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Title: liguge (Chao He) · GitHub

Open Graph Title: liguge - Overview

X Title: liguge - Overview

Description: Welcome to send me e-mails to communicate! Research interests include transfer learning in intelligent fault diagnosis and so forth. - liguge

Open Graph Description: Welcome to send me e-mails to communicate! Research interests include transfer learning in intelligent fault diagnosis and so forth. - liguge

X Description: Welcome to send me e-mails to communicate! Research interests include transfer learning in intelligent fault diagnosis and so forth. - liguge

Opengraph URL: https://github.com/liguge

X: @github

direct link

Domain: patch-diff.githubusercontent.com

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Links:

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Repositories 180 https://patch-diff.githubusercontent.com/liguge?tab=repositories
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https://liguge.github.io/https://liguge.github.io/
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ligugehttps://patch-diff.githubusercontent.com/liguge/liguge
https://patch-diff.githubusercontent.com/liguge#️-academic-homepage
Google scholarhttps://scholar.google.com/citations?user=1DtpMlAAAAAJ&hl=en
ResearchGatehttps://www.researchgate.net/profile/Chao-He-64
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WOShttps://www.webofscience.com/wos/author/record/HCJ-0483-2022
Scopushttps://www.scopus.com/authid/detail.uri?authorId=57222401905
https://patch-diff.githubusercontent.com/liguge#-my-published-paper
Physics-informed Interpretable Wavelet Weight Initialization and Balanced Dynamic Adaptive Threshold for Intelligent Fault Diagnosis of Rolling Bearingshttps://doi.org/10.1016/j.jmsy.2023.08.014
`https://example.com/link_to_your_paper
https://github.com/liguge/EWSNet
https://camo.githubusercontent.com/5950988a09e6ce6bc0e2f2a6656b1ae1634b97bb186524cbfc4f4c0edacf9e9c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4557534e65742e737667
https://doi.org/10.1016/j.jmsy.2023.08.014
IDSN: A one-stage Interpretable and Differentiable STFT domain adaptation Network for traction motor of high-speed trains cross-machine diagnosishttps://doi.org/10.1016/j.ymssp.2023.110846
https://example.com/link_to_your_paper
https://github.com/liguge/IDSN_public
https://camo.githubusercontent.com/045846f9c51e5e11c9ad71126529a57029566a0a14663ae3b7b8e1177789c67d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4944534e5f7075626c69632e737667
https://doi.org/10.1016/j.ymssp.2023.110846
Interpretable Physics-informed Domain Adaptation Paradigm for Cross-machine Transfer Diagnosishttps://doi.org/10.1016/j.knosys.2024.111499
https://example.com/link_to_your_paper
https://github.com/liguge/WIDAN
https://camo.githubusercontent.com/0fdf147b2b6c64edbe5b54d6a8c96ea700587b1fe4558516f98b4040b67735ee/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f574944414e2e737667
https://doi.org/10.1016/j.knosys.2024.111499
Interpretable modulated differentiable STFT and physics-informed balanced spectrum metric for freight train wheelset bearing cross-machine transfer fault diagnosis under speed fluctuationshttps://doi.org/10.1016/j.aei.2024.102568
https://example.com/link_to_your_paper
https://github.com/liguge/PyDSN
https://camo.githubusercontent.com/4e26628f9e3e7e13fcf2860d2b7db30a799a7e837712f5ca03013a237e4daa64/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f507944534e2e737667
https://doi.org/10.1016/j.aei.2024.102568
https://doi.org/10.48550/arXiv.2406.11917
Prior knowledge-embedded first-layer interpretable paradigm for rail transit vehicle human-computer collaboration fault monitoringhttps://doi.org/10.1016/j.jii.2026.101068
https://example.com/link_to_your_paper
https://github.com/liguge/PIFCapsule
https://camo.githubusercontent.com/7ec745c63c9e4378e95ba7959e219757303db99f7a179518d21f5f9f648922d6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f50494643617073756c652e737667
https://doi.org/10.1016/j.jii.2026.101068
Journals of Prognostics and Health Management（智能故障诊断和寿命预测期刊）https://github.com/liguge/Journals-for-Prognostics-and-Health-Management
https://camo.githubusercontent.com/d75a816c12fcad7f9dfc35b8c5427464fb251197cc5c6e391c2815fc5bd0f0bb/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4a6f75726e616c732d666f722d50726f676e6f73746963732d616e642d4865616c74682d4d616e6167656d656e742e737667
Awesome large language model for Prognostics and health management（用于预测性维护与健康管理的大型语言模型）https://github.com/liguge/Large-language-model-for-Prognostics-and-health-management
https://camo.githubusercontent.com/351e2aab5937ed72f9903b5aeb5e781a3ea0efeacb0368a0cae261c307ac15ff/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f417765736f6d652d6c617267652d6c616e67756167652d6d6f64656c2d666f722d50726f676e6f73746963732d616e642d6865616c74682d6d616e6167656d656e742e737667
Awesome Multi-modal datasets for fault diagnosis（多模态故障诊断数据集）https://github.com/liguge/Awesome-multi-modal-datasets-for-fault-diagnosis
https://camo.githubusercontent.com/bc7ee55f8d0681e707ab93203097d741f06788a5333e9eba69aeac4e1f126e4d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f417765736f6d652d6d756c74692d6d6f64616c2d64617461736574732d666f722d6661756c742d646961676e6f7369732e737667
Fault diagnosis for small samples based on attention mechanismhttps://doi.org/10.1016/j.measurement.2021.110242
https://example.com/link_to_your_paper
https://github.com/liguge/Fault-diagnosis-for-small-samples-based-on-attention-mechanism
https://camo.githubusercontent.com/45a9429388fc8983f523dca16f7adf74d640ebe76bfd5e4fa59daadf4d88664c/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4661756c742d646961676e6f7369732d666f722d736d616c6c2d73616d706c65732d62617365642d6f6e2d617474656e74696f6e2d6d656368616e69736d2e737667
https://doi.org/10.1016/j.measurement.2021.110242
基于Laplace小波卷积和BiGRU的少量样本故障诊断方法https://kns.cnki.net/kcms2/article/abstract?v=YoFA4grnCX4IptBzWglGXApO5AGPdZ156g6v-p2jrPHNmVObS4A1qgfoVQ5DLQmA0tZPfcGejDUBj3Pn0PsDC_CaIm9TKNDyXl3emEIY_kWSGFi3qKnIFL9hMXTytEQaZrlUvmHXxiA=&uniplatform=NZKPT&flag=copy
https://github.com/liguge/DLWCB
https://camo.githubusercontent.com/fd796ef3c10d96a0f5676f857c08ac794527d269e687c71c4ddfb7594922a4c0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f444c5743422e737667
https://kns.cnki.net/kcms2/article/abstract?v=YoFA4grnCX4IptBzWglGXApO5AGPdZ156g6v-p2jrPHNmVObS4A1qgfoVQ5DLQmA0tZPfcGejDUBj3Pn0PsDC_CaIm9TKNDyXl3emEIY_kWSGFi3qKnIFL9hMXTytEQaZrlUvmHXxiA=&uniplatform=NZKPT&flag=copy
Rolling Bearing Sub-Health Recognition via Extreme Learning Machine Based on Deep Belief Network Optimized by Improved Fireworkshttps://doi.org/10.1109/ACCESS.2021.3064962
https://example.com/link_to_your_paper
https://github.com/liguge/FWA-DBN-ELM-for-intelligent-fault-diagnosis
https://camo.githubusercontent.com/6d49e1be09f51a10fcb78bf3263bfcd0f68feea849b9df33f0e1f4a20df19e2a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4657412d44424e2d454c4d2d666f722d696e74656c6c6967656e742d6661756c742d646961676e6f7369732e737667
https://doi.org/10.1109/ACCESS.2021.3064962
https://patch-diff.githubusercontent.com/liguge#-published-papers-that-i-try-to-reproduceunofficial-code
https://doi.org/10.1109/TII.2019.2943898
https://github.com/liguge/Deep-Residual-Shrinkage-Networks-for-intelligent-fault-diagnosis-DRSN-
https://camo.githubusercontent.com/71957e5c3bff1fd07e46bc3cca709de59dbda4048a736ee33e7c824f15d4d5e6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f446565702d526573696475616c2d536872696e6b6167652d4e6574776f726b732d666f722d696e74656c6c6967656e742d6661756c742d646961676e6f7369732d4452534e2d2e737667
https://doi.org/10.1109/ICME52920.2022.9859658
https://github.com/liguge/MDPS_pytorch
https://camo.githubusercontent.com/3a70982f2c779d29d055d9ee0a8d40f0c855fdc096363dd6d34c2dd5c9410e88/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4d4450535f7079746f7263682e737667
https://doi.org/10.1016/j.ymssp.2022.109884
https://github.com/liguge/DDTLN
https://camo.githubusercontent.com/833ec9b94b31cc5b045b25b8f78042e161530a11db4a3247c0c5d0e8f79b9d28/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4444544c4e2e737667
https://doi.org/10.1016/j.engappai.2022.105794
https://github.com/liguge/GTFENet_pytorch
https://camo.githubusercontent.com/42df0c782274e4fa12a5a4ef87cf2241080c4e50f35f06b965a03c56324e04e3/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f475446454e65745f7079746f7263682e737667
https://doi.org/10.1109/JSEN.2022.3169341
https://github.com/liguge/Capsule-network-for-fault-diagnosis
https://camo.githubusercontent.com/09e80483cda1e73d87ca3c5c3de00f5b79915f3587b81ff53bc5b03d0858467d/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f43617073756c652d6e6574776f726b2d666f722d6661756c742d646961676e6f7369732e737667
https://github.com/liguge/chanzhen
https://doi.org/10.1109/TII.2022.3224979
https://github.com/liguge/MIXCNN_pytorch
https://camo.githubusercontent.com/437e49e093c70b9be3ccf18871220e827ff54dcfb984bec83f7d7dc6bc8039a0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4d4958434e4e5f7079746f7263682e737667
https://doi.org/10.1016/j.aei.2023.101877
https://github.com/liguge/PGCNN
https://camo.githubusercontent.com/83ef23019e45cea07537ad75ef0cf92543929094297bb995260c87a4d3c758b0/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f5047434e4e2e737667
https://doi.org/10.1016/j.knosys.2023.110748
https://github.com/liguge/MMSD
https://camo.githubusercontent.com/a541f4adb768d85663f1df195459cdb30057af4ebae4cd58378590cd49b46b7a/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4d4d53442e737667
https://doi.org/10.1088/1361-6501/ac3fc7
https://github.com/liguge/MAND
https://camo.githubusercontent.com/22abae5cedb5ac763b5fab8ef282af277f1c82bf384ab30109213dec7e9ea7e6/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f4d414e442e737667
https://github.com/liguge/xLSTM-for-intelligent-fault-diagnosis-of-rolling-bearings
https://camo.githubusercontent.com/861989afb01a2970b97c84b3fdb0a13c2b801634e46e4998cc66762a8dc5be76/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f73746172732f6c69677567652f784c53544d2d666f722d696e74656c6c6967656e742d6661756c742d646961676e6f7369732d6f662d726f6c6c696e672d62656172696e67732e737667
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