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Development of clustering-based sensor fault detection and diagnosis strategy for chilled water system

机译：基于聚类的冷水系统传感器故障检测与诊断策略的发展

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This paper presents a new clustering-based sensor fault detection and diagnosis (SFDD) strategy for chilled water system. For data clustering, k-means algorithm was used and the optimal quantity of clusters was determined by Davis-Bouldin value. With the cluster centroid dataset, the featuring centroid score (CS) was determined for the fault-free sensor reading dataset thus the threshold for fault detection could be set. The database for sensor fault detection was then formed. By characterizing the CS patterns of different types of sensor fault, the database for sensor fault diagnosis was generated accordingly. Various sensor fault types could be handled, including bias, drift, precision degradation and complete failure. In this study, the developed SFDD strategy was applied to the sensor of primary chilled water return temperature in a water-cooled chilled water system. With the databases of sensor fault detection and diagnosis, the real-time measured sensor readings can be examined. Once sensor fault is detected, the fault type can be confirmed within a day at soonest or 2 days at most. The smallest detectable absolute bias value, absolute drifting rate and precision degradation error could be down to 0.25 degrees C, 0.025 degrees C/h and 0.1 degrees C respectively, demonstrating robustness of the proposed SFDD strategy. (C) 2019 Elsevier B.V. All rights reserved.
机译：本文提出了一种新的基于簇的冷冻水系统传感器故障检测和诊断（SFDD）策略。对于数据聚类，使用k-means算法，并通过Davis-Bouldin值确定最佳聚类数量。使用群集质心数据集，可以确定无故障传感器读取数据集的特征质心分数（CS），从而可以设置故障检测的阈值。然后形成用于传感器故障检测的数据库。通过表征不同类型传感器故障的CS模式，相应地生成了用于传感器故障诊断的数据库。可以处理各种传感器故障类型，包括偏置，漂移，精度下降和完全故障。在这项研究中，已开发的SFDD策略被应用于水冷冷冻水系统中的主要冷冻水回水温度传感器。借助传感器故障检测和诊断数据库，可以检查实时测量的传感器读数。一旦检测到传感器故障，就可以在一天之内或最多2天之内确认故障类型。最小的可检测绝对偏差值，绝对漂移率和精度降级误差可分别降至0.25摄氏度，0.025摄氏度/小时和0.1摄氏度，证明了所提出的SFDD策略的鲁棒性。 （C）2019 Elsevier B.V.保留所有权利。

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