ArticleSensors (Basel, Switzerland)2024
An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction.
Article in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Low-Speed Fault Diagnosis of Machinery Based on Improved Spectral Amplitude Modulation with Advanced Denoising Methods.Sensors (Basel, Switzerland) · 2026Article
- Deep learning model using squeezenet and promoted ideal gas molecular motion for music genre classification from audio spectrograms.Scientific reports · 2025Article
- Coupling Fault Diagnosis Based on Dynamic Vertex Interpretable Graph Neural Network.Sensors (Basel, Switzerland) · 2024Article
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10 authors.
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Abstract
To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum-maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis.
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