scholarly journals Performance Degradation Modeling and Reliability Analysis of Aviation Connectors in Marine Environment

2021 ◽  
Vol 1043 (3) ◽  
pp. 032029
Author(s):  
Ge Wang ◽  
Jian-Qi Zhou ◽  
Yong-Tao Zhao ◽  
Jing Feng ◽  
Quan Sun
Structures ◽  
2021 ◽  
Vol 34 ◽  
pp. 3247-3256
Author(s):  
Debiao Meng ◽  
Zhiyuan Lv ◽  
Shiyuan Yang ◽  
Hongtao Wang ◽  
Tianwen Xie ◽  
...  

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Chunbo Yang ◽  
Shengkui Zeng ◽  
Jianbin Guo

TheK-out-of-Nconfiguration is a typical form of redundancy techniques to improve system reliability, where at leastK-out-of-Ncomponents must work for successful operation of system. When the components are degraded, more components are needed to meet the system requirement, which means that the value ofKhas to increase. The current reliability analysis methods overestimate the reliability, because using constantKignores the degradation effect. In a load-sharing system with degrading components, the workload shared on each surviving component will increase after a random component failure, resulting in higher failure rate and increased performance degradation rate. This paper proposes a method combining a tampered failure rate model with a performance degradation model to analyze the reliability of load-sharingK-out-of-Nsystem with degrading components. The proposed method considers the value ofKas a variable which is derived by the performance degradation model. Also, the load-sharing effect is evaluated by the tampered failure rate model. Monte-Carlo simulation procedure is used to estimate the discrete probability distribution ofK. The case of a solar panel is studied in this paper, and the result shows that the reliability considering component degradation is less than that ignoring component degradation.


Mechanika ◽  
2018 ◽  
Vol 24 (2) ◽  
Author(s):  
Zhi-Qiang LI ◽  
Ting-Xue XU ◽  
Jun-Yuan GU ◽  
Lin-Yu FU ◽  
Jian-Zhong ZHAO

2019 ◽  
Vol 2019 ◽  
pp. 1-10 ◽  
Author(s):  
Jingbo Gai ◽  
Yifan Hu ◽  
Junxian Shen

Bearing performance degradation assessment has great significance to condition-based maintenance (CBM). A novel degradation modeling method based on EMD-SVD and fuzzy neural network (FNN) was proposed to identify and evaluate the degradation process of bearings in the whole life cycle accurately. Firstly, the vibration signals of bearings in known states were decomposed by empirical mode decomposition (EMD) to obtain the intrinsic mode functions (IMFs) containing feature information. Then, the selected key IMFs which contain the main features were decomposed by singular value decomposition (SVD). And the decomposed results were used as the training samples of FNN. At last, the output results of the tested data were normalized to the health index (HI) through learning and training of FNN, and then the performance degradation degree could be described by the distance between the test sample and the normal one. According to the case study, this modeling method could evaluate the performance degradation of bearings effectively and identify the early fault features accurately. This method also provided an important maintenance strategy for the CBM of bearings.


IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 169047-169054 ◽  
Author(s):  
Rong Yuan ◽  
Mao Tang ◽  
Hui Wang ◽  
Haiqing Li

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