  Gear drives are one of the most widely used transmission system in many
machinery. Sound signals of a rotating machine contain the dynamic information
about its health conditions. Not much information available in the literature
reporting suitability of sound signals for fault diagnosis applications.
Maximum numbers of literature are based on FFT (Fast Fourier Transform)
analysis and have its own limitations with non-stationary signals like the ones
from gears. In this paper, attempt has been made in using sound signals
acquired from gears in good and simulated faulty conditions for the purpose of
fault diagnosis through a machine learning approach. The descriptive
statistical features were extracted from the acquired sound signals and the
predominant features were selected using J48 decision tree technique. The
selected features were then used for classification using Large Margin
K-nearest neighbor approach. The paper also discusses the effect of various
parameters on classification accuracy.
