  Gearbox fault diagnosis is one of the most important parts in any industrial
systems. Failure of components inside gearbox can lead to a catastrophic
failure, uneven breakdown, and financial losses in industrial organization. In
that case intelligent maintenance of the gearbox comes into context. This paper
presents an integrated gearbox fault diagnosis approach which can easily deploy
in online condition monitoring. This work introduces a nonparametric data
preprocessing technique i.e., calculus enhanced energy operator (CEEO) to
preserve the characteristics frequencies in the noisy and inferred vibrational
signal. A set of time domain and spectral domain features are calculated from
the raw and CEEO vibration signal and inputted to the multiclass support vector
machine (MCSVM) to diagnose the faults on the system. An effective comparison
between raw signal and CEEO signal are presented to show the impact of CEEO in
gearbox fault diagnosis. The obtained results of this work look very promising
and can be implemented in any type of industrial system due to its
nonparametric nature.
