CLC number: TP277
On-line Access: 2022-12-14
Received: 2022-02-13
Revision Accepted: 2022-12-17
Crosschecked: 2022-05-09
Cited: 0
Clicked: 1277
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0002-7512-0168
https://orcid.org/0000-0002-0528-2778
Weijun WANG, Yun WANG, Jun WANG, Xinyun FANG, Yuchen HE. Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2200053 @article{title="Ensemble enhanced active learning mixture discriminant analysis model and its application for semi-supervised fault classification", %0 Journal Article TY - JOUR
集成增强主动学习混合判别分析模型及其在半监督故障分类中的应用1中国计量大学浙江省智能制造质量大数据溯源与应用重点实验室,中国杭州市,310018 2浙江同济科技职业学院机电工程学院,中国杭州市,311231 3苏州市计量测试院,中国苏州市,215004 摘要:故障分类作为过程监控中不可缺少的部分,其性能高度依赖于过程知识的充分性。然而,由于采样条件有限及实验室分析昂贵,数据标签总是难以获取,这可能导致分类性能下降。为了解决这个难题,本文提出一种新的半监督故障分类方法,其中每个未标记样本相对于特定标记数据集的价值采用增强的主动学习来评估。具有高价值的未标记样本将作为训练数据集的补充信息。此外,引入了几个合理的指标和准则大大降低了人工标注的干扰。最后,通过数值例子和田纳西伊士曼过程(TEP)评估了该方法的故障分类有效性。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Abellán J, Masegosa AR, 2010. Bagging decision trees on data sets with classification noise. Proc 6th Int Symp on Foundations of Information and Knowledge Systems, p.248-265. [2]Abramson N, Braverman D, Sebestyen G, 1963. Pattern recognition and machine learning. IEEE Trans Inform Theory, 9(4):257-261. [3]Araya DB, Grolinger K, ElYamany HF, et al., 2017. An ensemble learning framework for anomaly detection in building energy consumption. Energy Build, 144:191-206. [4]Blum A, Chawla S, 2001. Learning from labeled and unlabeled data using graph mincuts. Proc 18th Int Conf on Machine Learning, p.19-26. [5]Botre C, Mansouri M, Karim MN, et al., 2017. Multiscale PLS-based GLRT for fault detection of chemical processes. J Loss Prev Process Ind, 46:143-153. [6]Bouveyron C, Girard S, 2009. Robust supervised classification with mixture models: learning from data with uncertain labels. Patt Recogn, 42(11):2649-2658. [7]Chapelle O, Sindhwani V, Sathiya Keerthi S, 2006. Branch and bound for semi-supervised support vector machines. Proc 19th Int Conf on Neural Information Processing Systems, p.217-224. [8]Chen X, Wang ZP, Zhang Z, et al., 2018. A semi-supervised approach to bearing fault diagnosis under variable conditions towards imbalanced unlabeled data. Sensors, 18(7):2097. [9]Chiang LH, Russell EL, Braatz RD, 2000. Fault diagnosis in chemical processes using Fisher discriminant analysis, discriminant partial least squares, and principal component analysis. Chemom Intell Lab Syst, 50(2):243-252. [10]Chiang LH, Kotanchek ME, Kordon AK, 2004. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Comput Chem Eng, 28(8):1389-1401. [11]Cui XD, Huang J, Chien JT, 2012. Multi-view and multi-objective semi-supervised learning for HMM-based automatic speech recognition. IEEE Trans Audio Speech Lang Process, 20(7):1923-1935. [12]Deng XG, Liu XY, Cao YP, et al., 2022. Incipient fault detection for dynamic chemical processes based on enhanced CVDA integrated with probability information and fault-sensitive features. J Process Contr, 114:29-41. [13]Dietterich TG, 2000. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Mach Learn, 40(2):139-157. [14]Dong YN, Qin SJ, 2018. A novel dynamic PCA algorithm for dynamic data modeling and process monitoring. J Process Contr, 67:1-11. [15]Downs JJ, Vogel EF, 1993. A plant-wide industrial process control problem. Comput Chem Eng, 17(3):245-255. [16]Farajzadeh-Zanjani M, Hallaji E, Razavi-Far R, et al., 2021. Adversarial semi-supervised learning for diagnosing faults and attacks in power grids. IEEE Trans Smart Grid, 12(4):3468-3478. [17]Feng J, Wang J, Han ZY, 2013. Process monitoring for chemical process based on semi-supervised principal component analysis. Proc 25th Chinese Control and Decision Conf, p.4282-4286. [18]Fraley C, Raftery AE, 2002. Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc, 97(458):611-631. [19]Ge ZQ, 2016. Supervised latent factor analysis for process data regression modeling and soft sensor application. IEEE Trans Contr Syst Technol, 24(3):1004-1011. [20]Ge ZQ, 2017. Review on data-driven modeling and monitoring for plant-wide industrial processes. Chemom Intell Lab Syst, 171:16-25. [21]Ge ZQ, 2018. Process data analytics via probabilistic latent variable models: a tutorial review. Ind Eng Chem Res, 57(38):12646-12661. [22]Ge ZQ, Song ZH, Gao FR, 2013. Review of recent research on data-based process monitoring. Ind Eng Chem Res, 52(10):3543-3562. [23]Ge ZQ, Song ZH, Ding SX, et al., 2017. Data mining and analytics in the process industry: the role of machine learning. IEEE Access, 5:20590-20616. [24]Hady MFA, Schwenker F, 2010. Combining committee-based semi-supervised learning and active learning. J Comput Sci Technol, 25(4):681-698. [25]Harkat MF, Mansouri M, Nounou MN, et al., 2019. Fault detection of uncertain chemical processes using interval partial least squares-based generalized likelihood ratio test. Inform Sci, 490:265-284. [26]Hastie T, Tibshirani R, 1996. Discriminant analysis by Gaussian mixtures. J Roy Stat Soc Ser B, 58(1):155-176. [27]He YL, Li K, Zhang N, et al., 2021. Fault diagnosis using improved discrimination locality preserving projections integrated with sparse autoencoder. IEEE Trans Instrum Meas, 70:3527108. [28]Huang CC, Chen T, Yao Y, 2013. Mixture discriminant monitoring: a hybrid method for statistical process monitoring and fault diagnosis/isolation. Ind Eng Chem Res, 52(31):10720-10731. [29]Ipeirotis PG, Provost F, Wang J, 2010. Quality management on Amazon Mechanical Turk. Proc ACM SIGKDD Workshop on Human Computation, p.64-67. [30]Jin YR, Qin CJ, Huang YX, et al., 2021. Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network. Measurement, 173:108500. [31]Kalantar B, Al-Najjar HAH, Pradhan B, et al., 2019. Optimized conditioning factors using machine learning techniques for groundwater potential mapping. Water, 11(9):1909. [32]Liu J, Song CY, Zhao J, 2018. Active learning based semi-supervised exponential discriminant analysis and its application for fault classification in industrial processes. Chemom Intell Lab Syst, 180:42-53. [33]Liu J, Song CY, Zhao J, et al., 2020. Manifold-preserving sparse graph-based ensemble FDA for industrial label-noise fault classification. IEEE Trans Instrum Meas, 69(6):2621-2634. [34]Liu JW, Liu Y, Luo XL, 2015. Semi-supervised learning methods. Chin J Comput, 38(8):1592-1617 (in Chinese). [35]Liu Y, Ge ZQ, 2018. Weighted random forests for fault classification in industrial processes with hierarchical clustering model selection. J Process Contr, 64:62-70. [36]MacGregor J, Cinar A, 2012. Monitoring, fault diagnosis, fault-tolerant control and optimization: data driven methods. Comput Chem Eng, 47:111-120. [37]Pu XK, Li CG, 2021. Probabilistic information-theoretic discriminant analysis for industrial label-noise fault diagnosis. IEEE Trans Ind Inform, 17(4):2664-2674. [38]Raina R, Battle A, Lee H, et al., 2007. Self-taught learning: transfer learning from unlabeled data. Proc 24th Int Conf on Machine Learning, p.759-766. [39]Raykar VC, Yu SP, Zhao LH, et al., 2010. Learning from crowds. J Mach Learn Res, 11:1297-1322. [40]Schwenker F, Trentin E, 2014. Pattern classification and clustering: a review of partially supervised learning approaches. Patt Recogn Lett, 37:4-14. [41]Settles B, 2012. Active Learning. Morgan & Claypool Publishers, USA. [42]Shao WM, Tian XM, 2017. Semi-supervised selective ensemble learning based on distance to model for nonlinear soft sensor development. Neurocomputing, 222:91-104. [43]Shao WM, Ge ZQ, Song ZH, 2019a. Semi-supervised mixture of latent factor analysis models with application to online key variable estimation. Contr Eng Pract, 84:32- 47. [44]Shao WM, Ge ZQ, Song ZH, et al., 2019b. Nonlinear industrial soft sensor development based on semi-supervised probabilistic mixture of extreme learning machines. Contr Eng Pract, 91:104098. [45]Snow R, O’Connor B, Jurafsky D, et al., 2008. Cheap and fast—but is it good? Evaluating non-expert annotations for natural language tasks. Proc Conf on Empirical Methods in Natural Language Processing, p.254-263. [46]Wang J, Feng J, Han ZY, 2014. Fault detection for the class imbalance problem in semiconductor manufacturing processes. J Circ Syst Comput, 23(4):1450049. [47]Wang JB, Shao WM, Song ZH, 2019. Semi-supervised variational Bayesian student's t mixture regression and robust inferential sensor application. Contr Eng Pract, 92:104155. [48]Wang L, Tian H, Zhang H, 2021. Soft fault diagnosis of analog circuits based on semi-supervised support vector machine. Analog Integr Circ Signal Process, 108(2):305-315. [49]Yan ZB, Huang CC, Yao Y, 2014. Semi-supervised mixture discriminant monitoring for chemical batch processes. Chemom Intell Lab Syst, 134:10-22. [50]Yao L, Ge ZQ, 2017. Locally weighted prediction methods for latent factor analysis with supervised and semisupervised process data. IEEE Trans Autom Sci Eng, 14(1):126-138. [51]Yin LL, Wang HG, Fan WH, et al., 2018. Combining active learning and Fisher discriminant analysis for the semi-supervised process monitoring. IFAC-PapersOnLine, 51(21):147-151. [52]Yin LL, Wang HG, Fan WH, et al., 2019. Incorporate active learning to semi-supervised industrial fault classification. J Process Contr, 78:88-97. [53]Yuen MC, King I, Leung KS, 2011. A survey of crowdsourcing systems. Proc IEEE 3rd Int Conf on Privacy, Security, Risk and Trust and IEEE 3rd Int Conf on Social Computing, p.766-773. [54]Zaman SMK, Liang XD, 2021. An effective induction motor fault diagnosis approach using graph-based semi-supervised learning. IEEE Access, 9:7471-7482. [55]Zhang N, Xu Y, Zhu QX, et al., 2022. Improved locality preserving projections based on heat-kernel and cosine weights for fault classification in complex industrial processes. IEEE Trans Reliab, early access. [56]Zheng JH, Wang HJ, Song ZH, et al., 2019. Ensemble semi-supervised Fisher discriminant analysis model for fault classification in industrial processes. ISA Trans, 92:109-117. [57]Zheng JH, Zhu JL, Chen GJ, et al., 2020. Dynamic Bayesian network for robust latent variable modeling and fault classification. Eng Appl Artif Intell, 89:103475. [58]Zhong K, Han M, Qiu T, et al., 2020. Fault diagnosis of complex processes using sparse kernel local Fisher discriminant analysis. IEEE Trans Neur Netw Learn Syst, 31(5):1581-1591. [59]Zou Y, Yu ZD, Liu XF, et al., 2019. Confidence regularized self-training. 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