Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery...Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.展开更多
针对动力电池荷电状态(state of charge,SOC)的估算问题,利用长短期记忆(LSTM)循环神经网络建立SOC估算模型,以实验室恒流放电数据训练模型并测试,测试最大绝对误差为2.7%。进一步以FSEC赛车电池实测数据验证,最大测试误差为3.9%。但在...针对动力电池荷电状态(state of charge,SOC)的估算问题,利用长短期记忆(LSTM)循环神经网络建立SOC估算模型,以实验室恒流放电数据训练模型并测试,测试最大绝对误差为2.7%。进一步以FSEC赛车电池实测数据验证,最大测试误差为3.9%。但在工程应用时,考虑到实际运行过程中的环境复杂性以及不同驾驶习惯对动力电池造成的不一致性,需要根据车辆实际行驶工况数据对其进行训练与测试,但是由于该数据中的SOC直接由BMS报文解析而来,无法确定BMS内的SOC算法是否准确,故不能直接用作训练模型时的标签,此时需计算出正确的训练标签或借助已有标签的模型,在其基础上根据实际运行数据对其模型参数进行动态调整。为解决无标签数据的训练问题,本文采取第二种方法,首次提出将迁移学习中的领域自适应网络(DaNN)与LSTM组合形成LSTM-DaNN的SOC估算算法,利用有标签数据预先训练好LSTM模型,再将其模型参数迁移至LSTM-DaNN,最后综合有标签与无标签数据一起对LSTM-DaNN模型进行训练。测试结果表明LSTM-DaNN可以在没有实际行驶工况标签(SOC)的情况下完成训练,最大测试误差为4.8%,相比模型自适应调整前误差下降了14.1%,且保证绝对误差<5%,满足实际需求。展开更多
The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildin...The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildings.Both knowledge transfer learning(KTL)and data incremental learning(DIL)can address the data shortage issue of such buildings.For new building scenarios with continuous data accumulation,the performance of BEP models has not been fully investigated considering the data accumulation dynamics.DIL,which can learn dynamic features from accumulated data adapting to the developing trend of new building time-series data and extend BEP model's knowledge,has been rarely studied.Previous studies have shown that the performance of KTL models trained with fixed data can be further improved in scenarios with dynamically changing data.Hence,this study proposes an improved transfer learning cross-BEP strategy continuously updated using the coarse data incremental(CDI)manner.The hybrid KTL-DIL strategy(LSTM-DANN-CDI)uses domain adversarial neural network(DANN)for KLT and long short-term memory(LSTM)as the Baseline BEP model.Performance evaluation is conducted to systematically qualify the effectiveness and applicability of KTL and improved KTL-DIL.Real-world data from six-type 36 buildings of six types are adopted to evaluate the performance of KTL and KTL-DIL in data-driven BEP tasks considering factors like the model increment time interval,the available target and source building data volumes.Compared with LSTM,results indicate that KTL(LSTM-DANN)and the proposed KTL-DIL(LSTM-DANN-CDI)can significantly improve the BEP performance for new buildings with limited data.Compared with the pure KTL strategy LSTM-DANN,the improved KTL-DIL strategy LSTM-DANN-CDI has better prediction performance with an average performance improvement ratio of 60%.展开更多
基金Shaanxi Province key Research and Development Plan-Listed project(2022-JBGS-07)。
文摘Aiming at the problems of low efficiency,poor anti-noise and robustness of transfer learning model in intelligent fault diagnosis of rotating machinery,a new method of intelligent fault diagnosis of rotating machinery based on single source and multi-target domain adversarial network model(WDMACN)and Gram Angle Product field(GAPF)was proposed.Firstly,the original one-dimensional vibration signal is preprocessed using GAPF to generate the image data including all time series.Secondly,the residual network is used to extract data features,and the features of the target domain without labels are pseudo-labeled,and the transferable features among the feature extractors are shared through the depth parameter,and the feature extractors of the multi-target domain are updated anatomically to generate the features that the discriminator cannot distinguish.The modelt through adversarial domain adaptation,thus achieving fault classification.Finally,a large number of validations were carried out on the bearing data set of Case Western Reserve University(CWRU)and the gear data.The results show that the proposed method can greatly improve the diagnostic efficiency of the model,and has good noise resistance and generalization.
文摘针对动力电池荷电状态(state of charge,SOC)的估算问题,利用长短期记忆(LSTM)循环神经网络建立SOC估算模型,以实验室恒流放电数据训练模型并测试,测试最大绝对误差为2.7%。进一步以FSEC赛车电池实测数据验证,最大测试误差为3.9%。但在工程应用时,考虑到实际运行过程中的环境复杂性以及不同驾驶习惯对动力电池造成的不一致性,需要根据车辆实际行驶工况数据对其进行训练与测试,但是由于该数据中的SOC直接由BMS报文解析而来,无法确定BMS内的SOC算法是否准确,故不能直接用作训练模型时的标签,此时需计算出正确的训练标签或借助已有标签的模型,在其基础上根据实际运行数据对其模型参数进行动态调整。为解决无标签数据的训练问题,本文采取第二种方法,首次提出将迁移学习中的领域自适应网络(DaNN)与LSTM组合形成LSTM-DaNN的SOC估算算法,利用有标签数据预先训练好LSTM模型,再将其模型参数迁移至LSTM-DaNN,最后综合有标签与无标签数据一起对LSTM-DaNN模型进行训练。测试结果表明LSTM-DaNN可以在没有实际行驶工况标签(SOC)的情况下完成训练,最大测试误差为4.8%,相比模型自适应调整前误差下降了14.1%,且保证绝对误差<5%,满足实际需求。
基金jointly supported by the Opening Fund of Key Laboratory of Low-grade Energy Utilization Technologies and Systems of Ministry of Education of China(Chongqing University)(LLEUTS-202305)the Opening Fund of State Key Laboratory of Green Building in Western China(LSKF202316)+4 种基金the open Foundation of Anhui Province Key Laboratory of Intelligent Building and Building Energy-saving(IBES2022KF11)“The 14th Five-Year Plan”Hubei Provincial advantaged characteristic disciplines(groups)project of Wuhan University of Science and Technology(2023D0504,2023D0501)the National Natural Science Foundation of China(51906181)the 2021 Construction Technology Plan Project of Hubei Province(2021-83)the Science and Technology Project of Guizhou Province:Integrated Support of Guizhou[2023]General 393.
文摘The available modelling data shortage issue makes it difficult to guarantee the performance of data-driven building energy prediction(BEP)models for both the newly built buildings and existing information-poor buildings.Both knowledge transfer learning(KTL)and data incremental learning(DIL)can address the data shortage issue of such buildings.For new building scenarios with continuous data accumulation,the performance of BEP models has not been fully investigated considering the data accumulation dynamics.DIL,which can learn dynamic features from accumulated data adapting to the developing trend of new building time-series data and extend BEP model's knowledge,has been rarely studied.Previous studies have shown that the performance of KTL models trained with fixed data can be further improved in scenarios with dynamically changing data.Hence,this study proposes an improved transfer learning cross-BEP strategy continuously updated using the coarse data incremental(CDI)manner.The hybrid KTL-DIL strategy(LSTM-DANN-CDI)uses domain adversarial neural network(DANN)for KLT and long short-term memory(LSTM)as the Baseline BEP model.Performance evaluation is conducted to systematically qualify the effectiveness and applicability of KTL and improved KTL-DIL.Real-world data from six-type 36 buildings of six types are adopted to evaluate the performance of KTL and KTL-DIL in data-driven BEP tasks considering factors like the model increment time interval,the available target and source building data volumes.Compared with LSTM,results indicate that KTL(LSTM-DANN)and the proposed KTL-DIL(LSTM-DANN-CDI)can significantly improve the BEP performance for new buildings with limited data.Compared with the pure KTL strategy LSTM-DANN,the improved KTL-DIL strategy LSTM-DANN-CDI has better prediction performance with an average performance improvement ratio of 60%.
