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<p>This study evaluates the potential of bio-based lubricants as sustainable alternatives to synthetic greases in vibrating mechanical systems. An intelligent monitoring framework was developed using the Negative Selection Algorithm (NSA), inspired by artificial immune systems, to classify vibration signals obtained from a pin-on-disk test rig. A commercial lithium-based grease was used as reference, while nine soybean-oil-derived bio-lubricants (LQG01–LQG09) were tested under controlled conditions. The NSA achieved accuracy above 97%, with several formulations (LQG04, LQG05, LQG07) reaching 100% similarity to the synthetic benchmark. These findings demonstrate that AI can provide a precise, non-destructive, and automated method for performance evaluation of lubricants. The novelty of this work lies in integrating bio-lubricant testing with immune-inspired algorithms, highlighting their technical feasibility and contribution to the ecological transition of the lubricant industry by reducing petroleum dependency and environmental impact.</p>

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Artificial intelligence and sustainability in industry: the role of bio-lubricants

  • 期刊论文

  • 发表日期：

    2025年12月15日

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  • Gustavo Soares,
  • Fábio Roberto Chavarette,
  • Henrique Antônio Mendonça Faria,
  • Fernanda Zanolli Freitas,
  • Aparecido Carlos Gonçalves,
  • Roberto Outa

摘要

This study evaluates the potential of bio-based lubricants as sustainable alternatives to synthetic greases in vibrating mechanical systems. An intelligent monitoring framework was developed using the Negative Selection Algorithm (NSA), inspired by artificial immune systems, to classify vibration signals obtained from a pin-on-disk test rig. A commercial lithium-based grease was used as reference, while nine soybean-oil-derived bio-lubricants (LQG01–LQG09) were tested under controlled conditions. The NSA achieved accuracy above 97%, with several formulations (LQG04, LQG05, LQG07) reaching 100% similarity to the synthetic benchmark. These findings demonstrate that AI can provide a precise, non-destructive, and automated method for performance evaluation of lubricants. The novelty of this work lies in integrating bio-lubricant testing with immune-inspired algorithms, highlighting their technical feasibility and contribution to the ecological transition of the lubricant industry by reducing petroleum dependency and environmental impact.