Mathematical modelling of rolling element bearings fault for the diagnosis in the gearbox-induction machine

• Main Authors: Azeddine Ratni, Djamel Benazouz
• Format: Article
• Language: English
• Published: JVE International 2020-03-01
• Series: Mathematical Models in Engineering
• Subjects: bearing fault detection; induction machine; gearbox; mcsa; spectral kurtosis
• Online Access: https://www.jvejournals.com/article/21206

Inner race fault in bearing suspension is relatively the common fault in induction motors coupled with a gearbox, their detection is feasible by vibration monitoring of characteristic bearing frequencies. However, vibration signals have numerous drawbacks like signal background noise due to external excitation motion, sensitivity due to the installation position and their invasive measurement nature. For this reason, it is necessary to apply an extremely efficient method known as stator current signal analysis which offers significant savings and implementation advantages over traditional vibration monitoring. This paper represents a mathematical model for electromechanical systems and for rolling-element bearing faults to study the influence of mechanical defects on electrical variables (stator current). The novelty in this work involves three contributions: modelling of rolling bearing faults by external forces applied on the electromechanical system; Physical representation of rolling bearing fault allowing the modeling of the studied system functionality and, the influence of mechanical fault (inner race) in the electrical variables (stator current). Simulation results at the end of this paper demonstrate the effectiveness of the proposed mathematical model to detect gearbox’ bearing fault based on the electrical stator current signal with high sensitivity using fast Kurtogram approach.