Fault Diagnosis of Helical Gear Box Using Decision Tree and Best-First Tree

Gear is one of critical transmission elements, found its wide applications in small, medium and large machineries. It is well proved that vibration signals acquired from a rotating machine comprise of the dynamic information about the health condition of the rotating machine. This paper uses vibration signals acquired from machinery comprising of gears in good and simulated faulty conditions for the purpose of fault diagnosis by machine learning approach. In this paper, a vibration based on machine learning approach is presented for helical gear box as it plays critical role in the industries. This approach has mainly three steps namely feature extraction, feature selection and classification. Here statistical analysis was used for feature extraction and decision tree and best-first decision tree for classification was taken and compared. The classification accuracies for different conditions were calculated and compared to find the best classifier for the fault diagnosis of the helical gear box.