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A systematic feature extraction and selection framework for data-driven whole-building automated fault detection and diagnostics in commercial buildings

机译：用于数据驱动的全建筑自动故障检测和商业建筑诊断的系统特征提取和选择框架

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摘要

In data-driven automated fault detection and diagnostics (AFDD) modeling for building energy systems, feature engineering is a critical process of extracting information from high-dimensional and noisy sensor measurement and turning it into informative and representative inputs or features for data-driven modeling. However, few studies specifically discuss the feature engineering, especially the interactions between feature extraction and feature selection in whole-building AFDD.We developed a systematic feature extraction and selection framework for whole-building AFDD. In this framework, features are aggressively extracted from raw sensor data using statistical feature extraction techniques with various window sizes and statistics. With many features extracted, a hybrid feature selection algorithm that combines the filter and wrapper method then selects the best feature set. The framework considers diversity in the duration of fault behavior among fault types in whole-building AFDD, thus achieving high model generalization.We implemented our developed framework in a virtual testbed calibrated with measured data from Oak Ridge National Laboratory's Flexible Research Platform designed to mimic the operation of a typical small commercial building. The AFDD model is trained by the simulation data generated from the virtual testbed. The results show that (1) the developed framework improves the generalization of the AFDD model by 10.7% compared with literature-reported feature extraction and selection methods and (2) features with diverse window sizes and statistics are selected, providing insight into physical systems beyond the current understanding of buildings and faults and improving the detection and diagnostics of multiple fault types.
机译：在数据驱动的自动故障检测和诊断（AFDD）建筑造型中建筑能量系统，特征工程是从高维和嘈杂的传感器测量中提取信息的关键过程，并将其转换为数据驱动建模的信息和代表性输入或功能。然而，很少有研究专门讨论特征工程，特别是全建筑物AFDD中的特征提取和特征选择之间的相互作用。我们为全建筑物AFDD开发了系统特征提取和选择框架。在本框架中，使用具有各种窗口尺寸和统计的统计特征提取技术，从原始传感器数据中积极提取功能。提取许多功能，将滤波器和包装器方法组合的混合特征选择算法选择最佳功能集。该框架在整个建筑物AFDD中的故障类型中的故障行为期间考虑了多样性，从而实现了高模型泛化。我们在虚拟测试平台中实现了我们的发达框架，其中oak岭全国实验室灵活的研究平台旨在模仿模仿典型小型商业建筑的运行。 AFDD模型由虚拟测试器生成的模拟数据训练。结果表明，（1）与文学报道的特征提取和选择方法相比，发达的框架提高了AFDD模型的泛化10.7％，并选择了具有多样化的窗口尺寸和统计数据的功能，从而进入身体系统的洞察力目前对建筑物和故障的认识，提高多种故障类型的检测和诊断。

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