基于缩放定律的故障诊断方法用于在线制冷剂泄漏检测
Fault Diagnosis Method Based on Scaling Law for On-line Refrigerant Leak Detection
AI总结：
本文提出了一种基于物理建模和空调系统控制机制的制冷剂泄漏故障诊断方法，通过推导与制冷剂泄漏相关的缩放定律，使模型能够适用于不同配置的空调系统，利用实验室的小规模离线故障测试数据估计缩放指数，并通过真实数据验证，证明了该方法在早期泄漏检测中的有效性。
摘要：
利用仪器化传感器数据进行早期故障检测是机器学习在工业设施中的一个有前景的应用领域。然而，由于目标诊断系统中复杂的系统配置和不足的故障数据，训练出的故障检测模型的泛化性能难以提高。将训练好的模型应用于其他系统并不容易。本文提出了一种考虑空调系统物理建模和控制机制的制冷剂泄漏故障诊断方法。我们推导出与制冷剂泄漏相关的有用缩放定律。如果控制机制相同，模型可以应用于其他空调系统，而不论系统配置如何。在实验室中获得的小规模离线故障测试数据用于估计缩放指数。我们通过真实数据评估所提出的缩放定律。基于两组之间相互作用的统计假设检验，我们证明了不同空调系统的缩放指数是等价的。此外，我们基于缩放定律对实际过程数据的泄漏程度时间序列进行了估计，并通过与专家评估的比较，证明了该方法在早期泄漏检测中的有效性。
英文摘要：
Early fault detection using instrumented sensor data is one of the promising application areas of machine learning in industrial facilities. However, it is difficult to improve the generalization performance of the trained fault-detection model because of the complex system configuration in the target diagnostic system and insufficient fault data. It is not trivial to apply the trained model to other systems. Here we propose a fault diagnosis method for refrigerant leak detection considering the physical modeling and control mechanism of an air-conditioning system. We derive a useful scaling law related to refrigerant leak. If the control mechanism is the same, the model can be applied to other air-conditioning systems irrespective of the system configuration. Small-scale off-line fault test data obtained in a laboratory are applied to estimate the scaling exponent. We evaluate the proposed scaling law by using real-world data. Based on a statistical hypothesis test of the interaction between two groups, we show that the scaling exponents of different air-conditioning systems are equivalent. In addition, we estimated the time series of the degree of leakage of real process data based on the scaling law and confirmed that the proposed method is promising for early leak detection through comparison with assessment by experts.
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