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Journal of Polymer & Composites Original Research Special issue

Application-Driven Rule-Based Framework for Lubrication Failure Modes in Industrial Systems

  1. Shaikh Azharuddin Kutubuddin Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur
  2. D.V. A. Rama Sastry Department of Mechanical Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur

Abstract

Modern lubricants increasingly rely on polymer-based composites, integrating synthetic base oils, polymer thickeners and solid additives like MoS₂ and PTFE for high-performance applications. These formulations not only enhance thermal and mechanical stability but also enable low-friction operation across diverse industrial conditions. Lubrication-related failures represent a critical cause of unplanned downtime and reduced reliability in industrial machinery. This paper presents an application-driven, rule-based framework designed to assess and mitigate lubrication failure modes in semi-solid and solid lubricants by linking operational parameters to lubricant properties and failure knowledge. The system integrates a curated database of solid and semi-solid lubricants, storing their key properties and knowledge base extracted from applications and case-studies related to industries such as mining equipment, steel mill rollers, etc. The framework maps known failure modes and recommended preventive strategies. By accepting input parameters such as operating temperature and mechanical load for application under consideration, the system evaluates the risk of failure using deterministic rules and property thresholds. It generates warnings for adverse conditions (e.g., thermal degradation or overloading) and recommends appropriate lubricants and maintenance actions. Unlike black-box predictive models, this approach emphasizes explainability and simplicity, offering a transparent decision-support tool that maintenance engineers can use with minimal technical overhead. Validated across multiple industrial scenarios, the framework demonstrates its effectiveness in identifying high-risk conditions, supporting condition-based decisions and reducing lubricant-related failures through proactive, rule-based insights.

Keywords

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