Gear fault diagnosis based on SVM and multi-sensor information fusion

To solve the problems that the vibration signals from a gearbox are usually noisy and it is difficult to find a potential failure in a gearbox by a single sensor,using support vector machine(SVM) as a tool for feature-level information fusion,eight gear vibration signals for fault diagnosis were investigated.The results show that the method for gear fault diagnosis based on multi-sensors information fusion has higher reliability and accuracy than that based on a single sensor.The crest factor is the most sensitive character for gear failure and the diagnostic accuracy rate reaches 93.33% by using the character to perform multi-sensor information fusion.