| Autor: |
J C Leng, Y T Gao, Z M Hu |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
Insight - Non-Destructive Testing and Condition Monitoring. 63:488-495 |
| ISSN: |
1354-2575 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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