Fault Detection and Diagnosis of Gear Wear Based on Teager-Huang Transform
ABSTRACT A new approach to fault diagnosis of gear wear based on Teager-Huang transform is presented. This method is based on Empirical Mode Decomposition (EMD) and Teager Kaiser Energy Operator (TKEO) technique. EMD can adaptively decompose the vibration signal into a series of zero mean Amplitude Modulation-Frequency Modulation (AM-FM)Intrinsic Mode Functions (IMFs). TKEO can track the instantaneous amplitude and instantaneous frequency of the AM-FM component at any instant. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the Teager-Huang transform approach is better to that of the Hilbert-Huang transform approach for gear fault detection. Teager-Huang transform can effectively diagnose the faults of the gear wear.
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ABSTRACT: Amplitude-Modulation Frequency-Modulation (AM-FM) decompositions represent images using spatially-varying sinusoidal waves and their spatially-varying amplitudes. The model uses different scales and bandpass filters to characterize the wide range of frequencies that may be present in an image. In the past few years, as the understanding of its the-ory advanced, AM-FM has been used in a series of medical imaging problems ranging from ultrasound to retinal image analysis, yielding excellent results. This paper summarizes the theory of AM-FM and some of its main medical imaging applications: carotid artery ultrasound, pneumoconiosis, diabetic retinopathy, and age-related macular degeneration.