Abstract
As one of the most common gear failure modes, tooth wear can produce nonlinear modulation sidebands in the vibration frequency spectrum. However, limited research has been reported in monitoring the gear wear based on vibration due to the lack of tools which can effectively extract the small sidebands. In order to accurately monitor gear wear progression in a timely fashion, this paper presents a gear wear condition monitoring approach based on vibration signal analysis using the modulation signal bispectrum-based sideband estimator (MSB-SE) method. The vibration signals are collected using a run-to-failure test of gearbox under an accelerated test process. MSB analysis was performed on the vibration signals to extract the sideband information. Using a combination of the peak value of MSB-SE and the coherence of MSB-SE, the overall information of gear transmission system can be obtained. Based on the amplitude of MSB-SE peaks, a dimensionless indicator is proposed to assess the effects of gear tooth wear. The results demonstrated that the proposed indicator can be used to accurately and reliably monitor gear tooth wear and evaluate the wear severity.
Highlights
Gears are critical mechanical components for power transmission systems and are widely used in automobiles, helicopters, and industrial power trains
To examine the performance and extend the applications of modulation signal bispectrum-based sideband estimator (MSB-SE)-based vibration analysis technique to the monitoring of gearboxes, this paper investigates monitoring the deterioration process of an industrial multi-stage helical gearbox based on vibration measurements and MSB analysis
The MSB-SEc of all shaft frequencies and their harmonics at the mesh frequency couplings is that the gear wear‐induced vibration has two main paths to go from its source to the of 2fm2 (773 Hz) and 3fm2 (1160 Hz) for all load cases are relatively obvious
Summary
Gears are critical mechanical components for power transmission systems and are widely used in automobiles, helicopters, and industrial power trains. The amplitudes of these small sidebands are useful for both detection and diagnosis As these sidebands are usually influenced by random noise, in order to obtain more accurate results, many signal processing methods have been tried in many of the latest studies. Inspired by the works presented in [17,19], where MSB was successfully used for identifying both the presence and magnitude of the seeded faults, MSB method would be used to evaluate the natural gear wear monitoring and severity assessment— it may be more challenging, as the wear process may induce vibration signature changes far weaker than tooth breakages.
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