2023
DOI: 10.1016/j.ress.2023.109182
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Aero-engine prognosis strategy based on multi-scale feature fusion and multi-task parallel learning

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Cited by 20 publications
(5 citation statements)
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References 33 publications
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“…MFSMTP is a multi-task learning network that can extract features from data using three different sizes of convolutional kernels to capture more feature information [ 32 ]. The parameters of MFSMTP were as follows: {Input (1, 75, 14), convolution1 (5, 1, 2), convolution2 (3, 1, 1), convolution3 (1, 1, 0), linear (192, Class)}.…”
Section: Resultsmentioning
confidence: 99%
See 3 more Smart Citations
“…MFSMTP is a multi-task learning network that can extract features from data using three different sizes of convolutional kernels to capture more feature information [ 32 ]. The parameters of MFSMTP were as follows: {Input (1, 75, 14), convolution1 (5, 1, 2), convolution2 (3, 1, 1), convolution3 (1, 1, 0), linear (192, Class)}.…”
Section: Resultsmentioning
confidence: 99%
“…The multi-feature fusion strategy is a solution for capturing the comprehensive features of samples across different scales [ 31 , 32 , 33 ]. Three types of multi-feature fusion algorithms are commonly used nowadays.…”
Section: Introductionmentioning
confidence: 99%
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“…Liu et al 11 employed the channel attention mechanism (CAM) to assign larger weights to important features. Zhou et al 12 proposed a comprehensive framework based on multiscale feature fusion and parallel learning to predict RUL. Zhang et al 13 designed a dual-aspect selfattention based on a transformer (DAST) model for aero-engine RUL estimation, which also obtains promising performance.…”
Section: Introductionmentioning
confidence: 99%