Alternative method for the metrological characterization of spur gears in the sub-millimeter range using optical equipment

The aim of this work is to develop a software that allows the inspection of spur gear manufactured in the sub-millimeter range. The measurements are made using a digital optical machine and using an analysis proprietary software implemented in Matlab®, which is able to handle images, captured using the digital optical machine. The software allows to evaluate the profile and pitch deviations as establish in the ISO/TR 10064-1:1992 standard

Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks

This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffness reduction associated with local failures by applying a supervised learning mode and coupled with analytical models. To do this, a multi-layer perceptron neural network has been configured using as input features statistical parameters sensitive to torsional stiffness decrease and derived from wavelet transforms of the response signal. The proposed method is applied to the gear condition monitoring and results show that it can update the mesh dynamic properties of the gear on line.