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<p>Failure causality modeling plays a critical role in mechanical system diagnostics. Current methods primarily rely on empirical knowledge and historical datasets, resulting in constrained applicability and inconsistent predictive accuracy. To address these limitations, this study proposes a novel mechanism-driven causality modeling framework that systematically integrates causal ordering theory with Ishikawa diagram analysis. The proposed approach first establishes parametric causal relationships between failure modes and root causes through physics-based causal ordering. It then converts these physical parameters into practical failure events via Ishikawa diagram representation. A practical engineering case study demonstrates that the proposed approach can reduce reliance on experience and data. Moreover, it helps to narrow down the scope of potential failure causes, which can improve the completeness and accuracy of the results. The proposed method, which is adaptable to situations lacking data and experience, can be implemented in both the design and maintenance stages. It offers valuable guidance to designers in enhancing machines’ reliability and assists maintainers in precisely pinpointing and tracing the root cause of failures.</p>

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A mechanism-driven failure causality modeling approach for mechanical systems combining causal ordering theory and Ishikawa diagram

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月23日

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  • Hui Yu,
  • Wenhao Zhang,
  • Wei Qu

摘要

Failure causality modeling plays a critical role in mechanical system diagnostics. Current methods primarily rely on empirical knowledge and historical datasets, resulting in constrained applicability and inconsistent predictive accuracy. To address these limitations, this study proposes a novel mechanism-driven causality modeling framework that systematically integrates causal ordering theory with Ishikawa diagram analysis. The proposed approach first establishes parametric causal relationships between failure modes and root causes through physics-based causal ordering. It then converts these physical parameters into practical failure events via Ishikawa diagram representation. A practical engineering case study demonstrates that the proposed approach can reduce reliance on experience and data. Moreover, it helps to narrow down the scope of potential failure causes, which can improve the completeness and accuracy of the results. The proposed method, which is adaptable to situations lacking data and experience, can be implemented in both the design and maintenance stages. It offers valuable guidance to designers in enhancing machines’ reliability and assists maintainers in precisely pinpointing and tracing the root cause of failures.