An integrated classifier for gear system monitoring

Abstract

A novel integrated classifier has been developed in this paper for real-time machinery health condition monitoring, specifically for gear systems. The diagnostic classification is performed by a neural fuzzy scheme; the diagnostic reliability is enhanced by integrating the (multi-step-ahead) future states of the dynamic system. An online hybrid training technique is adopted based on recursive Levenberg–Marquet and least-squares estimate (LSE) algorithms to improve the classifier convergence and adaptive capability to accommodate different machinery conditions. The viability of this new monitoring system is verified by experimental tests under different gear conditions. Test results show that the proposed integrated classifier provides a robust problem solving framework; it outperforms other related data-driven classification schemes, because of its efficient feature enhancement, formation integration, and system training. Furthermore, the integrated classifier has been implemented for condition monitoring in multistage printing machines; its effectiveness is verified by some primary application practices.