In industrial electro galvanizing lines aged anodes deteriorate zinc coating distribution over the strip width, leading to an increase in electricity and zinc cost. We introduce a data-driven approach in predictive maintenance of anodes to replace the cost- and labor-intensive manual inspection, which is still common for this task. The approach is based on parasitic resistance as an indicator of anode condition which might be aged or mis-installed. The parasitic resistance is indirectly observable via the voltage difference between the measured and baseline (theoretical) voltage for healthy anode. Here we calculate the baseline voltage by means of two approaches: (1) a physical model based on electrical and electrochemical laws, and (2) advanced machine learning techniques including boosting and bagging regression. The data was collected on one exemplary rectifier unit equipped with two anodes being studied for a total period of two years. The dataset consists of one target variable (rectifier voltage) and nine predictive variables used in the models, observing electrical current, electrolyte, and steel strip characteristics. For predictive modelling, we used Random Forest, Partial Least Squares and AdaBoost Regression. The model training was conducted on intervals where the anodes were in good condition and validated on other segments which served as a proof of concept that bad anode conditions can be identified using the parasitic resistance predicted by our models. Our results show a RMSE of 0.24 V for baseline rectifier voltage with a mean ± standard deviation of 11.32 ± 2.53 V for the best model on the validation set. The best-performing model is a hybrid version of a Random Forest which incorporates meta-variables computed from the physical model. We found that a large predicted parasitic resistance coincides well with the results of the manual inspection. The results of this work will be implemented in online monitoring of anode conditions to reduce operational cost at a production site.

中文翻译：

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寄生电阻可预测电镀锌中阳极的故障：机器学习，物理模型和混合模型的比较

在工业电镀锌生产线上，老化的阳极会使带钢宽度上的锌涂层分布变差，导致电力和锌成本增加。我们在阳极的预测性维护中引入了一种数据驱动的方法，以取代成本高昂且劳动强度大的手动检查，这在该任务中仍然很常见。该方法基于寄生电阻，作为可能老化或安装错误的阳极状况的指标。寄生电阻可通过健康阳极的实测电压与基准（理论）电压之间的电压差间接观察到。在这里，我们通过两种方法来计算基线电压：（1）基于电气和电化学定律的物理模型，以及（2）包括升压和装袋回归的高级机器学习技术。在装备有两个阳极的一个示例性整流器单元上收集了数据，研究了总共两年的时间。该数据集由一个目标变量（整流器电压）和模型中使用的九个预测变量组成，用于观察电流，电解质和钢带特性。对于预测建模，我们使用了随机森林，偏最小二乘和AdaBoost回归。在阳极处于良好状态的时间间隔上进行模型训练，并在其他部分进行了验证，这些概念证明了可以使用我们的模型预测的寄生电阻来识别不良阳极条件。我们的结果显示，基准整流器电压的RMSE为0.24 V，对于验证集上的最佳模型，均值±标准偏差为11.32±2.53V。表现最佳的模型是随机森林的混合版本，其中包含从物理模型计算得出的元变量。我们发现，较大的预测寄生电阻与手动检查的结果非常吻合。这项工作的结果将在阳极状况在线监测中实现，以降低生产现场的运营成本。