Data dynamic networks coupled with mechanical vibration energy for bearing defect identification

Abstract. Traditional machine defect identification methods have problems such as poor adaptability and low accuracy. As for solving the problems, a large number of improved machine defect identification techniques based on artificial intelligence have been developed; however, all of these methods focus on the neural networks and ignore the connections and influences of the data nodes. This paper proposes a feature-enhanced method based on a data dynamic network for bearing defect identifications. The research preprocesses the data by means of optimization algorithms and defines nodes and edges of data for the construction of a data dynamic network. In order to further improve the accuracy of the machine bearing defect identification, the data are also washed for the energy analysis to be coupled with the data dynamic network for feature enhancement. The training sets and test sets are defined with different data coupling techniques. A performance evaluation of the proposed method is carried out by means of the evaluation function for more effective detection of bearing defects.