Improved Hilbert-Huang transform with soft sifting stopping criterion and its application to fault diagnosis of wheelset bearings.

Vibration signals from rotating machineries are usually of multi-component and modulated signals. Hilbert-Huang transform (HHT), hereby referring to the combination of empirical mode decomposition (EMD) and normalized Hilbert transform (NHT), is an effective method to extract useful information from the multi-component and modulated signals. However, sifting stopping criterion (SSC) that is crucial to the HHT performance has not been well explored for this sift-driven method in the past decades.

Planetary Gearbox Dynamic Modeling Considering Bearing Clearance and Sun Gear Tooth Crack.

Planetary gearbox systems are critical mechanical components in heavy machinery such as wind turbines. They may suffer from various failure modes, due to the harsh working environment. Dynamic modeling is a useful method to support early fault detection for enhancing reliability and reducing maintenance costs.

WITHDRAWN: Bearing fault diagnosis using sparse Daubechies-wavelet impulse isolation and horizontal-vertical synchrosqueezing transform approach.

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ACCUGRAM: A novel approach based on classification to frequency band selection for rotating machinery fault diagnosis.

Frequency band selection (FBS) in rotating machinery fault diagnosis aims to recognize frequency band location including a fault transient out of a full band spectrum, and thus fault diagnosis can suppress noise influence from other frequency components. Impulsiveness and cyclostationarity have been recently recognized as two distinctive signatures of a transient. Thus, many studies have focused on developing quantification metrics of the two signatures and using them as indicators to guide FBS.