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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 (author) / Liu, Guangyu (author) / Zhang, Boyan (author) / Fan, Kaixing (author) / Li, Jun (author) / Du, Yifan (author)
Journal of Building Performance Simulation ; 16 ; 198-210
2023-03-04
13 pages
Article (Journal)
Electronic Resource
Unknown
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