Fault diagnosis of mechanical equipment based on data visualization

Abstract

The decision process of the traditional mechanical equipment fault diagnosis method is invisible; it is similar to the black box operation and difficult to find the hidden knowledge in the data. Aiming at this problem, a fault diagnosis method of mechanical equipment is proposed based on data visualization. Firstly the data is flattened based on the constellation, and taking into account the different contribution that each data plays, the weight of each feature data is optimized by genetic algorithm, and then the fault diagnosis model based on data visualization is constructed by using the boundary form of the plane point set. Finally the results of the experiments on gearbox test experiment reveal that the proposed method is superior to the K-Neighborhood method and accurate for the fault diagnosis.