Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Using Sparse Augmented Lagrangian Algorithm
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Publisher
IEEE
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http://xplorestaging.ieee.org/ielx7/9121924/9123991/09124433.pdf?arnumber=9124433
Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. A Nonlinear AutoRegressive-Based Noise Cancellation Method for Real-Time Fault Diagnosis of Rolling Bearings;IEEE Transactions on Instrumentation and Measurement;2024
2. IoT based Multi-Environmental Sensing System: Monitoring of Rotor Fault in Induction Motors;2023 IEEE 14th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED);2023-08-28
3. Nonintrusive wind blade fault detection using a deep learning approach by exploring acoustic information;The Journal of the Acoustical Society of America;2023-01-01
4. Acoustic Emission Analysis for Wind Turbine Blade Bearing Fault Detection Under Time-Varying Low-Speed and Heavy Blade Load Conditions;IEEE Transactions on Industry Applications;2021-05
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