Integrated Approach of Gearbox Fault Diagnosis

TL;DR

This paper proposes an integrated gearbox fault diagnosis method using a novel nonparametric preprocessing technique and machine learning, enabling effective online condition monitoring in industrial systems.

Contribution

It introduces CEEO, a nonparametric data preprocessing method, combined with feature extraction and MCSVM classification for improved gearbox fault diagnosis.

Findings

CEEO preserves characteristic frequencies in noisy signals.

The method achieves high accuracy in fault classification.

It is suitable for real-time industrial applications.

Abstract

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.