Abstract
This paper presents a fault detection and diagnosis (FDD) method, which uses one-dimensional convolutional neural network (1-D CNN) and WaveCluster clustering analysis to detect and diagnose sensor faults in the supply air temperature (Tsup) control loop of the air handling unit. In this approach, 1-D CNN is employed to extract man-guided features from raw data, and the extracted features are analyzed by WaveCluster clustering. The suspicious sensor faults are indicated and categorized by denoting clusters. Moreover, the Tc acquittal procedure is introduced to further improve the accuracy of FDD. In validation, false alarm ratio and missing diagnosis ratio are mainly used to demonstrate the efficiency of the proposed FDD method. Results show that the abrupt sensor faults in Tsup control loop can be efficiently detected and diagnosed, and the proposed method is equipped with good robustness within the noise range of 6 dBm∼13 dBm.
Highlights
With people’s increasing attention to energy consumption and greenhouse gas emission, heating, ventilation, and air conditioning (HVAC) will have continuous development in the future decades [1]
Any hardware failure or controller error, such as temperature sensor fault and the error in fault-tolerant control [3,4,5], associated with these subsystems might result in the abnormality of air handling unit (AHU) and affects the whole performance of HVAC system, leading to a negative impact on energy consumption, thermal comfort, and building maintenance cost [6]. e statistical result made in [7] shows that the publications about fault detection and diagnosis (FDD) of AHU occupied 42% of the total publications, being the most common application scenario
Focusing on detecting and diagnosing the abrupt sensor faults in supply air temperature (Tsup) loop of AHU, this paper presents a novel FDD method by combining one-dimensional convolutional neural network (1-D Convolutional neural network (CNN)) and clustering analysis
Summary
With people’s increasing attention to energy consumption and greenhouse gas emission, heating, ventilation, and air conditioning (HVAC) will have continuous development in the future decades [1]. Computational Intelligence and Neuroscience analysis in the connection of wavelet analysis and BPNN processes [18] In these abovementioned methods, the classification function of a well-trained BPNN was used to categorize the collected data and identify the running state of AHU. Focusing on detecting and diagnosing the abrupt sensor faults in supply air temperature (Tsup) loop of AHU, this paper presents a novel FDD method by combining one-dimensional convolutional neural network (1-D CNN) and clustering analysis. (3) Confirmation of candidate faults: remove the suspicious failures with low confidence score and generate the final FDD result By using this method, the abrupt sensor faults in Tsup loop of AHU system can be detected and diagnosed efficiently and accurately, and the high processing speed of 1-D CNN and WaveCluster clustering analysis makes this method applicable to online FDD
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