基于统计特征的水轮机空化状态识别

Recognition of Cavitation States of Hydro Turbines Based on Statistical Features

中文关键词:统计特征  空化状态  极限梯度提升  支持向量级  循环神经网络

keywords:statistical features  cavitation state  gradient boosting  support vectorm achine  recurrent neural network

中文摘要:采用数字信号处理技术提取水轮机空化振动信号特征，研究了信号时域、频域、功率谱密度的不同统计特征，以检测水轮机空化状态。考虑了时域的极大值、极小值、峰峰值、均值、方差、均方值，频域的均方频率、重心频率、频率方差以及功率的谱密度峭度、偏度、裕度因子、波形因子等统计量。特征信号包括一阶、二阶导数，进一步增强特征提取。将特征量分别输入极限梯度提升（extreme gradient boosting，XGBoost）、循环神经网络（recurrent neural network，RNN）与支持向量机（support vector machine，SVM）模型对空化状态进行分类，分别计算模型准确率。计算结果表明，XGBoost的准确率最高，RNN次高，SVM相对较低。实验结果表明，通过统计量对水轮机空化状态的识别，可以反映机组的空化阶段，验证了所提算法的正确性，对实际工程具有一定的指导意义。

Abstract:Digital signal processing technology is adopted to extract the characteristics of cavitation vibration signals of hydraulic turbines. Different statistical features of the signals in the time domain， frequency domain， and power spectral density are studied to detect the cavitation state of hydraulic turbines. The considered statistical metrics include: in the time domain， maximum value， minimum value， peak-to-peak value， mean value， variance， and mean square value； in the frequency domain， mean square frequency， centroid frequency， and frequency variance； as well as in the power spectral density， kurtosis， skewness， margin factor， and waveform factor. The feature signals include the first-order and second-order derivatives， which further enhance feature extraction. These features are input into the Extreme Gradient Boosting （XGBoost）， Recurrent Neural Network （RNN）， and Support Vector Machine （SVM） models respectively for classifying the cavitation states， and the accuracy of each model is calculated. The calculation results show that XGBoost achieves the highest accuracy， followed by RNN， while SVM has a relatively lower accuracy. Experimental results indicate that the identification of the cavitation state of hydraulic turbines using the proposed statistical metrics can reflect the cavitation stage of the turbine unit， verify the correctness of the proposed algorithm， and provide certain guiding significance for practical engineering.