Study on Storage Reliability Assessment and Prediction Method of Electronic Equipment

Author(s):  
Yuanhao Shi ◽  
Fei Kan ◽  
Kai Liu ◽  
Zeping Chen
2018 ◽  
Vol 138 ◽  
pp. 83-93 ◽  
Author(s):  
Hongquan Qu ◽  
Shuo Fu ◽  
Liping Pang ◽  
Chen Ding ◽  
Helin Zhang

Author(s):  
Hiroaki Takegami ◽  
Atsuhiko Terada ◽  
Kaoru Onuki ◽  
Ryutaro Hino

The Japan Atomic Energy Agency has been conducting R&D on thermochemical water-splitting Iodine-Sulfur (IS) process for hydrogen production to meet massive demand in the future hydrogen economy. A concept of sulfuric acid decomposer was developed featuring a heat exchanger block made of SiC. Recent activity has focused on the reliability assessment of SiC block. Although knowing the strength of SiC block is important for the reliability assessment, it is difficult to evaluate a large-scale ceramics structure without destructive test. In this study, a novel approach for strength estimation of SiC structure was proposed. Since accurate strength estimation of individual ceramics structure is difficult, a prediction method of minimum strength in the structure of the same design was proposed based on effective volume theory and optimized Weibull modulus. Optimum value of the Weibull modulus was determined for estimating the lowest strength. The strength estimation line was developed by using the determined modulus. The validity of the line was verified by destructive test of SiC block model, which is small-scale model of the SiC block. The fracture strength of small-scale model satisfied the predicted strength.


2014 ◽  
Vol 687-691 ◽  
pp. 978-983
Author(s):  
Yan Ping Tian ◽  
Xiao Hui Ye ◽  
Ming Yin

In order to solve the problem of complicated electronic equipment structure, inadequate fault information, hard to predict the fault and the existing failure prediction method cannot predict the state of the electronic equipment and other issues directly, we propose a combination failure prediction methods of least squares support vector machine (LSSVM) and hidden Markov model (HMM) based on Condition Based Maintenance (CBM). First, according to sensitivity analysis to determine the circuit elements to be changed to set the circuit by changing the parameters of the different components degraded state; secondly, create a combination failure prediction model; Finally, the circuit state prediction. The results show that the proposed method can directly predict the different states of the circuit, so as to realize the fault state prediction of the electronic equipment directly, the prediction accuracy can reach 93.3%.


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