*Gear mechanisms are one of the most significant components of the power transmission systems. Due to increasing emphasis on the high-speed, longer working life, high torques, etc. cracks may be observed on the gear surface. It is a distinctive topic to gear fault diagnosis at an early stage where the damage begins. 60% of the gear faults cause total gearbox failures. The 59% of the wind turbine faults and 19.1% of the damage in helicopter powertrain are address gears. Recently, Machine Learning (ML) algorithms have started to be used frequently in fault diagnosis with developing technology. In the literature, it can be observed that the data collected through vibration, acoustic, and torque sensors are subjected to feature extraction by various methods, and given as an input to ML algorithms. The aim of this study is to the determination of the gear root crack and its degree with vibration-based diagnostics approach using ML algorithms.*

*To performed early crack detection, the single tooth stiffness and the mesh stiffness calculated via ANSYS for both healthy and faulty (50-75%) teeth. The calculated data transferred to the 6-DOF dynamic model of a one-stage gearbox, the dynamic transmission errors (DTE), and vibration responses were collected. The defined procedure repeated at different operating speeds (450-1800 rpm) to examine the effect of changing working conditions and its effect on accuracy. The obtained signals used for the feature extraction with various statistical indicators (mean square root, kurtosis, standard deviation, etc.) and some numerical methods (discrete wavelet transform, empirical mode decomposition, singular value decomposition, etc.) which have an effect on the fault diagnosis accuracy rate depending on the classification algorithm. The data gathered for healthy and faulty cases were evaluated for the feature extraction with six statistical indicators. Besides, white Gaussian noise was added to the data obtained from the 6-DOF model, and it was aimed at early fault diagnosis and condition monitoring with ML algorithms.*

*In this study, the gear root crack and its degree analyzed for both healthy and two different crack sizes (50-75%) to determine the best performing ML algorithm. Thereby, a method was presented for early fault diagnosis without the need for a big experimental dataset. The proposed vibration-based ML approach can eliminate the high test rig construction costs and can potentially be used for the evaluation of different working conditions and gear design parameters. Therefore, catastrophic failures can be prevented, and maintenance costs can be optimized by early crack detection.*

Keywords: Gears, Machine Learning, Fault Diagnosis, Asymmetric Gears


Vibration-Based Early Crack Diagnosis With Machine Learning for Spur Gears

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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