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Reliability prediction of engineering systems with competing failure modes due to component degradation

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TLDR
In this paper, the reliability of an engineering system depends on two reliability metrics: the mechanical reliability, considering component failures, that a functional system topology is maintained and the performance reliability of adequate system performance in each functional configuration.
Abstract
Reliability of an engineering system depends on two reliability metrics: the mechanical reliability, considering component failures, that a functional system topology is maintained and the performance reliability of adequate system performance in each functional configuration. Component degradation explains not only the component aging processes leading to failure in function, but also system performance change over time. Multiple competing failure modes for systems with degrading components in terms of system functionality and system performance are considered in this paper with the assumption that system functionality is not independent of system performance. To reduce errors in system reliability prediction, this paper tries to extend system performance reliability prediction methods in open literature through combining system mechanical reliability from component reliabilities and system performance reliability. The extended reliability prediction method provides a useful way to compare designs as well as to determine effective maintenance policy for efficient reliability growth. Application of the method to an electro-mechanical system, as an illustrative example, is explained in detail, and the prediction results are discussed. Both mechanical reliability and performance reliability are compared to total system reliability in terms of reliability prediction errors.

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Citations
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Journal ArticleDOI

Transferable convolutional neural network based remaining useful life prediction of bearing under multiple failure behaviors

TL;DR: In the proposed method, a convolutional neural network is employed to extract the degradation features and multiple-kernel maximum mean discrepancies are integrated into optimization objective to reduce distribution discrepancy.
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Multiple failure behaviors identification and remaining useful life prediction of ball bearings

TL;DR: In the present research, clustering and change point detection algorithm (CPDA) is used for identification of the presence of multiple failure behaviors in the data and results show that identification of failure behavior helps in accurate prediction of RUL.
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Bayesian degradation assessment of CNC machine tools considering unit non-homogeneity

TL;DR: A degradation analysis based reliability assessment method for CNC machine tools under performance testing using the gamma process and parameters of model are updated by Bayesian estimation approach.
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Multidisciplinary robust design optimization based on time-varying sensitivity analysis

TL;DR: In this study, a multidisciplinary robust design optimization method that is based on time-varying sensitivity analysis is proposed and integrated with MDO to reduce the effects of time- varying uncertainties.
Journal ArticleDOI

A Bayesian optimal design for degradation tests based on the inverse Gaussian process

TL;DR: In this paper, the authors investigate the optimal design of the degradation tests on the basis of the inverse Gaussian process and propose an optimal design with pre-estimated planning values of model parameters, and address the issue of uncertainty in the planning values by using the Bayesian method.
References
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Book

Statistical Methods for Reliability Data

Wayne Nelson
TL;DR: In this paper, the use of Bayesian methods for reliability data is discussed and a detailed discussion of the application of these methods in the context of automated life test planning is presented.
Journal ArticleDOI

Statistical Methods for Reliability Data

TL;DR: Statistical Methods For Reliability Data updates and improves established techniques as it demonstrates how to apply the new graphical, numerical, or simulation-based methods to a broad range of models encountered in reliability data analysis.
Book

Reliability in engineering design

TL;DR: This paper presents a meta-modelling framework for Bayesian Reliability in Design and Testing, which automates the very labor-intensive and therefore time-heavy and expensive process of estimating Reliability Measures.
Proceedings ArticleDOI

Cause and analysis of stator and rotor failures in three-phase squirrel-cage induction motors

TL;DR: In this article, the authors attempt to identify the various causes of stator and rotor failures in three-phase squirrel cage induction motors, and a specific methodology is proposed to facilitate an accurate analysis of these failures.
Journal ArticleDOI

The PHI2 method: a way to compute time-variant reliability

TL;DR: This paper presents a method called PHI2 which is based on the outcrossing approach and allows to solve time-variant reliability problems using classical time-invariant reliability tools such as FORM/SORM methods.
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