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首页> 外文会议>2019 IEEE International Symposium on INnovations in Intelligent SysTems and Applications >Anomaly Detection in Aluminium Production with Unsupervised Machine Learning Classifiers
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Anomaly Detection in Aluminium Production with Unsupervised Machine Learning Classifiers

机译：无监督机器学习分类器在铝生产中的异常检测

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摘要

This work presents a predictive maintenance methodology aiming at forecasting specific types of faults of an industrial equipment for anode production, utilizing process sensor data from operation periods. The challenge of this problem is the early detection of a fault, particularly just before it occurs. For the forecasting, some unsupervised machine learning architectures were tested. Several considerations were made for the pre-processing steps as well. Finally, automatic feature selection methods were introduced, one of which was used to find the most significant features within successive time windows of the evaluated historical data set. The experimental results, which conform to the visual observations, show that a warning time frame around 20 minutes before the incident is feasible for 43% of the incidents of a particular fault type within a critical 1.5-month period, whereas only in about 0.1% of the timestamps more than 75 minutes before such a fault an alarm is raised.
机译：这项工作提出了一种预测性维护方法，旨在利用运行期间的过程传感器数据来预测用于阳极生产的工业设备的特定类型的故障。该问题的挑战在于及早发现故障，尤其是在发生故障之前。为了进行预测，测试了一些无监督的机器学习架构。还对预处理步骤进行了一些考虑。最后，介绍了自动特征选择方法，其中一种方法用于在所评估的历史数据集的连续时间窗口内找到最重要的特征。与视觉观察结果相符的实验结果表明，在关键的1.5个月内，对于特定故障类型的43％的事件，在事件发生前20分钟左右的警告时间是可行的，而在大约0.1％的时间范围内在此类故障之前超过75分钟的时间戳中，会发出警报。

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