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A platform for research: civil engineering, architecture and urbanism
Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules
Highlights A fault detection and diagnosis process is applied on AHU monitoring data. A novel methodology tailored on transient and non-transient operation is proposed. Faults in the transient period are detected with multivariate association rules. Temporal associations are exploited during the start-up period of the system. Decision rules are extracted for fault diagnosis in non-transient regime of AHU.
Abstract The pervasive monitoring of HVAC systems through Building Energy Management Systems (BEMSs) is enabling the full exploitation of data-driven based methodologies for performing advanced energy management strategies. In this context, the implementation of Automated Fault Detection and Diagnosis (AFDD) based on collected operational data of Air Handling Units (AHUs) proved to be particularly effective to prevent anomalous running modes which can lead to significant energy waste over time and discomfort conditions in the built environment. The present work proposes a novel methodology for performing AFDD, based on both unsupervised and supervised data-driven methods tailored according to the operation of an AHU during transient and non-transient periods. The whole process is developed and tested on a sample of real data gathered from monitoring campaigns on two identical AHUs in the framework of the Research Project ASHRAE RP-1312. During the start-up period of operation, the methodology exploits Temporal Association Rules Mining (TARM) algorithm for an early detection of faults, while during non-transient period a number of classification models are developed for the identification of the deviation from the normal operation. The proposed methodology, conceived for quasi real-time implementation, proved to be capable of robustly and promptly identifying the presence of typical faults in AHUs.
Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules
Highlights A fault detection and diagnosis process is applied on AHU monitoring data. A novel methodology tailored on transient and non-transient operation is proposed. Faults in the transient period are detected with multivariate association rules. Temporal associations are exploited during the start-up period of the system. Decision rules are extracted for fault diagnosis in non-transient regime of AHU.
Abstract The pervasive monitoring of HVAC systems through Building Energy Management Systems (BEMSs) is enabling the full exploitation of data-driven based methodologies for performing advanced energy management strategies. In this context, the implementation of Automated Fault Detection and Diagnosis (AFDD) based on collected operational data of Air Handling Units (AHUs) proved to be particularly effective to prevent anomalous running modes which can lead to significant energy waste over time and discomfort conditions in the built environment. The present work proposes a novel methodology for performing AFDD, based on both unsupervised and supervised data-driven methods tailored according to the operation of an AHU during transient and non-transient periods. The whole process is developed and tested on a sample of real data gathered from monitoring campaigns on two identical AHUs in the framework of the Research Project ASHRAE RP-1312. During the start-up period of operation, the methodology exploits Temporal Association Rules Mining (TARM) algorithm for an early detection of faults, while during non-transient period a number of classification models are developed for the identification of the deviation from the normal operation. The proposed methodology, conceived for quasi real-time implementation, proved to be capable of robustly and promptly identifying the presence of typical faults in AHUs.
Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules
Piscitelli, Marco Savino (author) / Mazzarelli, Daniele Mauro (author) / Capozzoli, Alfonso (author)
Energy and Buildings ; 226
2020-08-02
Article (Journal)
Electronic Resource
English
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