Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study co...Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.展开更多
Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data...Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data-driven fault detection and diagnosis model considering temporal dependency for composite air conditioning system that is capable of cooling the high heat flux in data centers.The input of fault detection and diagnosis model was an unsteady dataset generated by the experimentally validated transient mathematical model.The dataset concerned three typical faults,including refrigerant leakage,evaporator fan breakdown,and condenser fouling.Then,the CNN model was trained to construct a map between the input and system operating conditions.Further,the performance of the CNN model was validated by comparing it with the support vector machine and the neural network.Finally,the score-weighted class mapping activation method was utilized to interpret model diagnosis mechanisms and to identify key input features in various operating modes.The results demonstrated in the pump-driven heat pipe mode,the accuracy of the CNN model was 99.14%,increasing by around 8.5%compared with the other two methods.In the vapor compression mode,the accuracy of the CNN model achieved 99.9%and declined the miss rate of refrigerant leakage by at least 61%comparatively.The score-weighted class mapping activation results indicated the ambient temperature and the actuator-related parameters,such as compressor frequency in vapor compression mode and condenser fan frequency in pump-driven heat pipe mode,were essential features in system fault detection and diagnosis.展开更多
Deep learning(DL),especially convolutional neural networks(CNNs),has been widely applied in air handling unit(AHU)fault diagnosis(FD).However,its application faces two major challenges.Firstly,the accessibility of ope...Deep learning(DL),especially convolutional neural networks(CNNs),has been widely applied in air handling unit(AHU)fault diagnosis(FD).However,its application faces two major challenges.Firstly,the accessibility of operational state variables for AHU systems is limited in practical,and the effectiveness and applicability of existing DL methods for diagnosis require further validation.Secondly,the interpretability performance of DL models under various information scenarios needs further exploration.To address these challenges,this study utilized publicly available ASHRAE RP-1312 AHU fault data and employed CNNs to construct three FD models under three various information scenarios.Furthermore,the layer-wise relevance propagation(LRP)method was used to interpret and explain the effects of these three various information scenarios on the CNN models.An R-threshold was proposed to systematically differentiate diagnostic criteria,which further elucidates the intrinsic reasons behind correct and incorrect decisions made by the models.The results showed that the CNN-based diagnostic models demonstrated good applicability under the three various information scenarios,with an average diagnostic accuracy of 98.55%.The LRP method provided good interpretation and explanation for understanding the decision mechanism of CNN models for the unlimited information scenarios.For the very limited information scenario,since the variables are restricted,although LRP can reveal key variables in the model’s decision-making process,these key variables have certain limitations in terms of data and physical explanations for further improving the model’s interpretation.Finally,an in-depth analysis of model parameters—such as the number of convolutional layers,learning rate,βparameters,and training set size—was conducted to examine their impact on the interpretative results.This study contributes to clarifying the effects of various information scenarios on the diagnostic performance and interpretability of LRP-based CNN models for AHU FD,which helps provide improved reliability of DL models in practical applications.展开更多
变压器套管作为设备重要的绝缘部件,其绝缘性能直接影响着设备的安全运行。为诊断变压器套管绝缘状态,改善变压器套管油中溶解气体的小样本不平衡数据对变压器套管故障诊断结果的影响,使用粒子群优化结合反向传播神经网络(particle swar...变压器套管作为设备重要的绝缘部件,其绝缘性能直接影响着设备的安全运行。为诊断变压器套管绝缘状态,改善变压器套管油中溶解气体的小样本不平衡数据对变压器套管故障诊断结果的影响,使用粒子群优化结合反向传播神经网络(particle swarm optimization combined with back propagation neural network,PSO-BPNN)和自适应综合过采样(adaptive synthetic sampling,ADASYN)算法对变压器套管进行故障诊断。首先收集变压器套管的历史故障数据,建立具有明确故障类别的变压器套管油中溶解气体样本集,并通过ADASYN算法对原始数据中的少数类样本进行合成,得到平衡后的故障数据,然后将平衡后的油中溶解气体作为模型输入,故障状态作为标签输出,通过PSO-BPNN模型对变压器套管进行诊断,最后在原始样本集下使用反向传播神经网络(back propagation neural network,BPNN)、遗传结合反向传播神经网络(genetic combined with back propagation neural network,G-BPNN)算法、布谷鸟搜索结合反向传播神经网络(cuckoo search combined with back propagation neural network,CS-BPNN)算法以及PSO-BPNN模型对套管进行诊断。结果表明,针对变压器油纸套管绝缘状态进行故障诊断的多个模型中,基于ADASYN平衡数据后的PSO-BPNN模型和其他模型相比准确度最高,能有效减小小样本不平衡数据对诊断结果的影响,为判断变压器油纸套管绝缘性能提供了有效方法。展开更多
为实现车载空调制冷系统故障诊断功能,快速判断空调制冷系统可能出现的故障类型,文章建立车载空调制冷系统一维仿真模型,并以压缩机进出口温度、压力等参数为特征参数,冷凝器风量降低、制冷剂泄漏等故障为输出目标结果,构建车载空调制...为实现车载空调制冷系统故障诊断功能,快速判断空调制冷系统可能出现的故障类型,文章建立车载空调制冷系统一维仿真模型,并以压缩机进出口温度、压力等参数为特征参数,冷凝器风量降低、制冷剂泄漏等故障为输出目标结果,构建车载空调制冷系统的反向传播(back-propagation,BP)神经网络故障诊断模型和决策树故障诊断模型。研究结果表明:当冷凝器风量降低时,压缩机排气温度与排气压力上升,空调系统的制冷量和性能系数(coefficient of performance,COP)下降。通过对比2种不同诊断策略的仿真测试结果发现,采用BP神经网络进行车载空调制冷系统故障诊断的准确率可以达到92.5%。展开更多
基金Research Project of China Ship Development and Design Center。
文摘Screening similar historical fault-free candidate data would greatly affect the effectiveness of fault detection results based on principal component analysis(PCA).In order to find out the candidate data,this study compares unweighted and weighted similarity factors(SFs),which measure the similarity of the principal component subspace corresponding to the first k main components of two datasets.The fault detection employs the principal component subspace corresponding to the current measured data and the historical fault-free data.From the historical fault-free database,the load parameters are employed to locate the candidate data similar to the current operating data.Fault detection method for air conditioning systems is based on principal component.The results show that the weighted principal component SF can improve the effects of the fault-free detection and the fault detection.Compared with the unweighted SF,the average fault-free detection rate of the weighted SF is 17.33%higher than that of the unweighted,and the average fault detection rate is 7.51%higher than unweighted.
基金the support from the National Natural Science Foundation of China(Grant number 52176180)the support from“the open competition mechanism to select the best candidates”key technology project of Liaoning(Grant 2022JH1/10800008).
文摘Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data-driven fault detection and diagnosis model considering temporal dependency for composite air conditioning system that is capable of cooling the high heat flux in data centers.The input of fault detection and diagnosis model was an unsteady dataset generated by the experimentally validated transient mathematical model.The dataset concerned three typical faults,including refrigerant leakage,evaporator fan breakdown,and condenser fouling.Then,the CNN model was trained to construct a map between the input and system operating conditions.Further,the performance of the CNN model was validated by comparing it with the support vector machine and the neural network.Finally,the score-weighted class mapping activation method was utilized to interpret model diagnosis mechanisms and to identify key input features in various operating modes.The results demonstrated in the pump-driven heat pipe mode,the accuracy of the CNN model was 99.14%,increasing by around 8.5%compared with the other two methods.In the vapor compression mode,the accuracy of the CNN model achieved 99.9%and declined the miss rate of refrigerant leakage by at least 61%comparatively.The score-weighted class mapping activation results indicated the ambient temperature and the actuator-related parameters,such as compressor frequency in vapor compression mode and condenser fan frequency in pump-driven heat pipe mode,were essential features in system fault detection and diagnosis.
基金supported by the Opening Fund of Key Laboratory of Low-grade Energy Utilization Technologies and Systems of Ministry of Education of China(Chongqing University)(No.LLEUTS-202305)the National Natural Science Foundation of China(No.51906181)+4 种基金the Youth Innovation Technology Project of Higher School in Shandong Province(No.2022KJ204)“The 14th Five Year Plan”Hubei Provincial advantaged characteristic disciplines(groups)project of Wuhan University of Science and Technology(No.2023D0504,No.2023D0501)the Opening Fund of State Key Laboratory of Green Building in Western China(No.LSKF202316)Hubei Undergraduate Training Program for Innovation and Entrepreneurship(No.S202210488076)the Wuhan University of Science and Technology Postgraduate Innovation and Entrepreneurship Fund(JCX2023026).
文摘Deep learning(DL),especially convolutional neural networks(CNNs),has been widely applied in air handling unit(AHU)fault diagnosis(FD).However,its application faces two major challenges.Firstly,the accessibility of operational state variables for AHU systems is limited in practical,and the effectiveness and applicability of existing DL methods for diagnosis require further validation.Secondly,the interpretability performance of DL models under various information scenarios needs further exploration.To address these challenges,this study utilized publicly available ASHRAE RP-1312 AHU fault data and employed CNNs to construct three FD models under three various information scenarios.Furthermore,the layer-wise relevance propagation(LRP)method was used to interpret and explain the effects of these three various information scenarios on the CNN models.An R-threshold was proposed to systematically differentiate diagnostic criteria,which further elucidates the intrinsic reasons behind correct and incorrect decisions made by the models.The results showed that the CNN-based diagnostic models demonstrated good applicability under the three various information scenarios,with an average diagnostic accuracy of 98.55%.The LRP method provided good interpretation and explanation for understanding the decision mechanism of CNN models for the unlimited information scenarios.For the very limited information scenario,since the variables are restricted,although LRP can reveal key variables in the model’s decision-making process,these key variables have certain limitations in terms of data and physical explanations for further improving the model’s interpretation.Finally,an in-depth analysis of model parameters—such as the number of convolutional layers,learning rate,βparameters,and training set size—was conducted to examine their impact on the interpretative results.This study contributes to clarifying the effects of various information scenarios on the diagnostic performance and interpretability of LRP-based CNN models for AHU FD,which helps provide improved reliability of DL models in practical applications.
文摘变压器套管作为设备重要的绝缘部件,其绝缘性能直接影响着设备的安全运行。为诊断变压器套管绝缘状态,改善变压器套管油中溶解气体的小样本不平衡数据对变压器套管故障诊断结果的影响,使用粒子群优化结合反向传播神经网络(particle swarm optimization combined with back propagation neural network,PSO-BPNN)和自适应综合过采样(adaptive synthetic sampling,ADASYN)算法对变压器套管进行故障诊断。首先收集变压器套管的历史故障数据,建立具有明确故障类别的变压器套管油中溶解气体样本集,并通过ADASYN算法对原始数据中的少数类样本进行合成,得到平衡后的故障数据,然后将平衡后的油中溶解气体作为模型输入,故障状态作为标签输出,通过PSO-BPNN模型对变压器套管进行诊断,最后在原始样本集下使用反向传播神经网络(back propagation neural network,BPNN)、遗传结合反向传播神经网络(genetic combined with back propagation neural network,G-BPNN)算法、布谷鸟搜索结合反向传播神经网络(cuckoo search combined with back propagation neural network,CS-BPNN)算法以及PSO-BPNN模型对套管进行诊断。结果表明,针对变压器油纸套管绝缘状态进行故障诊断的多个模型中,基于ADASYN平衡数据后的PSO-BPNN模型和其他模型相比准确度最高,能有效减小小样本不平衡数据对诊断结果的影响,为判断变压器油纸套管绝缘性能提供了有效方法。
文摘为实现车载空调制冷系统故障诊断功能,快速判断空调制冷系统可能出现的故障类型,文章建立车载空调制冷系统一维仿真模型,并以压缩机进出口温度、压力等参数为特征参数,冷凝器风量降低、制冷剂泄漏等故障为输出目标结果,构建车载空调制冷系统的反向传播(back-propagation,BP)神经网络故障诊断模型和决策树故障诊断模型。研究结果表明:当冷凝器风量降低时,压缩机排气温度与排气压力上升,空调系统的制冷量和性能系数(coefficient of performance,COP)下降。通过对比2种不同诊断策略的仿真测试结果发现,采用BP神经网络进行车载空调制冷系统故障诊断的准确率可以达到92.5%。
