An Integrated Performance Measure Approach for System Reliability Assessment

2014 ◽  
Author(s):  
Zequn Wang ◽  
Pingfeng Wang
Keyword(s):  
System Reliability ◽  
Limit State ◽  
Reliability Assessment ◽  
Failure Surface ◽  
Performance Measure ◽  
State Function ◽  
Analysis Tool ◽  
Performance Measure Approach ◽  
Performance Function ◽  
Integrated Performance

This paper presents an integrated performance measure approach (iPMA) for system reliability assessment considering multiple dependent failure modes. An integrated performance function is developed to envelope all component level failure events, thereby enables system reliability approximation by considering only one integrated system limit state. The developed integrated performance function possesses two critical properties. First, it represents exact joint failure surface defined by multiple component failure events, thus no error will be induced due to the integrated limit state function in system reliability computation. Second, smoothness of the integrated performance on system failure surface can be guaranteed, therefore advanced response surface techniques can be conveniently employed for response approximation. With the developed integrated performance function, the maximum confidence enhancement based sequential sampling method is adopted as an efficient component reliability analysis tool for system reliability approximation. To furthermore improve the computational efficiency, a new constraint filtering technique is developed to adaptively identify active limit states during the iterative sampling process without inducing any extra computational cost. One case study is used to demonstrate the effectiveness of system reliability assessment using the developed iPMA methodology.

An Integrated Performance Measure Approach for System Reliability Analysis

2015 ◽  
Vol 137 (2) ◽  
Author(s):  
Zequn Wang ◽  
Pingfeng Wang
Keyword(s):  
System Reliability ◽  
Failure Modes ◽  
Reliability Assessment ◽  
Surrogate Models ◽  
Performance Measure ◽  
System Level ◽  
Performance Measure Approach ◽  
Component Failure ◽  
Sample Points ◽  
Integrated Performance

This paper presents a new adaptive sampling approach based on a novel integrated performance measure approach, referred to as “iPMA,” for system reliability assessment with multiple dependent failure events. The developed approach employs Gaussian process (GP) regression to construct surrogate models for each component failure event, thereby enables system reliability estimations directly using Monte Carlo simulation (MCS) based on surrogate models. To adaptively improve the accuracy of the surrogate models for approximating system reliability, an iPM, which envelopes all component level failure events, is developed to identify the most useful sample points iteratively. The developed iPM possesses three important properties. First, it represents exact system level joint failure events. Second, the iPM is mathematically a smooth function “almost everywhere.” Third, weights used to reflect the importance of multiple component failure modes can be adaptively learned in the iPM. With the weights updating process, priorities can be adaptively placed on critical failure events during the updating process of surrogate models. Based on the developed iPM with these three properties, the maximum confidence enhancement (MCE) based sequential sampling rule can be adopted to identify the most useful sample points and improve the accuracy of surrogate models iteratively for system reliability approximation. Two case studies are used to demonstrate the effectiveness of system reliability assessment using the developed iPMA methodology.

Inversion analysis to determine design parameters for reliability assessment in pavement structures

2014 ◽  
Vol 41 (10) ◽  
pp. 845-855 ◽  
Author(s):  
Sungho Mun
Keyword(s):  
Reliability Index ◽  
Limit State ◽  
Reliability Assessment ◽  
Pavement Performance ◽  
Design Parameters ◽  
State Function ◽  
Limit State Function ◽  
Performance Function ◽  
Pavement Structures ◽  
Inversion Analysis

Reliability assessment has been used to evaluate the performance of pavement structures. However, probabilistic inversion analysis of pavement structure design has not yet been tested to determine the design parameters of the pavement performance function, given a specified reliability index. In this study, a limit state function numerical calculation and the inversion technique of the Nelder–Mead simplex algorithm were used to determine the design parameters for the pavement performance function. The method of moments was used to develop the forward limit state function, which was then compared to Monte Carlo simulations; the comparison indicated good agreement between the two methods. Additionally, several cases were studied to determine the design parameters of the pavement performance function for the reliability index specified in this study. The case studies indicated that the structure number significantly affected the pavement performance function.

Reliability Analysis of Compliant Offshore Tower Under Earthquake Loads

2009 ◽  
Author(s):  
Syed Danish Hasan ◽  
Nazrul Islam ◽  
Khalid Moin
Keyword(s):  
Seismic Analysis ◽  
Limit State ◽  
Reliability Assessment ◽  
Failure Surface ◽  
Probabilistic Methods ◽  
State Function ◽  
Limit State Function ◽  
Universal Joint ◽  
Sea Bed ◽  
Water Particle Kinematics

Articulated offshore tower with universal joints in the intermediate level leads to a multi-hinged configuration that can be used for a variety of deep water application. They are flexibly linked to the sea-bed by a universal joint and comply with the oscillatory environmental loads causing large fluctuating seismic demands at the articulating joints. This paper investigates the dynamic response and the reliability assessment of articulated joint (s) of such structures under seismic sea environment. The analysis includes the influence of sea bed shaking on the water-particle kinematics by using Californian earthquakes. The sea state is characterized by DNV version of Pierson Moskowitz spectrum. The dynamic equation of motion is derived using Lagrangian approach, taking into the account the nonlinearities associated with structure and loads. A limit-state function for seismic demand for a universal joint has been derived. Using the derived limit-state function and the responses obtained after time-domain seismic analysis, reliability assessment of the articulated joint has been carried out, using efficient MPP-based probabilistic methods. Design point, important for probabilistic design of articulated joint, located on the failure surface has been worked out. Stochastic sensitivity analysis has been performed to assess the relative importance of design parameter on the stochastic response of articulated joint.

scholarly journals Semi-Supervised Deep Learning for High-Dimensional Uncertainty Quantification

2020 ◽  
Author(s):  
Zequn Wang ◽  
Mingyang Li
Keyword(s):  
Uncertainty Quantification ◽  
Reliability Analysis ◽  
Supervised Learning ◽  
Dimensional Space ◽  
Limit State ◽  
Failure Surface ◽  
Simulation Method ◽  
High Dimensional ◽  
State Function ◽  
Latent Space

Abstract Conventional uncertainty quantification methods usually lacks the capability of dealing with high-dimensional problems due to the curse of dimensionality. This paper presents a semi-supervised learning framework for dimension reduction and reliability analysis. An autoencoder is first adopted for mapping the high-dimensional space into a low-dimensional latent space, which contains a distinguishable failure surface. Then a deep feedforward neural network (DFN) is utilized to learn the mapping relationship and reconstruct the latent space, while the Gaussian process (GP) modeling technique is used to build the surrogate model of the transformed limit state function. During the training process of the DFN, the discrepancy between the actual and reconstructed latent space is minimized through semi-supervised learning for ensuring the accuracy. Both labeled and unlabeled samples are utilized for defining the loss function of the DFN. Evolutionary algorithm is adopted to train the DFN, then the Monte Carlo simulation method is used for uncertainty quantification and reliability analysis based on the proposed framework. The effectiveness is demonstrated through a mathematical example.

A Novel Time-Variant Reliability Analysis Method Based on Failure Processes Decomposition for Dynamic Uncertain Structures

2018 ◽  
Vol 140 (5) ◽  
Author(s):  
Shui Yu ◽  
Zhonglai Wang
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Dynamic Properties ◽  
Limit State ◽  
Quadratic Function ◽  
State Function ◽  
Analysis Method ◽  
Engineering Structures ◽  
Reliability Problem ◽  
Time Invariant

Abstract Due to the uncertainties and the dynamic parameters from design, manufacturing, and working conditions, many engineering structures usually show uncertain and dynamic properties. This paper proposes a novel time-variant reliability analysis method using failure processes decomposition to transform the time-variant reliability problems to the time-invariant problems for dynamic structures under uncertainties. The transformation is achieved via a two-stage failure processes decomposition. First, the limit state function with high dimensional input variables and high order temporal parameters is transformed to a quadratic function of time based on the optimized time point in the first-stage failure processes decomposition. Second, based on the characteristics of the quadratic function and reliability criterion, the time-variant reliability problem is then transformed to a time-invariant system reliability problem in the second-stage failure processes decomposition. Then, the kernel density estimation (KDE) method is finally employed for the system reliability evaluation. Several examples are used to verify the effectiveness of the proposed method to demonstrate its efficiency and accuracy.

Reliability Assessment Based on the Concept of Failure Surface Frontier

2005 ◽  
Author(s):  
Songqing Shan ◽  
G. Gary Wang
Keyword(s):  
High Efficiency ◽  
Limit State ◽  
Reliability Assessment ◽  
Failure Surface ◽  
Test Results ◽  
Limit States ◽  
Failure Region ◽  
Novel Concept ◽  
Quadratic Approximations ◽  
State Functions

This work proposes a novel concept of failure surface frontier (FSF), which is a hyper-surface consisting of the set of the non-dominated failure points on the limit states of a given failure region. FSF better represents the limit state functions for reliability assessment than conventional linear or quadratic approximations on the most probable point (MPP). Assumptions, definitions, and benefits of FSF are discussed first in detail. Then, a discriminative sampling based algorithm was proposed to identify FSF, from which reliability is assessed. Test results on well known problems show that reliability can be accurately estimated with high efficiency. The algorithm is also effective for problems of multiple failure regions, multiple most probable points (MPP), or failure regions of extremely small probability.

A Time-Variant Reliability Analysis Method Based on Stochastic Process Discretization

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
C. Jiang ◽  
X. P. Huang ◽  
X. Han ◽  
D. Q. Zhang
Keyword(s):  
Stochastic Process ◽  
Reliability Analysis ◽  
System Reliability ◽  
Limit State ◽  
Complex Structure ◽  
Random Variable ◽  
State Function ◽  
Analysis Method ◽  
Reliability Problem ◽  
Reliability Method

Time-variant reliability problems caused by deterioration in material properties, dynamic load uncertainty, and other causes are widespread among practical engineering applications. This study proposes a novel time-variant reliability analysis method based on stochastic process discretization (TRPD), which provides an effective analytical tool for assessing design reliability over the whole lifecycle of a complex structure. Using time discretization, a stochastic process can be converted into random variables, thereby transforming a time-variant reliability problem into a conventional time-invariant system reliability problem. By linearizing the limit-state function with the first-order reliability method (FORM) and furthermore, introducing a new random variable, the converted system reliability problem can be efficiently solved. The TRPD avoids the calculation of outcrossing rates, which simplifies the process of solving time-variant reliability problems and produces high computational efficiency. Finally, three numerical examples are used to verify the effectiveness of this approach.

scholarly journals Probabilistic Approach to System Reliability of Mechanism with Correlated Failure Models

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Xianzhen Huang ◽  
Yimin Zhang
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
Failure Modes ◽  
Limit State ◽  
Probabilistic Approach ◽  
Upper And Lower Bounds ◽  
State Function ◽  
System Reliability Analysis ◽  
Kinematic Performance ◽  
Planar Linkages

In this paper, based on the kinematic accuracy theory and matrix-based system reliability analysis method, a practical method for system reliability analysis of the kinematic performance of planar linkages with correlated failure modes is proposed. The Taylor series expansion is utilized to derive a general expression of the kinematic performance errors caused by random variables. A proper limit state function (performance function) for reliability analysis of the kinematic performance of planar linkages is established. Through the reliability theory and the linear programming method the upper and lower bounds of the system reliability of planar linkages are provided. In the course of system reliability analysis, the correlation of different failure modes is considered. Finally, the practicality, efficiency, and accuracy of the proposed method are shown by a numerical example.

Reliability Analysis of Pressure Vessels in Lubricant Process Unit for Risk Based Inspection

2008 ◽  
Author(s):  
Chi-Hui Chien ◽  
Chun-Hung Chen
Keyword(s):  
Reliability Analysis ◽  
Pressure Vessel ◽  
Safety Concern ◽  
Limit State ◽  
Reliability Assessment ◽  
Pressure Vessels ◽  
State Function ◽  
Process Unit ◽  
Limit State Function ◽  
Failure Probabilities

As a safety concern to a pressurized system, to monitor the corrosion rate of each pressure vessel in order to make the repair decision at the right time based on the required thickness to withstand the maximum allowable working pressure (MAWP), is important to the plant owner. A plant inspector will normally assess the risk by evaluating the probability of failure of each pressure vessel during service hours with inspection and maintenance planning. Therefore, a scheme of reliability assessment to the pressure vessels should be established. The objective of this study is to discuss the failure probabilities of the pressure vessels in a lubricant unit in order to provide the input information for Risk Based Inspection (RBI) assessments. The reliability assessment of a pressure vessel involves the estimation of the failure pressure and evaluation of the limit state function. Based on the formula for calculating required thickness of a pressure vessel component, and due to the presence of non-linearity in the limit state function and the non-normal distributed variables, the first order second moment method (FOSM) was adopted for carrying out the reliability analysis. The uncertainties of the random variables in the limit state function were modeled by using normal and non-normal probabilistic distributions. As the heat exchanger is an important pressure vessel to a pressurized system, the failure probabilities together with the ranking categories of the heat exchangers in a lubricant unit are chosen as a case study to be discussed and presented in this paper.

A reliability assessment approach for slab track structure based on vehicle-track dynamics and surrogate model

2021 ◽  
pp. 1748006X2110284
Author(s):  
Zai-Wei Li ◽  
Xiao-Zhou Liu ◽  
Si-Xin Chen
Keyword(s):  
Surrogate Model ◽  
Limit State ◽  
Reliability Assessment ◽  
Support Vector ◽  
Inversion Method ◽  
Geometric Condition ◽  
State Function ◽  
Slab Track ◽  
Track Dynamics ◽  
Track Irregularity

The reliability assessment of rail infrastructure is directly related to the safety and effectiveness of railway transportation and critical for railway management department. This paper presents an approach for service reliability analysis of slab track from the point of view of the vehicle-track system operational safety. The vehicle-track dynamics is simulated by the established finite element model (FEM) and the limit state function (LSF) is defined on the danger of derailment. To reflect the real state of track geometric condition, the track irregularity spectrums (TISs) are extracted from the measured track irregularity data and fitted by the seven-parameter formula. Then, a set of time series can be obtained from the TISs using a binary wavelet-based inversion method and then input to the FEM to obtain the value of LSF. Finally, Monte Carlo simulation (MCS) is used in the calculation of track reliability. To overcome the slow convergence of the vehicle-track models, this study develops a surrogate model based on support vector machine (SVM). It is validated that the established SVM can well approximate the relationship between the track irregularities to the derailment coefficients in terms of small error and high correlation. More importantly, the efficiency is more than 1000 times higher than traditional FEM. This is a pioneering study to incorporate vehicle-track dynamics into the reliability assessment of slab track in service. For a 1024-m section of slab track, results show that its reliability index can satisfy the requirement in specifications.

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