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<p>Manufacturing environments increasingly demand cost-effective bearing fault detection solutions that balance diagnostic accuracy with implementation practicality. This study investigates optimal feature complexity levels for small-sample bearing condition monitoring using the standardized Paderborn University benchmark dataset. Through systematic evaluation of 15 bearing specimens across three complexity categories, we analyze the performance-cost trade-offs critical for manufacturing deployment. Results demonstrate that moderate-complexity features achieve superior discriminative power, with spectral centroid reaching Cohen’s <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\varvec{d} \varvec{=} \varvec{2.06}\)</EquationSource> </InlineEquation> compared to fractal dimension’s <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(\varvec{d} \varvec{=} \varvec{0.40}\)</EquationSource> </InlineEquation>. Machine learning methods significantly outperform traditional threshold approaches, with Random Forest achieving 80% accuracy versus 40% for RMS-based detection under small-sample constraints typical in manufacturing. Statistical validation through leave-one-out cross-validation reveals that moderate complexity provides optimal performance-per-implementation-cost ratios. The study establishes practical guidelines for manufacturing engineers, demonstrating that strategic feature selection outweighs algorithmic sophistication for industrial bearing diagnostics under realistic data constraints.</p>

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Optimal feature complexity for small-sample bearing fault detection in manufacturing

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Paulo R. L. Almeida,
  • Thyago L. V. Lima,
  • Alisson V. Brito,
  • Abel C. Lima Filho

摘要

Manufacturing environments increasingly demand cost-effective bearing fault detection solutions that balance diagnostic accuracy with implementation practicality. This study investigates optimal feature complexity levels for small-sample bearing condition monitoring using the standardized Paderborn University benchmark dataset. Through systematic evaluation of 15 bearing specimens across three complexity categories, we analyze the performance-cost trade-offs critical for manufacturing deployment. Results demonstrate that moderate-complexity features achieve superior discriminative power, with spectral centroid reaching Cohen’s \(\varvec{d} \varvec{=} \varvec{2.06}\) compared to fractal dimension’s \(\varvec{d} \varvec{=} \varvec{0.40}\) . Machine learning methods significantly outperform traditional threshold approaches, with Random Forest achieving 80% accuracy versus 40% for RMS-based detection under small-sample constraints typical in manufacturing. Statistical validation through leave-one-out cross-validation reveals that moderate complexity provides optimal performance-per-implementation-cost ratios. The study establishes practical guidelines for manufacturing engineers, demonstrating that strategic feature selection outweighs algorithmic sophistication for industrial bearing diagnostics under realistic data constraints.