Gear crack level identification based on weighted K nearest neighbor classification algorithm

A crack fault is one of the damage modes most frequently occurring in gears. Identifying different crack levels, especially for early cracks is a challenge in gear fault diagnosis. This paper aims to propose a method to classify the different levels of gear cracks automatically and reliably. In this method, feature parameters in time domain, specially designed for gear damage detection and in frequency domain are extracted to characterize the gear conditions. A two-stage feature selection and weighting technique (TFSWT) via Euclidean distance evaluation technique (EDET) is presented and adopted to select sensitive features and remove fault-unrelated features. A weighted K nearest neighbor (WKNN) classification algorithm is utilized to identify the gear crack levels. The gear crack experiments were conducted and the vibration signals were captured from the gears under different loads and motor speeds. The proposed method is applied to identifying the gear crack levels and the applied results demonstrate its effectiveness.