Fault diagnosis of mining machine gearboxes based on continuous wavelet transform and machine learning

In the context of the accelerating process of digitalization and automation of coal mines, the role of gearboxes in the mining industry is undeniable. The stability of their operational condition is directly related to the safety and efficiency of coal production. However, considering their widespread industrial use, gearboxes are more prone to failures over long periods of operation than electric motors. If such faults are not detected and corrected in time, they can lead to damage to mining equipment, thereby having a detrimental effect on the overall coal extraction process. Therefore, the ability to predict gearbox faults in real time is of great practical importance. The application of various diagnostic methods for mechanical systems, including vibration signal analysis, is increasingly used and developed in industrial environments. This paper explores the potential of the Continuous Wavelet Transform (CWT) as a method for detecting faults in gearboxes, which are critical components of the electromechanical systems of mining machines.

Authors: Yu. N. Kozhubaev, V. N. Khokhlovsky, M. P. Korolev, R. V. Ershov, S. E. Sarajishvili

Direction: Electrical Engineering

Keywords: wavelet transform, fault diagnosis, gearbox

---

View full article