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

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 paper.Symplectic 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 batteries.The 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 values.The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway.And 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.

Stress-induced trend:the clustering feature of coal mine disasters and earthquakes in China

Nearly half of coal mine disasters in China have been found to occur in clusters or to be accompanied by earthquakes nearby,in which all the disaster types are involved.Stress disturbances seem to exist among mining areas and to be responsible for the observed clustering.The earthquakes accompanied by coal mine disasters may be the vital geophysical evidence for tectonic stress disturbances around mining areas.This paper analyzes all the possible causative factors to demonstrate the authenticity and reliability of the observed phenomena.A quantitative study was performed on the degree of clustering,and space-time distribution curves are obtained.Under the threshold of 100 km,47%of disasters are involved in cluster series and 372 coal mine disasters accompanied by earthquakes.The majority cluster series lasting for 1-2 days correspond well earthquakes nearby,which are speculated to be related to local stress disturbance.While the minority lasting longer than 4 days correspond well with fatal earthquakes,which are speculated to be related to regional stress disturbance.The cluster series possess multiple properties,such as the area,the distance,the related disasters,etc.,and compared with the energy and the magnitude of earthquakes,good correspondences are acquired.It indicates that the cluster series of coal mine disasters and earthquakes are linked with fatal earthquakes and may serve as footprints of regional stress disturbance.Speculations relating to the geological model are made,and five disaster-causing models are examined.To earthquake research and disaster prevention,widely scientific significance is suggested.

Internal Defects Detection Method of the Railway Track Based on Generalization Features Cluster Under Ultrasonic Images

There may be several internal defects in railway track work that have different shapes and distribution rules,and these defects affect the safety of high-speed trains.Establishing reliable detection models and methods for these internal defects remains a challenging task.To address this challenge,in this study,an intelligent detection method based on a generalization feature cluster is proposed for internal defects of railway tracks.First,the defects are classified and counted according to their shape and location features.Then,generalized features of the internal defects are extracted and formulated based on the maximum difference between different types of defects and the maximum tolerance among same defects’types.Finally,the extracted generalized features are expressed by function constraints,and formulated as generalization feature clusters to classify and identify internal defects in the railway track.Furthermore,to improve the detection reliability and speed,a reduced-dimension method of the generalization feature clusters is presented in this paper.Based on this reduced-dimension feature and strongly constrained generalized features,the K-means clustering algorithm is developed for defect clustering,and good clustering results are achieved.Regarding the defects in the rail head region,the clustering accuracy is over 95%,and the Davies-Bouldin index(DBI)index is negligible,which indicates the validation of the proposed generalization features with strong constraints.Experimental results prove that the accuracy of the proposed method based on generalization feature clusters is up to 97.55%,and the average detection time is 0.12 s/frame,which indicates that it performs well in adaptability,high accuracy,and detection speed under complex working environments.The proposed algorithm can effectively detect internal defects in railway tracks using an established generalization feature cluster model.

Prediction of dissolved oxygen content changes based on two-dimensional behavior features of fish school and T-S fuzzy neural network

Dissolved oxygen(DO)content is an important index of river water quality.Water quality sensors have been used in China for urban river water monitoring and DO content prediction.However,water quality sensors are expensive and difficult to maintain,and have a short operation period and difficult to maintain.This study developed a scientific and accurate method for prediction of DO content changes using fish school features.The behavioral features of the Carassius auratus fish school were described using two-dimensional fish school images.The degree of DO content decline was graded into five levels,and the corresponding numerical ranges of cluster characteristic parameters were determined by considering the opinions of ichthyologists.Finally,the variation of DO content was predicted using the characteristic parameters of the fish school and the multiple-input single-output Takagi-Sugeno fuzzy neural network.The prediction results were basically consistent with the actual variations of DO content.Therefore,it is feasible to use the behavioral features of the fish school to dynamically predict the level of DO content in water,and this method is especially suitable for prediction of sharp decline of DO content in a relatively short time.

Clustering feature decision trees for semi-supervised classification from high-speed data streams

Most stream data classification algorithms apply the supervised learning strategy which requires massive labeled data.Such approaches are impractical since labeled data are usually hard to obtain in reality.In this paper,we build a clustering feature decision tree model,CFDT,from data streams having both unlabeled and a small number of labeled examples.CFDT applies a micro-clustering algorithm that scans the data only once to provide the statistical summaries of the data for incremental decision tree induction.Micro-clusters also serve as classifiers in tree leaves to improve classification accuracy and reinforce the any-time property.Our experiments on synthetic and real-world datasets show that CFDT is highly scalable for data streams while gener-ating high classification accuracy with high speed.

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

从外部来源软件的标记的数据投射在目标补偿有用数据的缺乏的跨工程的缺点预言(CPDP ) 使用投射，以便造一个有意义的分类模型。然而，在从来源和目标工程提取的软件特征之间的分发差距可能太大，不能为训练使混合数据有用。在这份报纸，我们建议基于簇的新奇方法 FeSCH (用簇的特征选择混合数据) 由特征选择减轻分发差别。FeSCH 包括二个阶段。用一个基于密度的聚类方法聚类阶段簇特征的特征，和特征选择分阶段执行用评价策略从每簇选择特征。为 CPDP，我们在第二个阶段设计三不同启发式的评价策略。调查 FeSCH 的预言性能，我们基于真实世界的软件工程设计实验，并且学习在 FeSCH 的设计选择的效果(例如评价策略，特征选择比率，和分类器) 。试验性的结果证明 FeSCH 的有效性。与最先进的基线方法相比，第一， FeSCH 完成更好的性能，它的表演是使用的分类器影响的更少。第二， FeSCH 由有效地越过特征范畴选择特征提高表演，并且为为缺点预言选择有用特征提供指南。

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

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.