A New Reliability Prediction Method Based on Physics of Failure Method for Product Design and Manufacture

2012 ◽  
Vol 548 ◽  
pp. 521-526 ◽  
Author(s):  
Xing Hao Wang ◽  
Jiang Shao ◽  
Xiao Yu Liu
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Calculation ◽  
Computational Cost ◽  
Prediction Method ◽  
New Method ◽  
Reliability Prediction ◽  
Time To Failure ◽  
Physics Of Failure ◽  
First Order ◽  
Environment Stress

Different from the reliability prediction method on handbook, the reliability prediction method based on Physics of Failure (PoF) model takes failure mechanism as theoretical basis, and combines the design in-formation with the environment stress of the product to predict the time to failure. When the uncertain of the parameters is considered to predict the reliability, Monte-Carlo calculation method is always used here. How-ever, the Monte-Carlo method needs large computational cost, especially for large and complicated electronic systems. A new reliability prediction method which combines the first order reliability with the reliability pre-diction method based on PoF model was proposed. The new method utilized the first order method to calculate the position of design point and reliability index, thus Monte-Carlo calculation process was avoided. Example calculation results showed that the new method improves the prediction efficiency without decreasing the accuracy of reliability, thus it is feasible for reliability prediction of electronic product in engineering.

An Equilibrium Prediction Method for Control and Fault Detection of Energy Systems

2021 ◽  
Author(s):  
Austin Rogers ◽  
Fangzhou Guo ◽  
Bryan Rasmussen
Keyword(s):  
Monte Carlo ◽  
Fault Detection ◽  
Monte Carlo Simulations ◽  
Case Studies ◽  
Air Conditioning ◽  
Prediction Method ◽  
Worker Safety ◽  
Static System ◽  
Control Logic ◽  
First Order

Abstract Many fault detection, optimization, and control logic methods rely on sensor feedback that assumes the system is operating at steady state conditions, despite persistent transient disturbances. While filtering and signal processing techniques can eliminate some transient effects, this paper proposes an equilibrium prediction method for first order dynamic systems using an exponential regression. This method is particularly valuable for many commercial and industrial energy system, whose dynamics are dominated by first order thermo-fluid effects. To illustrate the basic advantages of the proposed approach, Monte Carlo simulations are used. This is followed by three distinct experimental case studies to demonstrate the practical efficacy of the proposed method. First, the ability to predict the carbon dioxide level in classrooms allows for energy efficient control of the ventilation system and ensures occupant comfort. Second, predicting the optimal time to end the cool-down of an industrial sintering furnace allows for maximum part throughput and worker safety. Finally, fault detection and diagnosis methods for air conditioning systems typically use static system models; however, the transient response of many air conditioning signals may be approximated as first order, and therefore, the prediction model enables the use of static fault detection methods with transient data. In this paper, the equilibrium prediction method's performance will be quantified using both Monte Carlo simulations and case studies.

Bayesian Variational Seismic Tomography using Normalizing Flows

2021 ◽  
Author(s):  
Xuebin Zhao ◽  
Andrew Curtis ◽  
Xin Zhang
Keyword(s):  
Monte Carlo ◽  
Seismic Tomography ◽  
Computational Cost ◽  
Large Data ◽  
Optimization Methods ◽  
Monte Carlo Sampling ◽  
New Method ◽  
Posterior Probability Distribution ◽  
Computationally Expensive ◽  
A Chain

<p>Seismic travel time tomography is used widely to image the Earth's interior structure and to infer subsurface properties. Tomography is an inverse problem, and computationally expensive nonlinear inverse methods are often deployed in order to understand uncertainties in the tomographic results. Monte Carlo sampling methods estimate the posterior probability distribution which describes the solution to Bayesian tomographic problems, but they are computationally expensive and often intractable for high dimensional model spaces and large data sets due to the curse of dimensionality. We therefore introduce a new method of variational inference to solve Bayesian seismic tomography problems using optimization methods, while still providing fully nonlinear, probabilistic results. The new method, known as normalizing flows, warps a simple and known distribution (for example a Uniform or Gaussian distribution) into an optimal approximation to the posterior distribution through a chain of invertible transforms. These transforms are selected from a library of suitable functions, some of which invoke neural networks internally. We test the method using both synthetic and field data. The results show that normalizing flows can produce similar mean and uncertainty maps to those obtained from both Monte Carlo and another variational method (Stein varational gradient descent), at significantly decreased computational cost. In our tomographic tests, normalizing flows improves both accuracy and efficiency, producing maps of UK surface wave speeds and their uncertainties at the finest resolution and the lowest computational cost to-date, allowing results to be interrogated efficiently and quantitatively for subsurface structure.</p>

Vibration Fatigue Analysis of the Solder Connector

2011 ◽  
Vol 105-107 ◽  
pp. 294-298
Author(s):  
Xiao Xi Guo ◽  
Chuan Ri Li ◽  
Long Tao Liu
Keyword(s):  
Life Prediction ◽  
Random Vibration ◽  
Prediction Method ◽  
Failure Model ◽  
Ansys Software ◽  
Physical Test ◽  
Physics Of Failure ◽  
Model And Simulation ◽  
Vibration Fatigue ◽  
Environment Stress

With the increasing problems of fatigue failure of PCB in aeronautic and astronautic electronic chassis, the paper proposes a life prediction method of the solder connector based on physics of failure by simulation and physical test. The virtual modal and random vibration response of the box should be solved by finite element method in the ANSYS software, and these results have been verified by modal test and random vibration test. While the structure parameter and the environment stress of the box, the results of the simulation as the input of the vibration fatigue physics of failure model, the life of solder connector could be computed. There is a comparison between results from the CalcePWA software and it from physics of failure model and simulation.

New Method Based on Monte Carlo Calculation and Voxelized Phantoms for Realistic Internal Dosimetry: Application to a Complex and Old Actinide Contamination

2009 ◽  
Vol 168 (3) ◽  
pp. 824-831 ◽  
Author(s):  
D. Broggio ◽  
J. Janeczko ◽  
S. Lamart ◽  
E. Blanchardon ◽  
N. Borisov ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Calculation ◽  
New Method ◽  
Internal Dosimetry

FAILURE MODELING OF CONSTANT FRACTION DISCRIMINATOR USING PHYSICS OF FAILURE APPROACH

2013 ◽  
Vol 20 (03) ◽  
pp. 1340002 ◽  
Author(s):  
ADITHYA THADURI ◽  
A. K. VERMA ◽  
V. GOPIKA ◽  
RAJESH GOPINATH ◽  
UDAY KUMAR
Keyword(s):  
Failure Mechanisms ◽  
Reliability Prediction ◽  
Time To Failure ◽  
Point Location ◽  
Physics Of Failure ◽  
Failure Modeling ◽  
Constant Fraction Discriminator ◽  
Custom Made ◽  
Failure Point ◽  
Critical Components

Due to several advancements in the technology trends in electronics, the reliability prediction by the constant failure methods and standards no longer provide accurate time to failure. The physics of failure methodology provides a detailed insight on the operation, failure point location and causes of failure for old, existing and newly developed components with consideration of failure mechanisms. Since safety is a major criteria for the nuclear industries, the failure modeling of advanced custom made critical components that exists on signal conditioning module are need to be studied with higher confidence. One of the components, constant fraction discriminator, is the critical part at which the failure phenomenon and modeling by regression is studied in this paper using physics of failure methodology.

scholarly journals A combined universal generating function and physics of failure Reliability Prediction Method for an LED driver

2021 ◽  
Vol 23 (1) ◽  
pp. 74-83
Author(s):  
Liming Fan ◽  
Kunsheng Wang ◽  
Dongming Fan
Keyword(s):  
Generating Function ◽  
System Reliability ◽  
Light Emitting Diode ◽  
Prediction Method ◽  
Reliability Prediction ◽  
Led Driver ◽  
Physics Of Failure ◽  
Circuit Topology ◽  
Led Drivers ◽  
Universal Generating Function

The accurate and effective reliability prediction of light emitting diode (LED) drivers has emerged as a key issue in LED applications. However, previous studies have mainly focused on the reliability of electrolytic capacitors or other single components while ignoring circuit topology. In this study, universal generating function (UGF) and physics of failure (PoF) are integrated to predict the reliability of LED drivers. Utilizing PoF, lifetime data for each component are obtained. A system reliability model with multi-phase is established, and system reliability can be predicted using UGF. Illustrated by a two-channel LED driver, the beneficial effects of capacitors and MOSFETs for the reliability of LED drivers is verified. This study (i) provides a universal numerical approach to predict the lifetime of LED drivers considering circuit topology, (ii) enhances the modelling and reliability evaluation of circuits, and (iii) bridges the gap between component and circuit system levels.

Monte Carlo calculation of the energy parameters and spatial distribution of the cathodic arc ions while passing through the macro-particles filters

2018 ◽  
Vol 1 (1) ◽  
pp. 30-34 ◽  
Author(s):  
Alexey Chernogor ◽  
Igor Blinkov ◽  
Alexey Volkhonskiy
Keyword(s):  
Magnetic Field ◽  
Mass Transfer ◽  
Monte Carlo ◽  
Monte Carlo Calculation ◽  
Inhomogeneous Magnetic Field ◽  
Flow Energy ◽  
Wide Range ◽  
Field Concentrator ◽  
Cathodic Arc ◽  
The Magnetic Field

The flow, energy distribution and concentrations profiles of Ti ions in cathodic arc are studied by test particle Monte Carlo simulations with considering the mass transfer through the macro-particles filters with inhomogeneous magnetic field. The loss of ions due to their deposition on filter walls was calculated as a function of electric current and number of turns in the coil. The magnetic field concentrator that arises in the bending region of the filters leads to increase the loss of the ions component of cathodic arc. The ions loss up to 80 % of their energy resulted by the paired elastic collisions which correspond to the experimental results. The ion fluxes arriving at the surface of the substrates during planetary rotating of them opposite the evaporators mounted to each other at an angle of 120° characterized by the wide range of mutual overlapping.

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