Gearbox Fault Diagnosis Using Industrial Machine Learning Techniques

Abstract

This paper highlights the need for precise and reliable diagnostic methods for early fault detection in gearbox systems, something critical for industrial maintenance. Advances in machine learning (ML) and image processing have opened new avenues for diagnosis. This study explores ML techniques, particularly edge detection and maximized pooling, with the Inverse Distance Weighting method, for diagnosing gearbox faults from vibration signal images. Using the ODYSSEE-A Eye platform, a model was developed that achieved 96% accuracy in identifying faults from a 500-sample dataset. The research results promote further investigation and progress in this area, indicating specific possible directions for further research.

Keywords

• machinery diagnosis
• machine learning
• image processing
• gearbox noise