A Tooth-Wise Dimensionality Reduction Approach Based on Encoder Signal for the Diagnosis of Gearbox

As the planetary gearboxes are widely used in the industrial applications, a novel method is presented to detect the anomaly of planetary gearbox. In this work, the data from a rotary encoder are analyzed to get the fault information. The proposed approach mainly divided into three steps. Considering that the adopted values are angular displacements, the tooth-wise samples are obtained from the measured signal firstly. Then all the samples are mapped into a high-dimension space by kernel PCA (PCA) to find the most discriminative dimensions. Finally, the similarity measurement is taken among the tooth-wise samples to reveal the anomaly. The validity of proposed method is demonstrated both simulated signal and experimental data.