Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A hybrid clustering multi-source fault diagnosis method for chiller temperature sensors
Sensor faults have been observed to negatively impact the operation of the HVAC system. Among these faults is the complexity of multi-source sensor faults, which may result in fault confusion due to multiple fault points and different fault patterns. This paper proposes a fault diagnosis model applicable to single- and multi-source faults of HVAC system sensors. Based on the distribution patterns of chillers sensor data, the ensemble empirical mode decomposition soft threshold denoising Gaussian mixture model (EEMDSTD-GMM) is proposed. The study suggests a K-means-based pre-classification method for potentially confusing types of sensor faults. EEMDSTD-GMM-K-means has shown a better fault diagnosis capability under four single-source sensor faults and five multi-source sensor faults. Under the three examined fault levels, the results indicate a satisfactory performance with an average diagnosis rate of 98.7% for single-source faults and 96.5% for multi-source faults.
A hybrid clustering multi-source fault diagnosis method for chiller temperature sensors
Sensor faults have been observed to negatively impact the operation of the HVAC system. Among these faults is the complexity of multi-source sensor faults, which may result in fault confusion due to multiple fault points and different fault patterns. This paper proposes a fault diagnosis model applicable to single- and multi-source faults of HVAC system sensors. Based on the distribution patterns of chillers sensor data, the ensemble empirical mode decomposition soft threshold denoising Gaussian mixture model (EEMDSTD-GMM) is proposed. The study suggests a K-means-based pre-classification method for potentially confusing types of sensor faults. EEMDSTD-GMM-K-means has shown a better fault diagnosis capability under four single-source sensor faults and five multi-source sensor faults. Under the three examined fault levels, the results indicate a satisfactory performance with an average diagnosis rate of 98.7% for single-source faults and 96.5% for multi-source faults.
A hybrid clustering multi-source fault diagnosis method for chiller temperature sensors
Yan, Xiuying (Autor:in) / Liu, Guangyu (Autor:in) / Zhang, Boyan (Autor:in) / Fan, Kaixing (Autor:in) / Li, Jun (Autor:in) / Du, Yifan (Autor:in)
Journal of Building Performance Simulation ; 16 ; 198-210
04.03.2023
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
A hybrid multiple sensor fault detection, diagnosis and reconstruction algorithm for chiller plants
| Taylor & Francis Verlag | 2023
Application of Classification Functions to Chiller Fault Detection and Diagnosis
| British Library Online Contents | 1997
Application of Classification Functions to Chiller Fault Detection and Diagnosis
| British Library Conference Proceedings | 1997