Hazra, BudhadityaPantula, ShilpaNarasimhan, Sriram2018-03-052018-03-052013http://hdl.handle.net/10012/13029A powerful continuous wavelet transform based signal processing tool named Synchro-squeezing transform (SST) has recently emerged in the context of non-stationary signal processing. Founded upon the premise of time-frequency (TF) reassignment, its basic objective is to provide a sharper representation of signals in the TF plane. Additionally, it can also extract the individual components of a non-stationary multi-component signal, which makes it attractive for rotating machinery signals. This work utilizes the decomposing power of SST transform to extract useful components from gear-motor signals in relevant sub-bands, followed by the application of standard rotating machinery condition indicators. For timely detection of faults in airport baggage conveyor gear-motors, a novelty detection technique based on the concept of self-organizing maps (SOM) is applied on the condition indicators. This approach promises improved anomaly detection performance than that can be achieved by applying condition indicators and SOM directly to the inherently complex raw-data. Data collected from conveyor gear-motors provides a test bed to demonstrate the efficacy of the proposed approach.enAttribution 3.0 United Stateshttps://creativecommons.org/licenses/by/3.0/us/rotating machineryData-driven methods for fault detectiondiagnosis, and prognosisConference Paper