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Abnormal Operation Detection in Heat Power Plant Using Ensemble of Binary Classifiers

机译：二元分类器集合在火电厂异常运行检测中的应用

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摘要

The problem of abnormal operation detection is considered for prediction of malfunctions appearance and their progress in the equipment of power plant. Abnormal operation detection method based on multivariate state estimation technique (MSET) along with machine learning algorithms is proposed. The ensemble of linear regression models is used for feature construction. The ensembles of binary classifiers (logistic regressions) together with the multilayer neural network are used for the abnormal operation index calculation based on the constructed features. The method was applied to abnormal operation detection in turbo feed pump (TFP 1100-350-17-4) at Kashirskaya heat power plant (Moscow region, Kashira). It is shown that the abnormal operation index of the pump starts to increase a few days before accidents appear and stays close to zero during the normal operation periods. The obtained results demonstrate that the developed model can be used to detect and predict operation anomalies in the power plant equipment.
机译：考虑异常操作检测的问题是为了预测故障的出现及其在电厂设备中的进展。提出了一种基于多元状态估计技术（MSET）的异常操作检测方法以及机器学习算法。线性回归模型的集合用于特征构建。基于构造的特征，将二元分类器的集合（逻辑回归）与多层神经网络一起用于异常操作指标计算。该方法已应用于Kashirskaya热电厂（莫斯科地区，喀什拉邦）的涡轮增压泵（TFP 1100-350-17-4）中的异常运行检测。结果表明，泵的异常运行指数在事故发生前几天开始增加，在正常运行期间保持接近于零。获得的结果表明，所开发的模型可用于检测和预测电厂设备中的运行异常。

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