In industrial processes, early detection of anomalies is crucial for reducing process failures, meeting the quality assurance (QA) requirements, and lowering raw material wastage. Therefore, anomaly detection algorithms should identify an anomaly in a timely manner, and hence, allows immediate corrective actions to be applied. In this context, this paper proposes a low-complexity algorithm for detecting anomalies in industrial steelmaking furnaces operation. The algorithm utilizes the vibration measurements collected from several built-in sensors to compute the temporal correlation using the autocorrelation function (ACF). Furthermore, the proposed model parameters are tuned by solving multi-objective optimization using a genetic algorithm (GA). The proposed algorithm is tested using a practical dataset provided by an industrial steelmaking plant. The obtained results show that the proposed algorithm outperforms the support vector machine (SVM) and random forest (RF) algorithms in most key performance measures with the advantage of a substantial decrease in training and execution times.

中文翻译：

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基于自相关集成高斯的工业制造中基于感官数据的异常检测

在工业过程中，及早发现异常对于减少过程故障，满足质量保证（QA）要求并减少原材料浪费至关重要。因此，异常检测算法应及时识别异常，因此可以立即采取纠正措施。在此背景下，本文提出了一种用于检测工业炼钢炉运行异常的低复杂度算法。该算法利用从几个内置传感器收集的振动测量值，使用自相关函数（ACF）计算时间相关性。此外，通过使用遗传算法（GA）解决多目标优化问题，对提出的模型参数进行了优化。使用工业炼钢厂提供的实用数据集对提出的算法进行了测试。