Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method

Research article (2016 8th International Conference on Modelling, Identification and Control (ICMIC), 2016) · cited 10× · AI/ML
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Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method

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Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method is a scholarly article[1].

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APA 4ort.xyz Knowledge Graph. (2026). Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method. Retrieved May 24, 2026, from https://4ort.xyz/entity/rolling-bearing-faults-diagnosis-based-on-empirical-mode-decomposition-optimized-threshold-de-noising-method
MLA “Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/rolling-bearing-faults-diagnosis-based-on-empirical-mode-decomposition-optimized-threshold-de-noising-method.
BibTeX @misc{4ortxyz_rolling-bearing-faults-diagnosis-based-on-empirical-mode-decomposition-optimized-threshold-de-noising-method_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Rolling bearing faults diagnosis based on empirical mode decomposition: Optimized threshold de-noising method}}, year = {2026}, url = {https://4ort.xyz/entity/rolling-bearing-faults-diagnosis-based-on-empirical-mode-decomposition-optimized-threshold-de-noising-method}, note = {Accessed: 2026-05-24}}
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