Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (6): 30-40.doi: 10.6040/j.issn.1672-3961.0.2022.215

• 交通工程——智慧交通专题 • Previous Articles    

Fault detection of autonomous vehicle based on bi-layer hybrid ensemble

MIN Haigen1,2,3, LEI Xiaoping1, LI Jie4, TONG Xing4, WU Xia1,3*, FANG Yukun1   

  1. 1. School of Information and Engineering, Chang'an University, Xi'an 710064, Shaanxi, China;
    2. Collaborative Innovation Center for Western China Traffic Safety and Intelligent Cooperative Control, Xi'an 710021, Shaanxi, China;
    3. The Joint Laboratory for Internet of Vehicles, Ministry of Education-China Mobile Communications Corporation, Chang'an University, Xi'an 710021, Shaanxi, China;
    4. Shandong Hi-Speed Information Group Co. Ltd., Jinan 250014, Shandong, China
  • Published:2022-12-23

CLC Number: 

  • TP391
[1] SCHRICK D V. Remarks on terminology in the field of supervision, fault detection and diagnosis[J]. IFAC Proceedings Volumes, 1997, 30(18): 959-964.
[2] ZHAO Ming, CHEN Jingchao. A review of methods for detecting point anomalies on numerical dataset[C] //2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference(ITNEC). Chong-qing, China: IEEE, 2020: 559-565.
[3] GAO Zhiwei, CECATI C, DING S X. A survey of fault diagnosis and fault-tolerant techniques: Part II: fault diagnosis with knowledge-based and hybrid/active approaches[J]. IEEE Transactions on Industrial Electronics, 2015, 62(6): 3768-3774.
[4] LUNZE J, RICHTER J H. Reconfigurable fault-tolerant control: a tutorial introduction[J]. European Journal of Control, 2008, 14(5): 359-386.
[5] SCHÖLKOPF B, WILLIAMSON R C, SMOLA A, et al. Support vector method for novelty detection[C] //Advances in Neural Information Processing Systems. Denver, USA: ACM, 1999: 582-588.
[6] ERFANI S M, RAJASEGARAR S, KARUNASEKERA S, et al. High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning[J]. Pattern Recognition, 2016: 121-134.
[7] MA J, PERKINS S. Time-series novelty detection using one-class support vector machines[C] //Proceedings of the International Joint Conference on Neural Networks. Portland, USA: IEEE, 2003: 1741-1745.
[8] BARBADO A, CORCHO Ó, BENJAMINS R. Rule extraction in unsupervised anomaly detection for model explainability: application to oneclass SVM[J]. Expert Systems with Applications, 2022, 189: 116100.
[9] 闵海根, 方煜坤, 吴霞, 等. 基于自动编码器和长短时记忆网络的智能汽车故障诊断方法研究[J]. 四川大学学报(自然科学版), 2021,58(5):79-87. MIN Haigen, FANG Yukun, WU Xia, et al. Fault diagnosis research for intelligent vehicles based on autoencoder and LSTM[J]. Journal of Sichuan University(Natural Science Edition), 2021, 58(5): 79-87.
[10] GONG Dong, LIU Lingqiao, LE Vuong, et al. Memorizing normality to detect anomaly: memory-augmented deep autoencoder for unsupervised anomaly detection[C] //2019 IEEE/CVF International Conference on Computer Vision(ICCV). Seoul, Korea: IEEE, 2020: 1705-1714.
[11] BREUNIG M M, KRIEGEL H P, NG R T, et al. LOF: identifying density-based local outliers[C] //SIGMOD'00: Proceedings of the 2000 ACM SIGMOD Intern-ational Conference on Management of Data. Dallas, USA: ACM, 2000: 93-104.
[12] 庄池杰, 张斌, 胡军, 等. 基于无监督学习的电力用户异常用电模式检测[J].中国电机工程学报, 2016, 36(2):379-387. ZHUANG Chijie, ZHANG Bin, HU Jun, et al. Anomaly detection for power consumption patterns based on unsupervised learning[J]. Proceedings of the CSEE, 2016, 36(2): 379-387.
[13] DING Nan, MA Haoxuan, GAO Huanbo, et al. Real-time anomaly detection based on long short-term memory and gaussian mixture model[J]. Computers & Electrical Engineering. 2019, 79: 106458.
[14] PROVOTAR O I, LINDER Y M, VERES M M. Unsupervised anomaly detection in time series using LSTM-based autoencoders[C] //2019 IEEE International Conference on Advanced Trends in Information Theory(ATIT). Kyiv, Ukraine: IEEE, 2019: 513-517.
[15] NANDURI A, SHERRY L. Anomaly detection in aircraft data using recurrent neural networks(RNN)[C] //Integrated Communications Navigation & Surveillance. Herndon, USA: IEEE, 2016: 5C2-1-5C2-8.
[16] FEI T L, KAI M T, ZHOU Z H. Isolation forest[C] //IEEE International Conference on Data Mining. Pisa, Italy: IEEE, 2008: 413-422.
[17] SHAHRAKI A, ABBASI M, HAUGEN Y. Boosting algorithms for network intrusion detection: a comparative evaluation of real adaboost, gentle AdaBoost and modest adaBoost[J]. Engineering Applications of Artificial Intelligence, 2020, 94: 103770.
[18] ZAHID Y, TAHIR M A, DURRANI M N. Ensemble learning using bagging and and inception-v3 for anomaly detection in surveillance videos[C] //2020 IEEE International Conference on Image Processing(ICIP). Abu Dhabi, United Arab Emirates: IEEE, 2020: 588-592.
[19] ZHONG Ying, CHEN Wenqi, WANG Zhiliang, et al. HELAD: a novel network anomaly detection model based on heterogeneous ensemble learning[J]. Computer Networks, 2019: 107049.
[20] FANG Yukun, MIN Haigen, WANG Wuqi, et al. A fault detection and diagnosis system for autonomous vehicles based on hybrid approaches[J]. IEEE Sensors Journal, 2020, 20(16): 9359-9371.
[21] 闵海根, 方煜坤, 吴霞, 等. 网联交通环境下的车-车通信故障诊断方法[J]. 山东大学学报(工学版), 2021,51(6):84-92. MIN Haigen, FANG Yukun, WU Xia, et al. Fault diagnosis of vehicle-to-vehicle communication in networked traffic environment[J]. Journal of Shandong University(Engineering Science), 2021, 51(6): 84-92.
[22] SAFAVI S, SAFAVI M A, HAMID H, et al. Multi-sensor fault detection, identification, isolation and health forecasting for autonomous vehicles[J]. Sensors, 2021, 21(7): 2547.
[23] PANG Guansong, SHEN Chunhua, CAO Longbin, et al. Deep learning for anomaly detection: a review[J]. ACM Computing Surveys, 2021, 54(2): 1-38.
[1] Xiaobin XU,Qi WANG,Bin GAO,Zhiyu SUN,Zhongjun LIANG,Shangguang WANG. Pre-allocation of resources based on trajectory prediction in heterogeneous networks [J]. Journal of Shandong University(Engineering Science), 2022, 52(4): 12-19.
[2] Yinfeng MENG,Qingfang LI. Recognition learning based on multivariate functional principal component representation [J]. Journal of Shandong University(Engineering Science), 2022, 52(3): 1-8.
[3] Xiushan NIE,Yuling MA,Huiyan QIAO,Jie GUO,Chaoran CUI,Zhiyun YU,Xingbo LIU,Yilong YIN. Survey on student academic performance prediction from the perspective of task granularity [J]. Journal of Shandong University(Engineering Science), 2022, 52(2): 1-14.
[4] Tongyu JIANG,Fan CHEN,Hongjie HE. Lightweight face super-resolution network based on asymmetric U-pyramid reconstruction [J]. Journal of Shandong University(Engineering Science), 2022, 52(1): 1-8, 18.
[5] Jun HU,Dongmei YANG,Li LIU,Fujin ZHONG. Cross social network user alignment via fusing node state information [J]. Journal of Shandong University(Engineering Science), 2021, 51(6): 49-58.
[6] Ye LIANG,Nan MA,Hongzhe LIU. Image-dependent fusion method for saliency maps [J]. Journal of Shandong University(Engineering Science), 2021, 51(4): 1-7.
[7] YANG Xiuyuan, PENG Tao, YANG Liang, LIN Hongfei. Adaptive multi-domain sentiment analysis based on knowledge distillation [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 15-21.
[8] FU Guixia, ZOU Guofeng, MAO Shuai, PAN Jinfeng, YIN Liju. Small sample person re-identification combining Gabor features and convolution features [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 22-29.
[9] TAO Liang, LIU Baoning, LIANG Wei. Automatic detection research of arrhythmia based on CNN-LSTM hybrid model [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 30-36.
[10] Xinlu ZONG,Jiayuan DU. Evacuation simulation model based on multi-target driven artificial bee colony algorithm [J]. Journal of Shandong University(Engineering Science), 2021, 51(3): 1-6.
[11] Junsan ZHANG,Qiaoqiao CHENG,Yao WAN,Jie ZHU,Shidong ZHANG. MIRGAN: a medical image report generation model based on GAN [J]. Journal of Shandong University(Engineering Science), 2021, 51(2): 9-18.
[12] Fengyu ZHOU,Panlong GU,Fang WAN,Lei YIN,Jiakai HE. Overview of multi-motion vision odometer [J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 1-10.
[13] WANG Mei, XUE Chenglong, ZHANG Qiang. Multi-kernel combination method based on rank spatial difference [J]. Journal of Shandong University(Engineering Science), 2021, 51(1): 108-113.
[14] Xiaolan XIE,Qi WANG. A scheduling algorithm based on multi-objective container cloud task [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 14-21.
[15] Guoyong CAI,Xinhao HE,Yangyang CHU. Visual sentiment analysis based on spatial attention mechanism and convolutional neural network [J]. Journal of Shandong University(Engineering Science), 2020, 50(4): 8-13.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!