可靠性(半导体)
融合
再制造
区间(图论)
人工神经网络
传感器融合
模糊逻辑
模式识别(心理学)
计算机科学
算法
数学
数据挖掘
人工智能
工程类
物理
组合数学
哲学
机械工程
量子力学
功率(物理)
语言学
作者
Yiwen Gao,Zhongyao Hu,Jiao-jiao Leng
出处
期刊:Insight
[British Institute of Non-Destructive Testing]
日期:2021-08-01
卷期号:63 (8): 488-495
被引量:1
标识
DOI:10.1784/insi.2021.63.8.488
摘要
Prediction of the remaining life of remanufacturing blanks is crucial to evaluate their remanufacturability. To overcome the deficiency of obtaining insufficient fatigue damage characteristic information using a single non-destructive testing method, a new magnetoacoustic fusion life prediction method based on Dempster-Shafer (D-S) evidence theory optimised by a weighted fusion algorithm is proposed. The characteristic parameters of metal magnetic memory (MMM) and acoustic emission (AE) signals are first extracted on the basis of a fatigue experiment and data layer fusion is carried out to establish the mapping relationship between MMM and AE characteristic parameters and specimen life based on a back-propagation (BP) neural network. The basic probability distribution of the life is assigned in a fuzzy manner according to the normal distribution and the reliability function of each life interval is obtained by data fusion based on D-S evidence theory. Furthermore, the basic probability distribution value is modified based on a weighted fusion algorithm and the corrected data are fused to obtain a more accurate life prediction result.
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