故障早期诊断::brain:基于PCA和多标签决策树的感应电动机多故障早期检测模型

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感应电动机的早期和极早期多标签故障诊断 马里奥Juez吉尔一个,胡安何塞·绍塞多-Dorantes B,阿尔瓦Arnaiz -冈萨雷斯,塞萨尔·伊格纳西奥·加西亚-奥索里奥一,卡洛斯·洛佩斯- Nozal一个,大卫·罗伊℃。 一个大学布尔戈斯,布尔戈斯,西班牙 b墨西哥克雷塔罗自治大学 c阿斯顿大学,英国伯明翰 抽象 机械故障的检测及其自动化诊断是工业优先考虑的问题,因为有效的故障诊断意味着有效管理维护时间,减少能耗,降低总体成本,最重要的是,确保了机械的可用性。 因此,本文提出了一种基于多传感器信息的新型智能多故障诊断方法,用于评估感应电动机中单个,组合和同时出现的故障情况。 所提出的方法的贡献和新颖性包括将不同的物理量(例如振动,定子电流,电压和转速)作为机器状态信息的有意义的来源加以考虑。 此外,对于每个可用的物理量,通过主成分分析减少的原始属性数量将导致保留的重要特征数量减少,从而可以通过多标签分类树实现最终的诊断结果。 该方法的有效性通过使用从实验室机电系统获得的完整实验数据集进行了验证,其中评估了健康情况和七个有故障的情况。 同样,结果的解释不需要任何现有的专业知识,并
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内容介绍
# Early and extremely early multi-label fault diagnosis in induction motors https://doi.org/10.1016/j.isatra.2020.07.002 Mario Juez-Gil <sup>a</sup>, Juan José Saucedo-Dorantes <sup>b</sup>, Álvar Arnaiz-González <sup>a</sup>, César Ignacio García-Osorio <sup>a</sup>, Carlos López-Nozal <sup>a</sup>, David Lowe <sup>c</sup>. <sup>a</sup> Universidad de Burgos, Burgos, Spain <sup>b</sup> Autonomous University of Queretaro, Mexico <sup>c</sup> Aston University, Birmingham, United Kingdom ## Abstract The detection of faulty machinery and its automated diagnosis is an industrial priority because efficient fault diagnosis implies efficient management of the maintenance times, reduction of energy consumption, reduction in overall costs and, most importantly, the availability of the machinery is ensured. Thus, this paper presents a new intelligent multi-fault diagnosis method based on multiple sensor information for assessing the occurrence of single, combined, and simultaneous faulty conditions in an induction motor. The contribution and novelty of the proposed method include the consideration of different physical magnitudes such as vibrations, stator currents, voltages, and rotational speed as a meaningful source of information of the machine condition. Moreover, for each available physical magnitude, the reduction of the original number of attributes through the Principal Component Analysis leads to retain a reduced number of significant features that allows achieving the final diagnosis outcome by a multi-label classification tree. The effectiveness of the method was validated by using a complete set of experimental data acquired from a laboratory electromechanical system, where a healthy and seven faulty scenarios were assessed. Also, the interpretation of the results do not require any prior expert knowledge and the robustness of this proposal allows its application in industrial applications, since it may deal with different operating conditions such as different loads and operating frequencies. Finally, the performance was evaluated using multi-label measures, which to the best of our knowledge, is an innovative development in the field condition monitoring and fault identification. ## Experiments The experiments are available in [this notebook](experiments.ipynb). ## Aknowlegments This work was supported through project TIN2015-67534-P (MINECO, Spain/FEDER, UE) of the Ministerio de Economía y Competitividad of the Spanish Government, project BU085P17 (JCyL/FEDER, UE) of the Consejería de Educación of the Junta de Castilla y León, Spain (both projects co-financed through European Union FEDER funds), and by the pre-doctoral grant (EDU/1100/2017), also of the Consejería de Educación of the Junta de Castilla y León, Spain and the European Social Fund. The authors gratefully acknowledge the support of NVIDIA Corporation, United States and its donation of the TITAN Xp GPUs used in this research. ## Citation policy Please cite this research as: ``` @ARTICLE{JUEZGIL2020, title = "Early and extremely early multi-label fault diagnosis in induction motors", author = "Mario Juez-Gil and Juan José Saucedo-Dorantes and Álvar Arnaiz-González and Carlos López-Nozal and César García-Osorio and David Lowe", journal = "ISA Transactions", year = "2020", volume = "", issn = "0019-0578", doi = "https://doi.org/10.1016/j.isatra.2020.07.002", url = "http://www.sciencedirect.com/science/article/pii/S0019057820302755", keywords = "Multi-fault detection, Early detection, Multi-label classification, Principal component analysis, Load insensitive model, Prediction at low operating frequencies", } ```
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