Scholarlink

<p>Reciprocating equipment, particularly piston engines, demonstrates inherently complex dynamic behavior due to cyclic variations in pressure, volume, and fluid properties occurring within each crankshaft revolution. Unlike steady-state rotating equipment such as turbines, compressors, and pumps, these transient conditions introduce unique challenges for accurate fault detection and diagnosis. The complexity of these systems requires the development of robust and sensitive fault indicators capable of detecting subtle deviations from normal operational patterns, which is critical to maintaining system safety, reliability, and optimal performance. This paper presents a comprehensive review of contemporary fault detection methodologies tailored for reciprocating machinery, categorizing approaches into knowledge-based, model-based, data-driven, and hybrid frameworks. Emphasis is placed on the analysis and application of pressure–volume (P–V) diagrams as an effective diagnostic tool for capturing real-time performance anomalies. The review critically evaluates the advantages and limitations of existing techniques, discussing challenges such as nonlinear dynamic behavior, noise interference, limited data availability, and the feasibility of real-time implementation. Additionally, this work identifies emerging trends and research gaps, proposing future directions aimed at enhancing fault diagnosis accuracy, robustness, and integration within advanced condition monitoring systems. The insights provided aim to guide researchers and practitioners toward more reliable and efficient maintenance strategies for reciprocating equipment in various industrial applications.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Comprehensive review on fault diagnostics and isolation methods for piston engines regarding monitoring of reciprocating equipment

  • 期刊论文

  • 发表日期：

    2025年9月16日

查看全文
  • Mobin Omidali,
  • Mostafa Khazaee,
  • Mehdi Jahromi,
  • Arash Mohammadi Dizgoini

摘要

Reciprocating equipment, particularly piston engines, demonstrates inherently complex dynamic behavior due to cyclic variations in pressure, volume, and fluid properties occurring within each crankshaft revolution. Unlike steady-state rotating equipment such as turbines, compressors, and pumps, these transient conditions introduce unique challenges for accurate fault detection and diagnosis. The complexity of these systems requires the development of robust and sensitive fault indicators capable of detecting subtle deviations from normal operational patterns, which is critical to maintaining system safety, reliability, and optimal performance. This paper presents a comprehensive review of contemporary fault detection methodologies tailored for reciprocating machinery, categorizing approaches into knowledge-based, model-based, data-driven, and hybrid frameworks. Emphasis is placed on the analysis and application of pressure–volume (P–V) diagrams as an effective diagnostic tool for capturing real-time performance anomalies. The review critically evaluates the advantages and limitations of existing techniques, discussing challenges such as nonlinear dynamic behavior, noise interference, limited data availability, and the feasibility of real-time implementation. Additionally, this work identifies emerging trends and research gaps, proposing future directions aimed at enhancing fault diagnosis accuracy, robustness, and integration within advanced condition monitoring systems. The insights provided aim to guide researchers and practitioners toward more reliable and efficient maintenance strategies for reciprocating equipment in various industrial applications.