System Reliability Methods

2018 ◽  
pp. 212-228
Author(s):  
Lloyd W. Condra
Keyword(s):  
System Reliability ◽  
Reliability Methods

A Probabilistic Framework for Gas Turbine Engine Materials With Multiple Types of Anomalies

2011 ◽  
Vol 133 (8) ◽  
Author(s):  
Michael P. Enright ◽  
R. Craig McClung
Keyword(s):  
Gas Turbine ◽  
System Reliability ◽  
Failure Modes ◽  
Gas Turbine Engine ◽  
Probabilistic Framework ◽  
Turbine Engine ◽  
Nickel Based Superalloy ◽  
Kaplan Meier ◽  
Reliability Methods ◽  
Failure Probabilities

Some rotor-grade gas turbine engine materials may contain multiple types of anomalies such as voids and inclusions that can be introduced during the manufacturing process. The number and size of anomalies can be very different for the various anomaly types, each of which may lead to premature fracture. The probability of failure of a component with multiple anomaly types can be predicted using established system reliability methods provided that the failure probabilities associated with individual anomaly types are known. Unfortunately, these failure probabilities are often difficult to obtain in practice. In this paper, an approach is presented that provides treatment for engine materials with multiple anomalies of multiple types. It is based on a previous work that has been extended to address the overlap among anomaly type failure modes using the method of Kaplan–Meier and is illustrated for risk prediction of a nickel-based superalloy. The results can be used to predict the risk of general materials with multiple types of anomalies.

System Reliability Analysis With Autocorrelated Kriging Predictions

2020 ◽  
Vol 142 (10) ◽  
Author(s):  
Hao Wu ◽  
Zhifu Zhu ◽  
Xiaoping Du
Keyword(s):  
System Reliability ◽  
Limit State ◽  
Surrogate Models ◽  
First Order ◽  
Highly Nonlinear ◽  
Reliability Method ◽  
Reliability Methods ◽  
Computationally Intensive ◽  
Improved Accuracy ◽  
State Functions

Abstract When limit-state functions are highly nonlinear, traditional reliability methods, such as the first-order and second-order reliability methods, are not accurate. Monte Carlo simulation (MCS), on the other hand, is accurate if a sufficient sample size is used but is computationally intensive. This research proposes a new system reliability method that combines MCS and the Kriging method with improved accuracy and efficiency. Accurate surrogate models are created for limit-state functions with minimal variance in the estimate of the system reliability, thereby producing high accuracy for the system reliability prediction. Instead of employing global optimization, this method uses MCS samples from which training points for the surrogate models are selected. By considering the autocorrelation of a surrogate model, this method captures the more accurate contribution of each MCS sample to the uncertainty in the estimate of the serial system reliability and therefore chooses training points efficiently. Good accuracy and efficiency are demonstrated by four examples.

A Probabilistic Framework for Gas Turbine Engine Materials With Multiple Types of Anomalies

2010 ◽  
Author(s):  
Michael P. Enright ◽  
R. Craig McClung
Keyword(s):  
Gas Turbine ◽  
System Reliability ◽  
Failure Modes ◽  
Gas Turbine Engine ◽  
Probabilistic Framework ◽  
Turbine Engine ◽  
Nickel Based Superalloy ◽  
Kaplan Meier ◽  
Reliability Methods ◽  
Failure Probabilities

Some rotor-grade gas turbine engine materials may contain multiple types of anomalies such as voids and inclusions that can be introduced during the manufacturing process. The number and size of anomalies can be very different for the various anomaly types, each of which may lead to premature fracture. The probability of failure of a component with multiple anomaly types can be predicted using established system reliability methods provided that the failure probabilities associated with individual anomaly types are known. Unfortunately, these failure probabilities are often difficult to obtain in practice. In this paper, an approach is presented that provides treatment for engine materials with multiple anomalies of multiple types. It is based on previous work that has extended to address the overlap among anomaly type failure modes using the method of Kaplan-Meier, and is illustrated for risk prediction of a nickel-based superalloy. The results can be used to predict the risk of general materials with multiple types of anomalies.

Structural and System Reliability

2022 ◽  
Author(s):  
Armen Der Kiureghian
Keyword(s):  
System Reliability ◽  
Comprehensive Treatment ◽  
Mathematical Concepts ◽  
Background Material ◽  
Reliability Methods ◽  
Systems Simulation ◽  
Stochastic Structural Dynamics ◽  
The University ◽  
Space Variant ◽  
The Ideal

Based on material taught at the University of California, Berkeley, this textbook offers a modern, rigorous and comprehensive treatment of the methods of structural and system reliability analysis. It covers the first- and second-order reliability methods for components and systems, simulation methods, time- and space-variant reliability, and Bayesian parameter estimation and reliability updating. It also presents more advanced, state-of-the-art topics such as finite-element reliability methods, stochastic structural dynamics, reliability-based optimal design, and Bayesian networks. A wealth of well-designed examples connect theory with practice, with simple examples demonstrating mathematical concepts and larger examples demonstrating their applications. End-of-chapter homework problems are included throughout. Including all necessary background material from probability theory, and accompanied online by a solutions manual and PowerPoint slides for instructors, this is the ideal text for senior undergraduate and graduate students taking courses on structural and system reliability in departments of civil, environmental and mechanical engineering.

Complementary Intersection Method for System Reliability Analysis

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Byeng D. Youn ◽  
Pingfeng Wang
Keyword(s):  
Reliability Analysis ◽  
System Reliability ◽  
High Efficiency ◽  
Second Order ◽  
Approximation Formula ◽  
Joint Failure ◽  
System Reliability Analysis ◽  
Reliability Method ◽  
Reliability Methods ◽  
Intersection Method

Although researchers desire to evaluate system reliability accurately and efficiently over the years, little progress has been made on system reliability analysis. Up to now, bound methods for system reliability prediction have been dominant. However, two primary challenges are as follows: (1) Most numerical methods cannot effectively evaluate the probabilities of the second (or higher)–order joint failure events with high efficiency and accuracy, which are needed for system reliability evaluation and (2) there is no unique system reliability approximation formula, which can be evaluated efficiently with commonly used reliability methods. Thus, this paper proposes the complementary intersection (CI) event, which enables us to develop the complementary intersection method (CIM) for system reliability analysis. The CIM expresses the system reliability in terms of the probabilities of the CI events and allows the use of commonly used reliability methods for evaluating the probabilities of the second–order (or higher) joint failure events efficiently. To facilitate system reliability analysis for large-scale systems, the CI-matrix can be built to store the probabilities of the first- and second-order CI events. In this paper, three different numerical solvers for reliability analysis will be used to construct the CI-matrix numerically: first-order reliability method, second-order reliability method, and eigenvector dimension reduction (EDR) method. Three examples will be employed to demonstrate that the CIM with the EDR method outperforms other methods for system reliability analysis in terms of efficiency and accuracy.

Transmission System Reliability Methods

1972 ◽  
Vol PAS-91 (2) ◽  
pp. 628-637 ◽  
Author(s):  
Murty Bhavaraju ◽  
Roy Billinton
Keyword(s):  
System Reliability ◽  
Transmission System ◽  
Reliability Methods
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