Track: Artificial Intelligence

Abstract

There are growing demands for Machinery health monitoring and fault diagnosis of rotating machinery to lower unscheduled breakdown. Gearboxes are one of the fundamental components of rotating machinery; their faults identification and classification always draw a lot of attention. However, non-stationary vibration signals and low energy of weak faults makes this task challenging in many cases. Thus, a new fault diagnosis method which combines the Empirical mode decomposition (EMD), time frequency method, and self-organizing feature extraction machine learning approach is proposed in this paper. In particular, efficient feature extraction and feature selection is a key issue to automatic condition monitoring and fault diagnosis processes. To focus on such issues, this paper presents a research study to formulate an real-time prediction method using vibration signals of various gears conditions that uses empirical mode decomposition (EMD) technique to extract features and select the predominant features by using various classification algorithms.