Interpretation and compensation of backlash error data in machine centers for intelligent predictive maintenance using ANNs

Abstract

It is especially significant for a manufacturing company to select a proper maintenance policy because maintenance impacts not only on economy, reliability and availability but also on personnel safety. This article reports on research in the backlash error data interpretation and compensation for intelligent predictive maintenance in machine centers based on artificial neural networks (ANNs). The backlash error, measurement system and prediction methods are analyzed in detail. The result indicates that it is possible to predict and compensate for the backlash error in both forward and backward directions in machine centers.