Celebrating fifty years of physics of failure

2013 ◽  
Author(s):  
K. Chatterjee ◽  
M. Modarres ◽  
J. B. Bernstein ◽  
D. Nicholls
Keyword(s):  
Physics Of Failure

Enhancement of Physics-of-Failure Prognostic Models with System Level Features

2002 ◽  
Author(s):  
Gregory J. Kacprzynski ◽  
Michael J. Roemer ◽  
Girish Modgil ◽  
Andrea Palladino ◽  
Kenneth Maynard
Keyword(s):  
System Level ◽  
Prognostic Models ◽  
Physics Of Failure

Reliability Physics (The Physics of Failure)

1962 ◽  
Author(s):  
Donald R. Earles ◽  
Mary F. Eddins
Keyword(s):  
Physics Of Failure

A Time-Dependent Reliability Estimation Method Based on Gaussian Process Regression

2018 ◽  
Author(s):  
Wang Han ◽  
Xiaoling Zhang ◽  
Xiesi Huang ◽  
Haiqing Li
Keyword(s):  
Machine Learning ◽  
Mechanical System ◽  
Physical Modeling ◽  
Learning Algorithm ◽  
Estimation Method ◽  
Gaussian Process Regression ◽  
Reliability Estimation ◽  
Time Dependent ◽  
Physics Of Failure ◽  
Gear Transmission System

This paper presents a time-dependent reliability estimation method for engineering system based on machine learning and simulation method. Due to the stochastic nature of the environmental loads and internal incentive, the physics of failure for mechanical system is complex, and it is challenging to include uncertainties for the physical modeling of failure in the engineered system’s life cycle. In this paper, an efficient time-dependent reliability assessment framework for mechanical system is proposed using a machine learning algorithm considering stochastic dynamic loads in the mechanical system. Firstly, stochastic external loads of mechanical system are analyzed, and the finite element model is established. Secondly, the physics of failure mode of mechanical system at a time location is analyzed, and the distribution of time realization under each load condition is calculated. Then, the distribution of fatigue life can be obtained based on high-cycle fatigue theory. To reduce the calculation cost, a machine learning algorithm is utilized for physical modeling of failure by integrating uniform design and Gaussian process regression. The probabilistic fatigue life of gear transmission system under different load conditions can be calculated, and the time-varying reliability of mechanical system is further evaluated. Finally, numerical examples and the fatigue reliability estimation of gear transmission system is presented to demonstrate the effectiveness of the proposed method.

Reliability Integrated Engineering Using Physics of Failure

2008 ◽  
Author(s):  
Weiqiang Wang ◽  
Diganta Das ◽  
Michael Osterman ◽  
Michael Pecht
Keyword(s):  
Physics Of Failure

Comparative Reliability Prediction Using Physics of Failure Models

2011 ◽  
Vol 2011 (HITEN) ◽  
pp. 000189-000195
Author(s):  
Milton Watts ◽  
K. Rob Harker
Keyword(s):  
Circuit Complexity ◽  
High Impact ◽  
Reliability Prediction ◽  
Life Test ◽  
Physics Of Failure ◽  
Failure Models ◽  
The Individual ◽  
Aging Models ◽  
Device Test

Quartzdyne Electronics has invested millions of device test hours in life testing of circuits in both powered and un-powered tests. In addition to time at temperature, these tests include thermal cycling and high impact drop testing. Recent projects have required the use of larger packages and components as we have expanded the variety of circuits that we build. It is desirable to predict the effects of these changes on long-term reliability before investing in tooling. In this study we will compare a new design which contains these larger components to the simpler, smaller designs for which we have extensive life-test data. Using a physics-of-failure approach, component mounting stresses will be analyzed using finite element modeling. These results will be compared to pre and post-aging shear strengths of actual components of varying sizes. Aging models will then be developed to predict the reliability of the new design based on the comparative stress margins of the individual components coupled with circuit complexity. Once validated, the aging models will enable reliability prediction and trade-off analysis for future designs.

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