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A platform for research: civil engineering, architecture and urbanism
A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis
Highlights A novel two-stage data-driven FDD strategy for building chiller system is presented. Chiller fault detection and diagnosis task is formulated as a multi-class classification problem. The LDA-based algorithm can identify 7 typical chiller faults with high accuracy and detect unknown new fault. Once the fault type has been identified, the corresponding fault severity level can be recognized with high accuracy.
Abstract Chillers contribute to a significant part of the building energy consumption. In order to save energy and improve the performance of building automation systems, there is an increasing need for chiller fault detection and diagnosis (FDD). This paper proposes a two-stage data-driven FDD strategy which formulates the chiller fault detection and diagnosis task as a multi-class classification problem. Linear Discriminant Analysis (LDA) is adopted to project the high dimensional data into a lower dimensional space so as to achieve maximum class separation and original class information maintenance. At the first stage, a fault is detected and diagnosed if the monitoring data set is the closest to one of the predefined fault clusters and within the predefined Manhattan distance range of the corresponding fault. At the second stage, fault severity level is recognized by comparing the monitoring data set with the corresponding predefined severity level clusters. The fault is diagnosed as at a particular severity level if it is the closest to the corresponding severity level cluster. The proposed strategy is validated by the experimental data of ASHRAE Research Project 1043 (RP-1043). Results show that the data-driven FDD strategy using LDA can detect and diagnose chiller faults effectively.
A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis
Highlights A novel two-stage data-driven FDD strategy for building chiller system is presented. Chiller fault detection and diagnosis task is formulated as a multi-class classification problem. The LDA-based algorithm can identify 7 typical chiller faults with high accuracy and detect unknown new fault. Once the fault type has been identified, the corresponding fault severity level can be recognized with high accuracy.
Abstract Chillers contribute to a significant part of the building energy consumption. In order to save energy and improve the performance of building automation systems, there is an increasing need for chiller fault detection and diagnosis (FDD). This paper proposes a two-stage data-driven FDD strategy which formulates the chiller fault detection and diagnosis task as a multi-class classification problem. Linear Discriminant Analysis (LDA) is adopted to project the high dimensional data into a lower dimensional space so as to achieve maximum class separation and original class information maintenance. At the first stage, a fault is detected and diagnosed if the monitoring data set is the closest to one of the predefined fault clusters and within the predefined Manhattan distance range of the corresponding fault. At the second stage, fault severity level is recognized by comparing the monitoring data set with the corresponding predefined severity level clusters. The fault is diagnosed as at a particular severity level if it is the closest to the corresponding severity level cluster. The proposed strategy is validated by the experimental data of ASHRAE Research Project 1043 (RP-1043). Results show that the data-driven FDD strategy using LDA can detect and diagnose chiller faults effectively.
A data-driven strategy for detection and diagnosis of building chiller faults using linear discriminant analysis
Li, Dan (author) / Hu, Guoqiang (author) / Spanos, Costas J. (author)
Energy and Buildings ; 128 ; 519-529
2016-07-05
11 pages
Article (Journal)
Electronic Resource
English
The Sensitivity of Chiller Performance to Common Faults
| Taylor & Francis Verlag | 2001
The Sensitivity of Chiller Performance to Common Faults
| British Library Conference Proceedings | 2002