Composite fault feature extraction for gears based on MCKD-EWT adaptive wavelet threshold noise reduction by Yanchang LV, Jingyue Wang, Chengqiang Zhang, Jianming Ding

“…The results of experimental data analysis show that compared with the feature extraction methods such as spatial scale threshold EWT-MCKD and Complete Ensemble Empirical Mode Decomposition (CEEMDAN)-MCKD, the proposed method is more suitable for the diagnosis of gear composite faults in a strong background noise environment, the noise interference is effectively suppressed, and the extraction effect of gear composite fault features is more obvious.…”
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Enhanced Short-Term PV Power Forecasting via a Hybrid Modified CEEMDAN-Jellyfish Search Optimized BiLSTM Model by Yanhui Liu, Jiulong Wang, Lingyun Song, Yicheng Liu, Liqun Shen

“…This study proposes a novel hybrid forecasting model that integrates complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), the jellyfish search (JS) optimization algorithm, and a bidirectional long short-term memory (BiLSTM) neural network. …”
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Cutting Feature Extraction Method for Ultra-High Molecular Weight Polyethylene Fiber-Reinforced Concrete Based on Feature Classification and Improved Hilbert–Huang Transform by Shanshan Hu, Jinzhao Feng, Hui Liu, Guoxin Tang, Geng’e Zhang, Fali Xiong, Shirun Zhong, Yilong Huang

“…In order to intelligently evaluate the performance of diamond saw blades when cutting UHMWPE-FRC, a feature extraction method, based on feature classification and an improved Hilbert–Huang transform (HHT), is proposed, which consider Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet threshold de-noising. …”
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