Research on Method about Determination of Condition-Based Maintenance Objects by Quantitative Analysis

2014 ◽  
Vol 635-637 ◽  
pp. 2023-2028
Author(s):  
Xiang Zan ◽  
Shi Xin Zhang ◽  
Yi Zheng ◽  
Yan Chao Liu
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Regression Analysis ◽  
Quantitative Analysis ◽  
Essential Role ◽  
Condition Based Maintenance ◽  
Traditional Methods

As an essential role in determination of condition-based maintenance (CBM) objects ,necessity and applicability analysis are both important. Necessity analysis is first and applicability analysis is second is proposed. Due to shortcoming of traditional methods, a quantitative is proposed. The key of method are criticality evaluation based on Monte Carlo simulation and applicability analysis based on regression analysis, which can solve the problem short of unite standard and influence by subjective factors. The result shows the model works well.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

scholarly journals Determination of electron backscattering coefficient of beryllium by a high-precision Monte Carlo simulation

2021 ◽  
Vol 26 ◽  
pp. 100862
Author(s):  
Abrar Hussain ◽  
Lihao Yang ◽  
Shifeng Mao ◽  
Bo Da ◽  
Károly Tőkési ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
High Precision ◽  
Backscattering Coefficient ◽  
Electron Backscattering

Determination of a general economic location constant by monte carlo simulation

1974 ◽  
Vol 1 (1) ◽  
pp. 119-124
Author(s):  
T.L. Honeycutt ◽  
C.E. Grad ◽  
J.D. Wilson
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo

Determination of the Residual Entropy of Simple Mixtures by Monte Carlo Simulation

Langmuir ◽  
2017 ◽  
Vol 33 (42) ◽  
pp. 11603-11610 ◽  
Author(s):  
Eric Detmar ◽  
Simin Yazdi Nezhad ◽  
Ulrich K. Deiters
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Residual Entropy

Assessing Wear Evolutions in Railway Wheelsets Using a Survival Modeling Approach

2021 ◽  
Author(s):  
Guilerme A. C. Caldeira ◽  
JoaquimAP Braga ◽  
António R. Andrade
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Life Cycle ◽  
Condition Based Maintenance ◽  
Monitoring Systems ◽  
Simulation Procedure ◽  
Condition Monitoring Systems ◽  
Survival Modeling ◽  
Wheel Profile ◽  
Independence Of Events

Abstract The present paper provides a method to predict maintenance needs for the railway wheelsets by modeling the wear out affecting the wheelsets during its life cycle using survival analysis. Wear variations of wheel profiles are discretized and modelled through a censored survival approach, which is appropriate for modeling wheel profile degradation using real operation data from the condition monitoring systems that currently exist in railway companies. Several parametric distributions for the wear variations are modeled and the behavior of the selected ones is analyzed and compared with wear trajectories computed by a Monte Carlo simulation procedure. This procedure aims to test the independence of events by adding small fractions of wear to reach larger wear values. The results show that the independence of wear events is not true for all the established events, but it is confirmed for small wear values. Overall, the proposed framework is developed in such a way that the outputs can be used to support predictions in condition-based maintenance models and to optimize the maintenance of wheelsets.

scholarly journals Application of Monte Carlo Methods to Consider Probabilistic Effects in a Race Simulation for Circuit Motorsport

2020 ◽  
Vol 10 (12) ◽  
pp. 4229 ◽  
Author(s):  
Alexander Heilmeier ◽  
Michael Graf ◽  
Johannes Betz ◽  
Markus Lienkamp
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Monte Carlo Method ◽  
State Of The Art ◽  
Probability Distributions ◽  
Strategic Decisions ◽  
The Monte Carlo Method ◽  
Random Events ◽  
Example Variability

Applying an optimal race strategy is a decisive factor in achieving the best possible result in a motorsport race. This mainly implies timing the pit stops perfectly and choosing the optimal tire compounds. Strategy engineers use race simulations to assess the effects of different strategic decisions (e.g., early vs. late pit stop) on the race result before and during a race. However, in reality, races rarely run as planned and are often decided by random events, for example, accidents that cause safety car phases. Besides, the course of a race is affected by many smaller probabilistic influences, for example, variability in the lap times. Consequently, these events and influences should be modeled within the race simulation if real races are to be simulated, and a robust race strategy is to be determined. Therefore, this paper presents how state of the art and new approaches can be combined to modeling the most important probabilistic influences on motorsport races—accidents and failures, full course yellow and safety car phases, the drivers’ starting performance, and variability in lap times and pit stop durations. The modeling is done using customized probability distributions as well as a novel “ghost” car approach, which allows the realistic consideration of the effect of safety cars within the race simulation. The interaction of all influences is evaluated based on the Monte Carlo method. The results demonstrate the validity of the models and show how Monte Carlo simulation enables assessing the robustness of race strategies. Knowing the robustness improves the basis for a reasonable determination of race strategies by strategy engineers.

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