Gear Fault Feature Extraction Based on Fuzzy Function and Improved Hu Invariant Moments

To intelligently identify gear fault types on the basis of gear vibration signals, due to the non-linear and non-stationary characteristics of gear vibration signals, a fuzzy function is used to represent the gear vibration signals in different states as two-dimensional time-frequency images. To solve the problem that the recognition effect of discontinuity area in the image by traditional Hu invariant moments is not ideal, improved Hu invariant moments are proposed, and the feature parameters of time-frequency images of gear vibration signals are extracted based on the improved Hu invariant moments. Different intelligent classifiers are used to recognize the gear vibration signals in different states. The recognition accuracy is higher by improved Hu invariant moments than by traditional Hu invariant moments, which shows that the method of gear fault feature extraction based on a fuzzy function and improved Hu invariant moments is quite ideal, and can be used in intelligent diagnosis of gear faults.