Proposing a New Signal-Averaging Method Based on Correlation and Analysing its Performance on Five-Speed Car Gearbox

Using signal processing methods for fault detection of machinery is increasing nowadays. Noises added to the signal can negatively effect on efficiency of these methods. Time-domain averaging is a usual method to increase the strength of a signal. But, success of averaging is depends on an assumption which is corresponding points in averaging has same angle on the axle which is called synchronization. A little difference in synchronization can cause more decrease in efficiency of the method. Using tachometer is a usual way for synchronization. But, precision of tachometer isn’t enough. In this paper, as an attempt to solve this problem, a new method based on correlation is proposed for averaging which can synchronise data more precisely. Proposed method has been tested on real data gathered from a five-speed car gearbox. Using real-world data including a lot of components is an advantage of this research. Experimental results shows proposed method can reduce mean squared error from 0.419 to 0.103 in average which is a significant reduce.