一个可解释的基于深度学习的预测性维护解决方案,用于监测空气压缩机状态
Alexandru Ciobotaru1, Cosmina Corches1, Dan Gota1
1Automation Department, Faculty of Automation and Computer Science, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
使用混合深度神经网络 (DNN) 和支持矢量机器 (SVM) 模型的预测性维护 (PdM) 准确地预测空气压缩机故障. 可解释的人工智能 (XAI) 提高了关键基础设施的这些人工智能驱动的维护预测的可靠性.
科学领域:
- * 工业工程和运营研究 * 工业工程和运营研究
- * 人工智能和机器学习
- *可靠性工程 *可靠性工程
背景情况:
- *空气压缩机是医疗保健,制造业和汽车等行业的关键部件,故障可能导致严重的中断和安全问题.
- *预测性维护 (PdM) 对于提高工业设备的可靠性至关重要,因为它可以在发生之前检测出潜在的故障.
- *目前的PdM方法需要强大的模型,能够分析传感器数据,以便早期检测故障.
研究的目的:
- * 开发和评估混合深度神经网络 (DNN) 和支持矢量机器 (SVM) 模型,用于空气压缩机组件的预测性维护.
- *将混合模型的性能与各种硬件平台的独立DNN和SVM模型进行比较.
- * 评估可解释AI (XAI) 方法对PdM系统透明度和可解释性的影响.
主要方法:
- * 设计了一种混合DNN-SVM模型,用于空气压缩机部件 (排气,轴承,水,散热器) 的状态监测和故障预测.
- *模型在NVIDIA T4 GPU,Raspberry Pi 4 Model B和NVIDIA Jetson Nano上进行了训练和验证,测量了延迟,能源消耗和二氧化碳排放.
- * 对于模型的可解释性,使用了沙普利增量解释 (SHAP),局部可解释模型-不可知论解释 (LIME) 和部分依赖图 (PDP).
主要成果:
- *混合DNN-SVM模型实现了高平均性能指标:98.71%的准确性,99.25%的精度,98.78%的回忆率和99.01%的F1得分在所有测试的设备中.
- *独立的DNN和SVM模型的平均性能较低,F1分数分别为89.37%和91.62%.
- * 整合XAI方法提高了预测性维护结果的透明度和可靠性.
结论:
- *混合DNN-SVM模型为空气压缩机预测性维护提供了一个高度准确和可靠的解决方案.
- * 该研究证明了在边缘计算设备上部署先进的AI驱动的PdM解决方案的可行性.
- *可解释的人工智能对于建立信任和促进人工智能驱动的维护系统的知情决策至关重要.
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