Data fusion of acceleration and velocity features (dFAVF) approach for fault diagnosis in rotating machines

Dynamics Laboratory, School of Mechanical, Aerospace and Civil Engineering (MACE), The University of Manchester M13 9PL United Kingdom

^* Corresponding author: kenisuomo.luwei@manchester.ac.uk

Abstract

Earlier research studies have suggested the unified vibration-based approach for fault diagnosis (FD) in identical machines with different foundation flexibilities and multi-rotating speeds. Intially the acceleration-based features were used for this approach then further work optimised the approach by combining acceleration and velocity features from vibration data for analysis. However the optimised approach was only tested on the identical machines rotating at different speeds below the machine’s first critical speed. The current paper tends to observe the optimised approach when applied to a test rig operating below and above the machine’s first critical speed.