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Fault Diagnosis of Helical Gear Box using Variational Mode Decomposition and Random Forest Algorithm

Akhil Muralidharan1,2, V. Sugumaran1, K.P Soman3, M. Amarnath4
SMBS, VIT University, Chennai Campus, Vandalur-Kelambakkam Road, Chennai, India.
Corresponding author.
Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore, Tamil Nadu, India.
Department of Mechanical Engineering, Indian Institute of Information Technology Design and Manufacturing, Jabalpur, Madhya Pradesh, India.

Structural Durability & Health Monitoring 2014, 10(1), 55-80. https://doi.org/10.3970/sdhm.2014.010.055

Abstract

Gears are machine elements that transmit motion by means of successively engaging teeth. In purely scientific terms, gears are used to transmit motion. A faulty gear is a matter of serious concern as it affects the functionality of a machine to a great extent. Thus it is essential to diagnose the faults at an initial stage so as to reduce the losses that might be incurred. This necessitates the need for continuous monitoring of the gears. The vibrations produced by gears from good and simulated faulty conditions can be effectively used to detect the faults in these gears. The introduction of Variational Mode Decomposition (VMD) as a new signal pre-processing technique along with the different decision trees have provided good classification performance. VMD allows decomposition of the signal into various modes by identifying a compact frequency support around its central frequency, such that adding all the modes reconstructs the original signal. Alternating direction multiplier method (ADMM) is used by VMD to find the intrinsic mode functions on central frequencies. Meaningful statistical features can be extracted from VMD processed signals. J48 decision tree algorithm was used to identify the useful features and the selected features were used for classification using the decision trees namely, Random Forest, REP Tree and Logistic Model Tree algorithms. The performance analyses of various algorithms are discussed in detail.

Keywords

Gear fault diagnosis, Variational Mode Decomposition, Alternating Direction Multiplier Method, J48 tree algorithm, Random Forest algorithm, Logistic Model Tree, REP Tree

Cite This Article

Muralidharan, A., Sugumaran, V., Soman, K., Amarnath, M. (2014). Fault Diagnosis of Helical Gear Box using Variational Mode Decomposition and Random Forest Algorithm. Structural Durability & Health Monitoring, 10(1), 55–80.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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