Journal of Drive and Control (드라이브 ㆍ 컨트롤)

The Korean Society for Fluid Power and Construction Equipment (유공압건설기계학회)
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Reliable Fault Diagnosis Method Based on An Optimized Deep Belief Network for Gearbox

  • Oybek Eraliev (Department of Control & Mechanical Engineering, the Graduate School, Hankook University) ;
  • Ozodbek Xakimov (Department of Mechanical Engineering, Jeju National University) ;
  • Chul-Hee Lee (Department of Control & Mechanical Engineering, the Graduate School, Hankook University)
  • Received : 2023.10.18
  • Accepted : 2023.11.16
  • Published : 2023.12.01
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Abstract

High and intermittent loading cycles induce fatigue damage to transmission components, resulting in premature gearbox failure. To identify gearbox defects, numerous vibration-based diagnostics techniques, using several artificial intelligence (AI) algorithms, have recently been presented. In this paper, an optimized deep belief network (DBN) model for gearbox problem diagnosis was designed based on time-frequency visual pattern identification. To optimize the hyperparameters of the model, a particle swarm optimization (PSO) approach was integrated into the DBN. The proposed model was tested on two gearbox datasets: a wind turbine gearbox and an experimental gearbox. The optimized DBN model demonstrated strong and robust performance in classification accuracy. In addition, the accuracy of the generated datasets was compared using traditional ML and DL algorithms. Furthermore, the proposed model was evaluated on different partitions of the dataset. The results showed that, even with a small amount of sample data, the optimized DBN model achieved high accuracy in diagnosis.

Keywords

Acknowledgement

This research was supported by a grant(2023-MOIS35-005) of Policy-linked Technology Development Program on Natural Disaster Prevention and Mitigation funded by Ministry of Interior and Safety (MOIS, Korea).

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