Gearbox Fault Detection using Synchro-squeezing Transform
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Date
2016
Authors
Hazra, Budhaditya
Narasimhan, Sriram
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
This paper presents a novel fault-detection method for gearbox vibration signatures using synchro-squeezing transform (SST). Premised upon the concept of time-frequency (TF) reassignment, SST provides a sharp representation of signals in TF plane compared to many popular TF methods. Additionally, it can also extract the individual components, called intrinsic mode functions or IMFs, of a non-stationary multi-component signal, akin to empirical mode decomposition. The rich mathematical structure based on continuous wavelet transform makes SST a promising candidate for gearbox diagnosis. This work utilizes the decomposing power of SST to extract the IMFs from gearbox signals. For robust detection of faults in gear-motors, a fault detection technique based on time-varying autoregressive coefficients of IMFs as features is utilized. Sequential Karhunen-Loeve transform is employed on the condition indicators to select the appropriate window sizes on which SST can be applied. Laboratory experimental data obtained from drivetrain diagnostics simulator provides test bed to demonstrate the robustness of the proposed algorithm.
Description
The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.proeng.2016.05.023 © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
Synchro-squeezing transform (SST), Sequential Karhunen-Loeve transform (SKLT), time-varying auto-regressive model (TVAR)