Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis

When faults occur in the gear, energy distribution of gear vibration signals measured in time–frequency plane would be different from the distribution under the normal state. Therefore, it is possible to detect a fault by comparing the energy distribution of gear vibration signals with and without fault conditions. Hilbert–Huang transform can offer a complete and accurate energy–frequency–time distribution. On the other hand, Shannon entropy could give a useful criterion for analyzing and comparing probability distribution and offer a measure of the information of any distribution. Targeting the feature of energy distribution of gear vibration signal, the merit of entropy and Hilbert–Huang transform, the concept of time–frequency entropy based on Hilbert–Huang transform is defined and furthermore gear fault diagnosis method based on time–frequency entropy is proposed. The analysis results from simulated signals and experimental signals with normal and defective gears show that the diagnosis approach proposed could identify gear status-with or without fault accurately and effectively. However, further study is needed to the classify gear fault pattern such as crack fault or broken teeth.