Storage Reliability Estimation Method using Non-Repetitively Sampled, Accelerated Degradation Data in Quadratic Form

2019 ◽  
Vol 43 (12) ◽  
pp. 867-874
Author(s):  
Young Kap Son
Keyword(s):  
Quadratic Form ◽  
Estimation Method ◽  
Reliability Estimation ◽  
Accelerated Degradation ◽  
Degradation Data

scholarly journals Improvement of Reliability Determination Performance of Real Time Kinematic Solutions Using Height Trajectory

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 657
Author(s):  
Aoki Takanose ◽  
Yoshiki Atsumi ◽  
Kanamu Takikawa ◽  
Junichi Meguro
Keyword(s):  
Real Time ◽  
Urban Areas ◽  
Estimation Method ◽  
Tall Buildings ◽  
Autonomous Driving ◽  
Reliability Estimation ◽  
Height Variation ◽  
Position Information ◽  
Real Time Kinematic ◽  
Evaluation Test

Autonomous driving support systems and self-driving cars require the determination of reliable vehicle positions with high accuracy. The real time kinematic (RTK) algorithm with global navigation satellite system (GNSS) is generally employed to obtain highly accurate position information. Because RTK can estimate the fix solution, which is a centimeter-level positioning solution, it is also used as an indicator of the position reliability. However, in urban areas, the degradation of the GNSS signal environment poses a challenge. Multipath noise caused by surrounding tall buildings degrades the positioning accuracy. This leads to large errors in the fix solution, which is used as a measure of reliability. We propose a novel position reliability estimation method by considering two factors; one is that GNSS errors are more likely to occur in the height than in the plane direction; the other is that the height variation of the actual vehicle travel path is small compared to the amount of movement in the horizontal directions. Based on these considerations, we proposed a method to detect a reliable fix solution by estimating the height variation during driving. To verify the effectiveness of the proposed method, an evaluation test was conducted in an urban area of Tokyo. According to the evaluation test, a reliability judgment rate of 99% was achieved in an urban environment, and a plane accuracy of less than 0.3 m in RMS was achieved. The results indicate that the accuracy of the proposed method is higher than that of the conventional fix solution, demonstratingits effectiveness.

scholarly journals Reliability Assessment of a Multi-State HVDC System by Combining Markov and Matrix-Based Methods

Energies ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3097
Author(s):  
Roberto Benato ◽  
Antonio Chiarelli ◽  
Sebastian Dambone Sessa
Keyword(s):  
Current System ◽  
Estimation Method ◽  
Reliability Estimation ◽  
Voltage Source ◽  
Voltage Source Converter ◽  
Hvdc System ◽  
Critical Elements ◽  
The Matrix ◽  
The Impact

The purpose of this paper is to highlight that, in order to assess the availability of different HVDC cable transmission systems, a more detailed characterization of the cable management significantly affects the availability estimation since the cable represents one of the most critical elements of such systems. The analyzed case study consists of a multi-terminal direct current system based on both line commutated converter and voltage source converter technologies in different configurations, whose availability is computed for different transmitted power capacities. For these analyses, the matrix-based reliability estimation method is exploited together with the Monte Carlo approach and the Markov state space one. This paper shows how reliability analysis requires a deep knowledge of the real installation conditions. The impact of these conditions on the reliability evaluation and the involved benefits are also presented.

scholarly journals Reliability estimation for drive axle of wheel loader under extreme small sample

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401983684 ◽  
Author(s):  
Leilei Cao ◽  
Lulu Cao ◽  
Lei Guo ◽  
Kui Liu ◽  
Xin Ding
Keyword(s):  
Sample Size ◽  
Promising Result ◽  
Small Sample Size ◽  
Bootstrap Method ◽  
Estimation Method ◽  
Small Sample ◽  
Reliability Estimation ◽  
Fatigue Reliability ◽  
Drive Axle ◽  
Semi Empirical

It is difficult to have enough samples to implement the full-scale life test on the loader drive axle due to high cost. But the extreme small sample size can hardly meet the statistical requirements of the traditional reliability analysis methods. In this work, the method of combining virtual sample expanding with Bootstrap is proposed to evaluate the fatigue reliability of the loader drive axle with extreme small sample. First, the sample size is expanded by virtual augmentation method to meet the requirement of Bootstrap method. Then, a modified Bootstrap method is used to evaluate the fatigue reliability of the expanded sample. Finally, the feasibility and reliability of the method are verified by comparing the results with the semi-empirical estimation method. Moreover, from the practical perspective, the promising result from this study indicates that the proposed method is more efficient than the semi-empirical method. The proposed method provides a new way for the reliability evaluation of costly and complex structures.

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.

scholarly journals The Weibull Birnbaum-Saunders Distribution And Its Applications

2020 ◽  
Vol 9 (1) ◽  
pp. 61-81
Author(s):  
Lazhar BENKHELIFA
Keyword(s):  
Maximum Likelihood ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Real Data ◽  
Reliability Estimation ◽  
Maximum Likelihood Estimates ◽  
Model Parameters ◽  
Data Sets ◽  
Proposed Model ◽  
Modeling Data

A new lifetime model, with four positive parameters, called the Weibull Birnbaum-Saunders distribution is proposed. The proposed model extends the Birnbaum-Saunders distribution and provides great flexibility in modeling data in practice. Some mathematical properties of the new distribution are obtained including expansions for the cumulative and density functions, moments, generating function, mean deviations, order statistics and reliability. Estimation of the model parameters is carried out by the maximum likelihood estimation method. A simulation study is presented to show the performance of the maximum likelihood estimates of the model parameters. The flexibility of the new model is examined by applying it to two real data sets.

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