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Fault diagnosis method for lithium-ion batteries in electric vehicles based on isolated forest algorithm

JOURNAL OF ENERGY STORAGE 2022年 50卷

作者： Jiang, Jiuchun Li, Taiyu Chang, Chun Yang, Chen Liao, Li Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Ener Wuhan 430068 Peoples R China Sunwoda Elect Co Ltd Shenzhen 518108 Peoples R China

Electric vehicle safety accidents caused by Lithium-ion (Li-ion) batteries failures are numerous in recent years. The voltage data of a faulty battery will have abnormal changes before a safety accident occurs. The voltage variation of a progressive failure is more obvious, while the voltage change of a sudden failure is concealed. This paper proposes a fault diagnosis method for power lithium battery based on isolated forest algorithm. Firstly, the original voltage data are signal processed and decomposed into static components highly correlated with aging inconsistency and dynamic components reflecting anomalous information, and then the characteristic parameters of static and dynamic components are extracted and fed into the isolated forest algorithm for anomaly detection to identify anomalous cells. Finally, the proposed method is tested with voltage data from four faulty vehicles. The tests prove that the method has good advance detection ability for both progressive and sudden failures, which confirms its advance detection effect in power lithium battery fault diagnosis and its feasibility of real-time application in real vehicles.

关键词： Lithium-ion battery Fault diagnosis Variational mode decomposition(VMD) isolated forest algorithm Outlier detection

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Distribution network line loss analysis method based on improved clustering algorithm and isolated forest algorithm

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SCIENTIFIC REPORTS 2024年第1期14卷 1-15页

作者： Li, Jian Li, Shuoyu Zhao, Wen Li, Jiajie Zhang, Ke Jiang, Zetao Guangdong Power Grid Co Ltd Metrol Ctr Guangzhou 511545 Peoples R China Dongguan Power Supply Bur Power Supply Serv Dongguan 523576 Peoples R China

The long-term loss of distribution network in the process of distribution network development is caused by the backward management mode of distribution network. The traditional analysis and calculation methods of distribution network loss can not adapt to the current development environment of distribution network. To improve the accuracy of filling missing values in power load data, particle swarm optimization algorithm is proposed to optimize the clustering center of the clustering algorithm. Furthermore, the original isolated forest anomaly recognition algorithm can be used to detect outliers in the load data, and the coefficient of variation of the load data is used to improve the recognition accuracy of the algorithm. Finally, this paper introduces a breadth-first-based method for calculating line loss in the context of big data. An example is provided using the distribution network system of Yuxi City in Yunnan Province, and a simulation experiment is carried out. And the findings revealed that the error of the enhanced fuzzy C-mean clustering algorithm was on average - 6.35, with a standard deviation of 4.015 in the situation of partially missing data. The area under the characteristic curve of the improved isolated forest algorithm subjects in the case of the abnormal sample fuzzy situation was 0.8586, with the smallest decrease, based on the coefficient of variation, and through the refinement of the analysis, it was discovered that the feeder line loss rate is 7.62%. It is confirmed that the suggested technique can carry out distribution network line loss analysis fast and accurately and can serve as a guide for managing distribution network line loss.

关键词： Fuzzy C-Means isolated forest algorithm Medium voltage distribution networks Line loss analysis Data processing

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Target Assignment Method for UAV Formation based on isolated forest algorithm

Target Assignment Method for UAV Formation based on Isolated...

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2022年第二届高校教育发展与信息技术创新国际学术会议

作者： Xin Zhang Zhijun Liu College of Aeronautical Engineering Beijing Polytechnic

Collaborative target assignment is one of the key technologies of UAV formation battle command *** paper proposes a method of UAV formation cooperative target assignment based on isolated forest *** the constraint conditions of isolated forest algorithm,and then use the isolated forest algorithm to allocate the cooperative target of UAV formation,design the allocation process,and achieve the cooperative target allocation of UAV *** experimental results show that the energy consumption of target allocation is low.

关键词： isolated forest algorithm UAV Collaboration target allocation

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isolated forest-based early warning approach for micro drilling time-frequency conversion jammed drilling incidents

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PETROLEUM SCIENCE AND TECHNOLOGY 2025年

作者： Yang, Huohai Wang, Zhichao Zhang, Jichuan Chen, Xin Gao, Lin Zhang, Li Li, Renze Li, Zhirong Southwest Petr Univ Sch Petr Engn Chengdu Peoples R China Top Energy Technol Co Chengdu Peoples R China Southwest Petr Univ Sch New Energy & Mat Chengdu Peoples R China

In past jammed drilling early warning research, relying on static physical models and manual experience assessment is common but ineffective in handling complex dynamic changes during drilling. This research presents a novel real-time jamming accident early warning system for micro drills, based on time-frequency conversion and the isolated forest algorithm. By comparing time-frequency analysis techniques, wavelet transform is chosen. It integrates the isolated forest algorithm for anomaly detection with wavelet transform for efficient analysis. Wavelet transform captures load and torque trends during drilling, while the isolated forest algorithm excels in real-time anomaly detection. Experimental results from on-site micro drilling data show that the system significantly enhances safety and financial benefits. For example, it accurately detected potential jamming incidents with high precision, reducing downtime and equipment damage and improving the accuracy of stuck drilling accident prediction compared to traditional methods.

关键词： anomaly detection drilling parameters incident warning isolated forest algorithm timing data wavelet transform

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Fault Detection of Air-spring Devices Based on GANomaly and isolated forest algorithms 16

Fault Detection of Air-spring Devices Based on GANomaly and ...

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16th IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Wan, Zipeng Ma, Junjie Qin, Na Zhou, Zhonghe Huang, Deqing Southwest Jiaotong Univ Sch Elect Engn Chengdu Peoples R China Shaanxi Railway & Logist Ind Grp Co Ltd Xian Peoples R China

ISBN: (纸本)9781665422482

Aiming at detecting the crack-type and bulge-type faults of the high-speed train's air-spring devices, a computer vision-based image fault detection method is proposed. In this paper, we select the GANomaly network model, which is sensitive to bulge fault features, and histogram of oriented gradients (HOG) feature extraction combines with the isolated forest algorithm, which is sensitive to crack fault features to detection failures. Based on the means of sliding window segmentation, the positive and negative samples are divided into a large number of small pictures. They are easier to detect abnormal features. Then, these pictures are fed into the GANomaly network model. By comparing with the latent vector spaces obtained via encoding between positive and negative samples, bulge-type faults can be detected. HOG features are extracted from the small pictures, utilizing the isolated forest algorithm to detect crack type faults. Finally, marking a small picture with the highest anomaly score in the original image to complete precise location of fault object. or Math in Paper Title or Abstract.

关键词： air-spring GANomaly HOG feature isolated forest algorithm

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Optimization of fitness data monitoring system based on Internet of Things and cloud computing

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COMPUTER COMMUNICATIONS 2021年 177卷 125-132页

作者： Shang, Xiuhai Che, Xusheng Sangmyung Univ Dept Phys Educ Seoul 03016 South Korea NanTong Univ Sport & Sci Collage Nantong 226019 Jiangsu Peoples R China

In the service dimension, the construction of fitness science data supervision service mode is discussed. Based on the stakeholder theory, through the statistical analysis of the stakeholders of fitness science data supervision, three core stakeholders of the government, users and data service personnel are identified. Based on these three dimensions, we find out the core concepts of government policy model, user demand model and service model. At the same time, each dimension is deeply analyzed. Through the relationship analysis between these three dimensions, the user-oriented collaborative supervision service model of fitness scientific data is expected to guide the specific service practice of fitness scientific data supervision through the establishment of this model. In addition, an unsupervised learning method in machine learning, the isolation forest algorithm, is introduced to detect abnormal data;at the same time, using real fitness data sets, through comparative experiments with local anomaly factor algorithms, it is verified that the isolation forest algorithm has a good effect of anomaly detection;this article also uses redis cache to optimize the performance of the fitness data monitoring system, which solves the access pressure of the main database in a multi-user high-concurrency environment;Finally, the usability and stability of the system are verified by functional tests and stress tests.

关键词： Internet of Things Cloud computing Fitness data supervision isolated forest algorithm Data monitoring system

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Research on privacy data protection of power wireless network based on heterogeneous data sequence

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ENERGY REPORTS 2022年 8卷 687-695页

作者： Li, Furong Wang, Duan Huanghuai Univ Sch Int Educ Zhumadian 463000 Peoples R China

As an energy industry, the power industry is related to the national economy and the people's livelihood. It is applied to intensive technology types, and involves all walks of life. Due to the opening of power market and the changes of social economy, such as Internet economy and data economy, the traditional power industry is facing great challenges. Considering the practical problem that the private data anomaly detection model of power network lacks high-quality labeled data set and the anomaly detector is updated in time according to the feedback, an adaptive anomaly detection model of human in the loop is constructed so as to effectively reduce the phenomenon of false positives. This paper proposes an adaptive anomaly detection algorithm based on isolated forest and prediction analysis. The abnormal score is calculated according to the algorithm. The sorting list of abnormal data is given. The samples to be labeled are selected by pal strategy and handed over to the analysts, and then the weight of each side in the database is updated in time according to the labeling feedback. The experimental results show that the detection accuracy of this algorithm is 95% under the low manual annotation budget, which is 5%-10% higher than other algorithms. Besides, about 70% abnormal behaviors were detected from 20000 test data. It realizes the purpose of identifying directional network attacks from multi-source heterogeneous and noisy cyberspace data. This research can help the power department quickly find the abnormal behavior caused by network attack, which aims to solve the problems of low intelligence level and lack of correlation between multi-source heterogeneous data in power network attack detection. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Power wireless network Privacy data protection Multi-source heterogeneous data Data sequence detection isolated forest algorithm

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A method for detecting abnormal behavior of ships based on multi-dimensional density distance and an abnormal isolation mechanism

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2023年第8期20卷 13921-13946页

作者： Zhang, Lixiang Zhu, Yian Ren, Jie Lu, Wei Yao, Ye Northwestern Polytech Univ Sch Comp Sci Xian 710072 Peoples R China Xian Univ Finance & Econ Sch Informat Xian 710100 Peoples R China

Abnormal ship behavior detection is essential for maritime navigation safety. Most existing abnormal ship behavior detection methods only build A ship trajectory position outlier detection model;however, the construction of a ship speed outlier detection model is also significant for maritime navigation safety. In addition, in most existing methods for detecting a ship's abnormal behavior based on abnormal thresholds, one unsuitable threshold leads to the risk of the ship not being minimized as much as possible. In this paper, we proposed an abnormal ship behavior detection method based on distance measurement and an isolation mechanism. First, to address the problem of traditional trajectory compression methods and density clustering methods only using ship position information, the minimum description length principle based on acceleration (AMDL) algorithm and Multi-Dimensional Density Clustering (MDDBSCAN) algorithm is used in this study. These algorithms not only considered the position information of the ship, but also the speed information. Second, regarding the issue of the difficulty in determining the anomaly threshold, one method for determining the anomaly threshold based on the relationship between the velocity weights and noise points of the MDDBSCAN algorithm has been introduced. Finally, due to the randomness issue of the selected segmentation value in iforest, a strategy of selectively constructing isolated trees was proposed, thus further improving the efficiency of abnormal ship behavior detection. The experimental results on the historical automatic identification system data set of Xiamen port prove the practicality and effectiveness of our proposed method. Our experiment results show that the proposed method achieves an improvement of about 10% over the trajectory outlier detection based on the local outlier fraction method, about 14% over the isolation-based online anomalous trajectory method in terms of the accuracy of ship position informatio

关键词： AIS data outlier detection MDDBSCAN isolated forest algorithm

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Construction of pollution risk early warning model for urban drinking water supply chain

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WATER SUPPLY 2022年第12期22卷 8540-8556页

作者： Cao, Yongxiao Zhang, Xianglong Chen, Zihan Zhang, Zhixiao Wei, Huaibin North China Univ Water Resources & Elect Power Sch Water Conservancy Zhengzhou 450046 Peoples R China Anyang Zhongzhou Co Ltd Henan 455000 Peoples R China

In order to improve the efficiency of urban drinking water safety monitoring and early warning management, a pollution risk early warning model of urban drinking water supply chain is proposed. Firstly, the current situation of urban drinking water supply is analyzed and the causes of pollution are analyzed. Then, the AR model (autoregressive model) is used to predict the time series of multiple water quality indicators by constantly introducing new monitoring data mode for the residual vector group, the outlier scores of each vector group are obtained by using the isolated forest algorithm to judge whether the water quality is abnormal or not, and the fuzzy comprehensive evaluation method is used to evaluate the level of the abnormal situation and carry out the corresponding level early warning. The experimental results show that the AUC can reach 0.919 when using the prediction residual vector group of turbidity and conductivity to detect the numerical changes of water quality parameters in drinking water supply chain, accurately predict the abnormal data, make early warning, and provide guarantee for the survival of urban residents and urban development.

关键词： AR model fuzzy comprehensive evaluation method isolated forest algorithm pollution risk early warning urban drinking water safety

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A Typical Output Mode Analysis Method for Distributed Renewable Generation Based on K-Means 4th

A Typical Output Mode Analysis Method for Distributed Renewa...

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4th International Symposium on New Energy and Electrical Technology (ISNEET)

作者： Zhang, Yi Zhang, Feng Wu, Yifeng Qiu, Yutao Mo, Jianguo Tang, Lv Xie, Xiahui Zhejiang Univ Water Resources & Elect Power Hangzhou Zhejiang Peoples R China State Grid Zhejiang Electir Power Co Ltd Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9789819770496;9789819770472;9789819770465

With the development of a green and low-carbon economy, distributed renewable generation (DRG) is growing rapidly, with obvious advantages and disadvantages. DRG has characteristics such as volatility, randomness, and uncontrollability, which may have an impact and adverse impact on the safe operation of the power grid. If it can accurately predict and grasp its power generation law, it has significant significance for improving the safe operation of the power grid. This article innovatively proposes a K-means based DRG typical output mode analysis method. The method first collects the DRG output of two regions over a long period of time, uses isolated forest algorithm to normalize and fuse the collected data, and uses K-MEANS method to cluster the processed data. Finally, 16 representative output modes of these regions are obtained, These models can be used to guide the output prediction and analysis of corresponding types of weather days. The example analysis shows that this method is generally effective and has good robustness, and can be used to guide the analysis of DRG output patterns in similar regions.

关键词： Distributed renewable generation (DRG) isolated forest algorithm K-means

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