Fault detection and diagnostics in ventilation units using linear regression virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are one of the largest components in buildings systems and are responsible for large part of energy consumption. Ventilation units are complex components, often customized for the specific building. Their faults impact buildings' energy efficiency and occupancy comfort. In order to ensure their correct operation, proper Fault Detection and Diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose to exploit physical relations inside the unit to create virtual sensors from other sensors' readings, introducing redundancy in the system. We create linear regression models for three sensors using other sensors related through physical laws as inputs. We use two different measures to detect when a virtual sensor deviates from the actual one: coefficient of determination and acceptable range. We test our method on a real building at the University of Southern Denmark. Our method detects a fault in temperature sensor, where its readings have an abnormal trend and fall outside acceptable ranae for one day.Postprint (author's final draft

A method for fault detection and diagnostics in ventilation units using virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.Peer ReviewedPostprint (published version

Fault detection and diagnostics in ventilation units using linear regression virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are one of the largest components in buildings systems and are responsible for large part of energy consumption. Ventilation units are complex components, often customized for the specific building. Their faults impact buildings' energy efficiency and occupancy comfort. In order to ensure their correct operation, proper Fault Detection and Diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose to exploit physical relations inside the unit to create virtual sensors from other sensors' readings, introducing redundancy in the system. We create linear regression models for three sensors using other sensors related through physical laws as inputs. We use two different measures to detect when a virtual sensor deviates from the actual one: coefficient of determination and acceptable range. We test our method on a real building at the University of Southern Denmark. Our method detects a fault in temperature sensor, where its readings have an abnormal trend and fall outside acceptable ranae for one day

A method for fault detection and diagnostics in ventilation units using virtual sensors

Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.Peer Reviewe