (MBN), incorporating three modules, i.e., durability assessment, load-bearing capacity analysis, and time-dependent reliability analysis. In MBN, separate sub-BNs are built based on different modules and connected by pinch point variables where probabilistic information is transmitted via soft evidence. Besides, this framework considers time-dependent environmental parameters and two-dimensional chloride transport and their effects on reliability. Meanwhile, adjustment coefficients are applied to improve the results of the analytical mechanical model with respect to different limit states through the finite element model (FEM). The proposed MBN framework is illustrated for a corroded RC beam under a marine atmospheric environment to investigate the effects of environmental modeling, chloride transport patterns, and concrete crack inspection on reliability assessment. The results indicate that under the assumed conditions in the case study, early inspection of large cracks may significantly overestimate the failure probability by about 500%. Besides, failure probability might be underestimated by about 95%, ignoring the time-variant environment and two-dimensional chloride transport.

Data-Driven Multi-Criteria Assessment Framework for Analyzing the Reliability of Bus Services

Intelligent systems have been extensively used to improve the reliability of transport services as a result of technological advances. Despite the technical and methodological achievements, public transportation companies are still facing excessive challenges in assessing the performance and reliability of the system. This study establishes a data-driven multi-criteria decision-making model for prioritizing bus routes that illustrates both operator and consumer views on bus routes. The multi-criteria fuzzy outranking process is handled by ELECTRE III and Condorcet methods. The developed model utilizes alternative indices of bus travel-time reliability to fully capture the uncertain nature of the input data. The reliability assessment framework is based on automatic vehicle location (AVL) data which works as an effective evaluation system for enhanced service reliability on different routes network-wide. Using this model, bus transport companies can set a benchmark and a reliable ranking system for their bus routes. This hybrid prioritization framework is used for characterizing and enhancing transport network efficiency. The effectiveness of the model is examined by quantifying the reliability of eight bus routes controlled by the Qazvin public transportation system, in Iran. A wide range of AVL data sources is employed within an in-depth statistical analysis based on both user and operator preferences. According to the concordance matrix results, line 18 has been found to be superior to other bus routes, and the possibility of identifying less efficient bus routes has been fulfilled.

Impact of cyber failures on operation and adequacy of Multi-Microgrid distribution systems

The primary objective of this paper is to create a suitable reliability assessment framework for Cyber–Physical Multi-MicroGrid (CPMMG) distribution systems, considering the device-level failures of control and protection systems. For this purpose, the typical structure of a CPMMG distribution system with a centralized protection system is first exemplified. Then, the interdependencies between cyber and power infrastructures are investigated in such systems. Regarding the protection system, a Markov model is presented to investigate hidden failures of fault location, isolation, and service restoration processes. Possible operation modes in a CPMMG distribution system – normal, islanding, joint, and shutdown modes – are then explained and modeled. Finally, a suitable Monte Carlo simulation-based reliability assessment framework is developed to quantify well-known reliability indices. In addition, two new adequacy indices are proposed: Interrupted but Gained Compensation (IbGC) and Supplied by Expensive Resources (SbER). A comprehensive case study is conducted to reveal the salient features of the proposed framework. The result showed that the impact of cyber failures in such systems is highly dependent on the design of the cyber system. Therefore, the adverse impact of cyber failures of control and protection systems can be effectively mitigated by proper design.

Cascading failure-based reliability assessment for post-seismic performance of highway bridge network

This paper proposes a novel reliability assessment framework for post-seismic performance of highway bridge network that considers cascading failures caused by traffic flow redistribution. Firstly, the fragility curve of bridge is obtained by probabilistic seismic demand model. Then, we propose the impact factor of path choice probability to characterize the cascading failure of the network. The stochastic user equilibrium assignment of highway bridge network traffic flow is calculated based on the impact factor of path choice probability. To evaluate the network's efficiency and identify the congestion-prone links, the Monte Carlo simulation method is introduced to calculate the probability density curves of different influencing parameters. Finally, the framework proposed in this paper is utilized to analyze the congestion-prone links of the transportation network of some areas of Mianyang City and verify its applicability.