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Efficient feature selection for enhanced chiller fault diagnosis: A multi-source ranking information-driven ensemble approach
Fault diagnosis (FD) is essential for ensuring the reliable operation of chillers and preventing energy waste. Feature selection (FS) is a critical prerequisite for effective FD. However, current FS methods have two major gaps. First, most approaches rely on single-source ranking information (SSRI) to evaluate features individually, which results in non-robust outcomes across different models and datasets due to the one-sided nature of SSRI. Second, thermodynamic mechanism features are often overlooked, leading to incomplete initial feature libraries, making it challenging to select optimal features and achieve better diagnostic performance. To address these issues, a robust ensemble FS method based on multi-source ranking information (MSRI) is proposed. By employing an efficient strategy based on maximizing relevance while proper redundancy, the MSRI method fully leverages Mutual Information, Information Gain, Gain Ratio, Gini index, Chi-squared, and Relief-F from both qualitative and quantitative perspectives. Additionally, comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature library. From a methodological standpoint, a general framework for constructing the MSRI-based FS method is provided. The proposed method is applied to chiller FD and tested across ten widely-used machine learning models. Thirteen optimized features are selected from the original set of forty-two, achieving an average diagnostic accuracy of 98.40% and an average F-measure above 94.94%, demonstrating the effectiveness and generalizability of the MSRI method. Compared to the SSRI approach, the MSRI method shows superior robustness, with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53% to 6.12%. Moreover, the MSRI method reduced computation time by 98.62% compared to wrapper methods, without sacrificing accuracy.
Efficient feature selection for enhanced chiller fault diagnosis: A multi-source ranking information-driven ensemble approach
Fault diagnosis (FD) is essential for ensuring the reliable operation of chillers and preventing energy waste. Feature selection (FS) is a critical prerequisite for effective FD. However, current FS methods have two major gaps. First, most approaches rely on single-source ranking information (SSRI) to evaluate features individually, which results in non-robust outcomes across different models and datasets due to the one-sided nature of SSRI. Second, thermodynamic mechanism features are often overlooked, leading to incomplete initial feature libraries, making it challenging to select optimal features and achieve better diagnostic performance. To address these issues, a robust ensemble FS method based on multi-source ranking information (MSRI) is proposed. By employing an efficient strategy based on maximizing relevance while proper redundancy, the MSRI method fully leverages Mutual Information, Information Gain, Gain Ratio, Gini index, Chi-squared, and Relief-F from both qualitative and quantitative perspectives. Additionally, comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature library. From a methodological standpoint, a general framework for constructing the MSRI-based FS method is provided. The proposed method is applied to chiller FD and tested across ten widely-used machine learning models. Thirteen optimized features are selected from the original set of forty-two, achieving an average diagnostic accuracy of 98.40% and an average F-measure above 94.94%, demonstrating the effectiveness and generalizability of the MSRI method. Compared to the SSRI approach, the MSRI method shows superior robustness, with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53% to 6.12%. Moreover, the MSRI method reduced computation time by 98.62% compared to wrapper methods, without sacrificing accuracy.
Efficient feature selection for enhanced chiller fault diagnosis: A multi-source ranking information-driven ensemble approach
Build. Simul.
Wang, Zhanwei (author) / Xia, Penghua (author) / Guo, Jingjing (author) / Zhou, Sai (author) / Wang, Lin (author) / Wang, Yu (author) / Zhang, Chunxiao (author)
Building Simulation ; 18 ; 141-159
2025-01-01
19 pages
Article (Journal)
Electronic Resource
English
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