冷水机组故障特征优化选择
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摘要
从现场应用的角度,针对冷水机组典型故障,提出了一种特征选择(FS)的方法,选择少量获取成本低的特征表征故障,达到以最低成本的传感器投入获得最优的故障检测与诊断(FDD)性能,从而节省FDD成本。首先,在现场应用的约束下,对64个原始特征进行特征初选,选择出传感器成本低和对故障敏感程度高的16个特征;然后,基于互信息的FS模型对这16特征进行特征中选,确定故障指示特征的最佳个数;最后,基于灰色聚类分析的FS模型再对这16特征进行特征终选,确定具体的特征种类。使用ASHRAE RP-1043故障实验数据和基于支持向量机的FDD工具验证了提出FS方法的有效性。
From the perspective of field applications, a feature selection(FS) method was proposed for chiller fault detection and diagnosis(FDD). The purpose was to obtain an optimal diagnostic performance and to save initial FDD costs. The technological paths were as follows: first, under the constraint of field applications, the original 64 features were selected preliminarily, 16 features obtained by low-cost sensors were selected; second, these 16 features were selected medially based on mutual information, in order to determine the optimal number of features describing the typical chiller faults; last, the specific feature subsets were determined based on grey clustering analysis. The experimental data from the ASHRAE RP-1043 and the FDD tool based on support vector machine were used to evaluate the proposed FS method.
From the perspective of field applications, a feature selection(FS) method was proposed for chiller fault detection and diagnosis(FDD). The purpose was to obtain an optimal diagnostic performance and to save initial FDD costs. The technological paths were as follows: first, under the constraint of field applications, the original 64 features were selected preliminarily, 16 features obtained by low-cost sensors were selected; second, these 16 features were selected medially based on mutual information, in order to determine the optimal number of features describing the typical chiller faults; last, the specific feature subsets were determined based on grey clustering analysis. The experimental data from the ASHRAE RP-1043 and the FDD tool based on support vector machine were used to evaluate the proposed FS method.
引文
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[8] BRAUN J. Automated Fault Detection and Diagnostics for Vapor Compression Cooling Equipment[J]. Journal of Solar Engineering, 2003, 125(3): 266-274.
[9]PENG H C , LONG F H , DING C. Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy[J]. IEEE transactions on pattern analysis and machine intelligence, 2005, 27(8):1226-1238.
[10] WU D C, TSAI W H. A steganographic method for images by pixel-value differencing[J]. Pattern Recognition Letters, 2003, 24(9): 1613-1626.
[11] 鲁峰,黄金泉. 基于灰色关联聚类的特征提取算法[J]. 系统工程理论与实践,2012,32(4):872-876.
[12] COMSTOCK MATHEW, BRAUN JAMES. Development of Analysis Tools for the Evaluation of Fault Detection and Diagnostics for Chillers [R]. ASHRAE Research Project 1043-RP, HL 99-20, Report #4036-3, 1999.
[13] 崔立志,刘思峰,李致平,等. 一种新的灰色相似关联度模型及其应用[J]. 统计与决策,2010,(7): 7-9.
[14] HAN H, GU B, KANG J, et al. Study on a hybrid SVM model for chiller FDD applications[J]. Applied Thermal Engineering, 2011, 31(4): 582-592.
[15] KIM M, YOON S H, DOMANSKI P A, et al. Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner[J]. International Journal of Refrigeration, 2008, 31(5): 790-799.
[2] LI G N, HU Y P, CHEN H X, et al. An improved fault detection method for incipient centrifugal chiller faults using the PCA-R-SVDD algorithm[J]. Energy and Buildings, 2016, 116: 104-113.
[3] ZHAO Y, WEN J, XIAO F, et al. Diagnostic Bayesian networks for diagnosing air handling units faults–part I: Faults in dampers, fans, filters and sensors[J]. Applied Thermal Engineering, 2017, 111: 1272-1286.
[4] TRAN D A T, CHEN Y M, AO H L, et al. An enhanced chiller FDD strategy based on the combination of the LSSVR-DE model and EWMA control charts[J]. International Journal of Refrigeration, 2016, 72: 81-96.
[5] DU Z M, FAN B, JIN X Q, et al. Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis[J]. Building and Environment, 2014, 73: 1-11.
[6] 黄彬彬, 谷波, 任能. 基于不完整描述样本的制冷系统故障诊断研究[J]. 低温与超导, 2008, 36(8): 41-49.
[7] WANG Z W, WANG Z W, GU X W, et al. Feature selection based on Bayesian network for chiller fault diagnosis from the perspective of field applications[J]. Applied Thermal Engineering, 2018, 129: 674-683.
[8] BRAUN J. Automated Fault Detection and Diagnostics for Vapor Compression Cooling Equipment[J]. Journal of Solar Engineering, 2003, 125(3): 266-274.
[9]PENG H C , LONG F H , DING C. Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy[J]. IEEE transactions on pattern analysis and machine intelligence, 2005, 27(8):1226-1238.
[10] WU D C, TSAI W H. A steganographic method for images by pixel-value differencing[J]. Pattern Recognition Letters, 2003, 24(9): 1613-1626.
[11] 鲁峰,黄金泉. 基于灰色关联聚类的特征提取算法[J]. 系统工程理论与实践,2012,32(4):872-876.
[12] COMSTOCK MATHEW, BRAUN JAMES. Development of Analysis Tools for the Evaluation of Fault Detection and Diagnostics for Chillers [R]. ASHRAE Research Project 1043-RP, HL 99-20, Report #4036-3, 1999.
[13] 崔立志,刘思峰,李致平,等. 一种新的灰色相似关联度模型及其应用[J]. 统计与决策,2010,(7): 7-9.
[14] HAN H, GU B, KANG J, et al. Study on a hybrid SVM model for chiller FDD applications[J]. Applied Thermal Engineering, 2011, 31(4): 582-592.
[15] KIM M, YOON S H, DOMANSKI P A, et al. Design of a steady-state detector for fault detection and diagnosis of a residential air conditioner[J]. International Journal of Refrigeration, 2008, 31(5): 790-799.