Beretta Mattia, Julian Anatole, Sepulveda Jose, Cusidó Jordi, Porro Olga
Unitat Transversal de Gestió de l'Àmbit de Camins UTGAC, Universitat Politécnica de Catalunya, 08034 Barcelona, Spain.
SMARTIVE S.L., 08204 Sabadell, Spain.
Sensors (Basel). 2021 Feb 22;21(4):1512. doi: 10.3390/s21041512.
A novel and innovative solution addressing wind turbines' main bearing failure predictions using SCADA data is presented. This methodology enables to cut setup times and has more flexible requirements when compared to the current predictive algorithms. The proposed solution is entirely unsupervised as it does not require the labeling of data through work orders logs. Results of interpretable algorithms, which are tailored to capture specific aspects of main bearing failures, are merged into a combined health status indicator making use of Ensemble Learning principles. Based on multiple specialized indicators, the interpretability of the results is greater compared to black-box solutions that try to address the problem with a single complex algorithm. The proposed methodology has been tested on a dataset covering more than two year of operations from two onshore wind farms, counting a total of 84 turbines. All four main bearing failures are anticipated at least one month of time in advance. Combining individual indicators into a composed one proved effective with regard to all the tracked metrics. Accuracy of 95.1%, precision of 24.5% and F1 score of 38.5% are obtained averaging the values across the two windfarms. The encouraging results, the unsupervised nature and the flexibility and scalability of the proposed solution are appealing, making it particularly attractive for any online monitoring system used on single wind farms as well as entire wind turbine fleets.
提出了一种新颖且创新的解决方案，该方案利用SCADA数据解决风力涡轮机主轴承故障预测问题。与当前的预测算法相比，这种方法能够缩短设置时间，并且具有更灵活的要求。所提出的解决方案完全是无监督的，因为它不需要通过工单日志对数据进行标记。针对主轴承故障的特定方面量身定制的可解释算法的结果，利用集成学习原理合并到一个综合健康状态指标中。与试图用单一复杂算法解决该问题的黑箱解决方案相比，基于多个专业指标，结果的可解释性更强。所提出的方法已在一个数据集上进行了测试，该数据集涵盖了两个陆上风力发电场两年多的运行数据，共有84台涡轮机。所有四个主轴承故障都至少提前一个月被预测到。将各个指标组合成一个综合指标在所有跟踪指标方面都被证明是有效的。在两个风力发电场中对这些值进行平均，得到的准确率为95.1%，精确率为24.5%，F1分数为38.5%。所取得的令人鼓舞的结果、无监督的性质以及所提出解决方案的灵活性和可扩展性很有吸引力，使其对于单个风力发电场以及整个风力涡轮机机队所使用的任何在线监测系统都特别有吸引力。
