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Diagnosing faults in rotating machinery during operation is essential for maintaining production efficiency. However, gearbox vibration signals under variable speeds are highly complex, and advanced signal analysis methods often require significant computational resources. Thus, this paper introduces a novel method for analyzing large-scale variable speed signals to diagnose rolling bearing faults effectively. The method employs downsampling in both time and frequency domains, greatly reducing computational demands while preserving diagnostic precision. The proposed approach is computationally efficient and suitable for low-configuration machinery, enabling broader industrial applications while minimizing resource use.

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An Enhanced Technique for Rolling Bearing Fault Diagnosis in Variable Rotating Speeds with Large-Scale Data

  • 会议论文

  • 发表日期：

    2026年

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  • Trong-Du Nguyen,
  • Thi-Thanh-Nga Nguyen,
  • Nhan-Phuc Hoang,
  • Thanh-Trung Pham,
  • Phuc-Tan Le,
  • Phong-Dien Nguyen

摘要

Diagnosing faults in rotating machinery during operation is essential for maintaining production efficiency. However, gearbox vibration signals under variable speeds are highly complex, and advanced signal analysis methods often require significant computational resources. Thus, this paper introduces a novel method for analyzing large-scale variable speed signals to diagnose rolling bearing faults effectively. The method employs downsampling in both time and frequency domains, greatly reducing computational demands while preserving diagnostic precision. The proposed approach is computationally efficient and suitable for low-configuration machinery, enabling broader industrial applications while minimizing resource use.