Data mining based sensor fault diagnosis and validation for building air conditioning system

2006 ◽  
Vol 47 (15-16) ◽  
pp. 2479-2490 ◽  
Author(s):  
Zhijian Hou ◽  
Zhiwei Lian ◽  
Ye Yao ◽  
Xinjian Yuan
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Air Conditioning ◽  
Sensor Fault ◽  
Air Conditioning System

Research on diagnostic strategy for faults in VRF air conditioning system using hybrid data mining methods

2021 ◽  
pp. 111144
Author(s):  
Yuzhou Wang ◽  
Zhengfei Li ◽  
Huanxin Chen ◽  
Jianxin Zhang ◽  
Qian Liu ◽  
...  
Keyword(s):  
Data Mining ◽  
Air Conditioning ◽  
Diagnostic Strategy ◽  
Air Conditioning System ◽  
Hybrid Data ◽  
Mining Methods

Deep learning-based fault diagnosis of variable refrigerant flow air-conditioning system for building energy saving

2018 ◽  
Vol 225 ◽  
pp. 732-745 ◽  
Author(s):  
Yabin Guo ◽  
Zehan Tan ◽  
Huanxin Chen ◽  
Guannan Li ◽  
Jiangyu Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Energy Saving ◽  
Air Conditioning ◽  
Building Energy ◽  
Air Conditioning System ◽  
Building Energy Saving

scholarly journals A Novel Chiller Sensors Fault Diagnosis Method Based on Virtual Sensors

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3013
Author(s):  
Long Gao ◽  
Donghui Li ◽  
Ding Li ◽  
Lele Yao ◽  
Limei Liang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Air Conditioning ◽  
Short Term Memory ◽  
Fault Detection And Diagnosis ◽  
Normal Operation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Data Set ◽  
Virtual Sensors ◽  
Maximal Information Coefficient

Sensor fault detection and diagnosis (FDD) has great significance for ensuring the energy saving and normal operation of the air conditioning system. Chiller systems serving as an important part of central air conditioning systems are the major energy consumer in commercial and industrial buildings. In order to ensure the normal operation of the chiller system, virtual sensors have been proposed to detect and diagnose sensor faults. However, the performance of virtual sensors could be easily impacted by abnormal data. To solve this problem, virtual sensors combined with the maximal information coefficient (MIC) and a long short-term memory (LSTM) network is proposed for chiller sensor fault diagnosis. Firstly, MIC, which has the ability to quantify the degree of relevance in a data set, is applied to examine all potentially interesting relationships between sensors. Subsequently, sensors with high correlation are divided into several groups by the grouping thresholds. Two virtual sensors, which are constructed in each group by LSTM with different input sensors and corresponding to the same physical sensor, could have the ability to predict the value of physical sensors. High correlation sensors in each group improve the fitting effect of virtual sensors. Finally, sensor faults can be diagnosed by the absolute deviation which is generated by comparing the virtual sensors’ output with the actual value measured from the air-cooled chiller. The performance of the proposed method is evaluated by using a real data set. Experimental results indicate that virtual sensors can be well constructed and the proposed method achieves a significant performance along with a low false alarm rate.

scholarly journals Recognition of Variable-Speed Equipment in an Air-Conditioning System Using Numerical Analysis of Energy-Consumption Data

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4975 ◽  
Author(s):  
Rongjiang Ma ◽  
Xianlin Wang ◽  
Ming Shan ◽  
Nanyang Yu ◽  
Shen Yang
Keyword(s):  
Data Mining ◽  
Energy Consumption ◽  
Air Conditioning ◽  
Constant Speed ◽  
Variable Speed ◽  
Commercial Building ◽  
Total Energy Consumption ◽  
Air Conditioning System ◽  
Consumption Data ◽  
Air Conditioning Systems

Motor-driven equipment (ME) is one of the key components in an air-conditioning system, which contributes to the vast majority of the total energy consumption by air-conditioning systems. Distinguishing variable- and constant-speed equipment is important since the energy simulation models of the two types differ. Traditionally, types of ME are known in advance, and energy consumption data are consequently analyzed. However, in the application scenarios of energy consumption data mining, precedent information on the ME type could be missing. Thus, this study applies this process in reverse, providing new insight into energy consumption data of ME to recognize variable-speed ME in an air-conditioning system. The energy consumption data of ME in an air-conditioning system implemented in a commercial building were collected and numerically analyzed. A proposed simple parameter, coefficient of the median, and several numerical parameters were calculated and used to distinguish variable- from constant-speed ME. Results showed that the energy consumption data distributions of the two types of ME differed. The proposed coefficient of the median could successfully distinguish variable- from constant-speed ME, and it could be applied as an important step in energy consumption data mining of air-conditioning systems.

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