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Fault diagnosis for lithium-ion batteries in electric vehicles based on signal decomposition and two-dimensional feature clustering

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Green Energy and Intelligent Transportation 2022年第1期1卷 121-132页

作者： Shuowei Li Caiping Zhang Jingcai Du Xinwei Cong Linjing Zhang Yan Jiang Leyi Wang National Active Distribution Network Technology Research Center(NANTEC) Beijing Jiaotong UniversityBeijing 100044China Sunwoda Electronic Co. Ltd.Shenzhen518100China Department of Electrical&Computer Engineering Wayne State UniversityDetroitMI 48202USA

Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles(EVs).The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies,which are difficult to distinguish from faults.A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this *** geometry mode decomposition(SGMD)is introduced to obtain the components characterizing battery states,and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of *** 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of *** proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal *** the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness,high reliability,and long time scale warning,and the method is easy to implement online.

关键词： Electric vehicle Fault diagnosis Extended average voltage Dynamic time warping feature clustering

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A Cluster Based feature Selection Method for Cross-Project Software Defect Prediction

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Journal of Computer Science & Technology 2017年第6期32卷 1090-1107页

作者： Chao Ni Wang-Shu Liu Xiang Chen Qing Gu Dao-Xu Chen Qi-Guo Huang State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210023 China School of Computer Science and Technology Nantong University Nantong 226019 China

Cross-project defect prediction （CPDP） uses the labeled data from external source software projects to com- pensate the shortage of useful data in the target project, in order to build a meaningful classification model. However, the distribution gap between software features extracted from the source and the target projects may be too large to make the mixed data useful for training. In this paper, we propose a cluster-based novel method FeSCH （feature Selection Using Clusters of Hybrid-Data） to alleviate the distribution differences by feature selection. FeSCH includes two phases. Tile feature clustering phase clusters features using a density-based clustering method, and the feature selection phase selects features from each cluster using a ranking strategy. For CPDP, we design three different heuristic ranking strategies in the second phase. To investigate the prediction performance of FeSCH, we design experiments based on real-world software projects, and study the effects of design options in FeSCH （such as ranking strategy, feature selection ratio, and classifiers）. The experimental results prove the effectiveness of FeSCH. Firstly, compared with the state-of-the-art baseline methods, FeSCH achieves better performance and its performance is less affected by the classifiers used. Secondly, FeSCH enhances the performance by effectively selecting features across feature categories, and provides guidelines for selecting useful features for defect prediction.

关键词： software defect prediction cross-project defect prediction feature selection feature clustering density-basedclustering

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A New Diagnosis Method with Few-shot Learning Based on a Class-rebalance Strategy for Scarce Faults in Industrial Processes

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Machine Intelligence Research 2023年第4期20卷 583-594页

作者： Xinyao Xu De Xu Fangbo Qin Research Center of Precision Sensing and Control Institute of AutomationChinese Academy of SciencesBeijing100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing100049China

For industrial processes, new scarce faults are usually judged by experts. The lack of instances for these faults causes a severe data imbalance problem for a diagnosis model and leads to low performance. In this article, a new diagnosis method with few-shot learning based on a class-rebalance strategy is proposed to handle the problem. The proposed method is designed to transform instances of the different faults into a feature embedding space. In this way, the fault features can be transformed into separate feature clusters. The fault representations are calculated as the centers of feature clusters. The representations of new faults can also be effectively calculated with few support instances. Therefore, fault diagnosis can be achieved by estimating feature similarity between instances and faults. A cluster loss function is designed to enhance the feature clustering performance. Also, a class-rebalance strategy with data augmentation is designed to imitate potential faults with different reasons and degrees of severity to improve the model’s generalizability. It improves the diagnosis performance of the proposed method. Simulations of fault diagnosis with the proposed method were performed on the Tennessee-Eastman benchmark. The proposed method achieved average diagnosis accuracies ranging from 81.8% to 94.7% for the eight selected faults for the simulation settings of support instances ranging from 3 to 50. The simulation results verify the effectiveness of the proposed method.

关键词： Data augmentation feature clustering class-rebalance strategy few-shot learning fault diagnosis

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Research of Underwater Bottom Object and Reverberation in feature Space

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Journal of Marine Science and Application 2013年第2期12卷 235-239页

作者： Xiukun Li Zhi Xia Science and Technology on Underwater Acoustic Laboratory Harbin Engineering University Harbin 150001 China

The critical technical problem of underwater bottom object detection is founding a stable feature space for echo signals classification. The past literatures more focus on the characteristics of object echoes in feature space and reverberation is only treated as interference. In this paper, reverberation is considered as a kind of signal with steady characteristic, and the clustering of reverberation in frequency discrete wavelet transform （FDWT） feature space is studied. In order to extract the identifying information of echo signals, feature compression and cluster analysis are adopted in this paper, and the criterion of separability between object echoes and reverberation is given. The experimental data processing results show that reverberation has steady pattern in FDWT feature space which differs from that of object echoes. It is proven that there is separability between reverberation and object echoes.

关键词： underwater bottom object pattern of reverberation feature clustering feature space underwater object detection

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