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Kappaganthu, K. and Nataraj, C. (2011) Mutual Information Based Feature Selection from Data Driven and Model Based Techniques for Fault Detection in Rolling Element Bearings. Proceedings of the ASME IDETC/CIE, Washington DC, 28-31 August 2011, 941-953.
https://doi.org/10.1115/DETC2011-47822

has been cited by the following article:

• TITLE: Gear Fault Detection Using Recurrence Quantification Analysis and Support Vector Machine

  AUTHORS: T. Haj Mohamad, Y. Chen, Z. Chaudhry, C. Nataraj

  KEYWORDS: Gear Diagnostics, Recurrence Analysis, Optimization, Time Series Analysis

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.11 No.5, May 25, 2018

  ABSTRACT: This paper presents the application of recurrence plots (RPs) and recurrence quantification analysis (RQA) in the diagnostics of various faults in a gear-train system. For this study, multiple test gears with different health conditions (such as a healthy gear, and defective gears with a root crack on one tooth, multiple cracks on five teeth and missing tooth) are studied. The vibration data of a gear-train is measured by a triaxial accelerometer installed on the test. Two different support vector machine classifiers are trained and compared. Mutual information is used to rank the extracted features in order to select an optimal subset that provides as much information as possible about the intrinsic dynamics of the system. Results indicate that our approach is quite efficient in diagnosing the status of the health of the gear system and characterizing the dynamic behavior.