An evolving learning-based fault detection and diagnosis method: Case study for a passive chilled beam system
An evolving learning-based fault detection and diagnosis method: Case study for a passive chilled beam system
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一种不断发展的基于学习的故障检测和诊断方法：被动冷梁系统案例研究
DOI：
10.1016/j.energy.2022.126337
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发表时间：
2023
期刊：
影响因子：
9
通讯作者：
Dahal, Sujit
中科院分区：
文献类型：
--
作者：
Wang, Liping;Braun, James;Dahal, Sujit
传统的故障检测和诊断（FDD）方法从有限的操作条件下获得的训练数据中学习，然后停止学习。在这项研究中，我们开发了一种用于HVAC系统的基于进化学习的FDD方法，该方法随着建筑系统及其组件的性能变化而学习。具体而言，一个不断发展的学习算法增长高斯混合回归，用于构建一个数据驱动的模型，代表正常的性能和故障诊断的传递函数。将基于进化学习的故障诊断方法应用于被动式冷梁系统的常见故障检测与诊断。我们采用广义性能指标，如预测（期望）和测量之间的偏差，两个参数之间的差异，以及从参数中提取的其他特征。提出了一种新的故障特征选择方法。确定性能指标是否在正常操作范围内的不确定性阈值影响误报警率。通过将不确定性阈值从零提高到两个标准差，正常操作的误报率从14.8%降低到1.3%，归类为未知操作的正常操作数据百分比从25%降低到0%。对8个已知故障进行了检测和诊断，准确率为100%。一个新的断层在演化之前首先被归类为未知断层。通过更新高斯分量的关键参数对传递函数进行演化，实现了对未知故障的准确诊断。基于进化学习的故障诊断方法和新的特征选择方法可用于检测和诊断建成环境中其他系统或子系统的常见故障。
Traditional fault detection and diagnosis (FDD) methods learn from training data obtained under limited operating conditions, after which they stop learning. In this study, we developed an evolving learning-based FDD method for HVAC systems, which learns as the performance of a building system and its components changes. Specifically, an evolving learning algorithm—growing Gaussian mixture regression—is used to construct both a data-driven model representing normal performance and a transfer function for fault diagnosis. The evolving learning-based FDD method was demonstrated for detecting and diagnosing common faults of passive chilled beam systems. We employ generalized performance indices, such as the deviations between predictions (expectations) and measurements, the differences between two parameters, and other features extracted from parameters. A novel feature selection method was developed for selecting fault signatures. An uncertainty threshold determining whether a performance index was within the range of normal operation influences false alarm rates. By increasing the uncertainty thresholds from zero to two standard deviations, false alarm rates for normal operations were reduced from 14.8% to 1.3% and the percentage of normal operation data categorized as an unknown operation was reduced from 25% to 0%. Eight known faults were detected and diagnosed with an accuracy of 100%. A new fault was first categorized as an unknown fault before evolving. After evolving the transfer function by updating the key parameters of the Gaussian components, the unknown fault was also accurately diagnosed. The evolving learning-based FDD method and novel feature selection method can be employed for detecting and diagnosing common faults of other systems or subsystems in the built environment.