Scholarlink

<p>The governor valve actuator (GVA), as the actuating mechanism of the steam turbine governing system, directly impacts production safety and economic efficiency. Its highly coupled nature leads to high-dimensional operational data, complex fault modes, and inherent opacity in diagnostic algorithms, posing significant challenges to the real-time performance, reliability, and generalizability of fault diagnosis and early warning tasks. To address these challenges in complex multi-sensor networks, this paper proposes a post-hoc and pseudo-post-hoc predictive maintenance (PPPM) framework leveraging advanced machine learning and SHapley Additive exPlanations, an XAI technology. The PPPM optimizes fault diagnosis and early warning models and provides interpretable attribution analysis to guide predictive maintenance workflows. Experimental results on the GVA fault testing platform prove the effectiveness of the proposed method. For the fault diagnosis and localization task, taking the random forest model as an example, PPPM achieves the optimization of 50% of the measurement points of the sensor network and the attribution analysis of fault localization, which improves the real-time, generality and reliability of the diagnosis model. For the warning task, PPPM carries out sensor network optimization and attribution analysis to improve the pseudo-supervised warning model through the pseudo-supervised learning method. Taking isolated forests as an example, the optimized model improves the W-F1 score by 5.997% and the AUC by 6.942%.</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

Explainable AI for post-hoc and pseudo-post-hoc predictive maintenance of governor valve actuators

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月11日

查看全文
  • Jun Tang,
  • Yang Liu,
  • Xiaolu Huang,
  • Zhanpeng Jiang,
  • Fenghe Wu

摘要

The governor valve actuator (GVA), as the actuating mechanism of the steam turbine governing system, directly impacts production safety and economic efficiency. Its highly coupled nature leads to high-dimensional operational data, complex fault modes, and inherent opacity in diagnostic algorithms, posing significant challenges to the real-time performance, reliability, and generalizability of fault diagnosis and early warning tasks. To address these challenges in complex multi-sensor networks, this paper proposes a post-hoc and pseudo-post-hoc predictive maintenance (PPPM) framework leveraging advanced machine learning and SHapley Additive exPlanations, an XAI technology. The PPPM optimizes fault diagnosis and early warning models and provides interpretable attribution analysis to guide predictive maintenance workflows. Experimental results on the GVA fault testing platform prove the effectiveness of the proposed method. For the fault diagnosis and localization task, taking the random forest model as an example, PPPM achieves the optimization of 50% of the measurement points of the sensor network and the attribution analysis of fault localization, which improves the real-time, generality and reliability of the diagnosis model. For the warning task, PPPM carries out sensor network optimization and attribution analysis to improve the pseudo-supervised warning model through the pseudo-supervised learning method. Taking isolated forests as an example, the optimized model improves the W-F1 score by 5.997% and the AUC by 6.942%.