School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, People's Republic of China.
Rev Sci Instrum. 2021 Feb 1;92(2):025108. doi: 10.1063/5.0024611.
To use acoustic-emission technology to detect leaks inside valves, the necessary first step is to model the valve-internal-leakage acoustic-emission signal (VILAES) mathematically. A multi-variable classification model that relates the VILAES characteristics and the leakage rate under varying pressure is built by combining time-frequency domain characteristics and the random-forest method. A Butterworth bandpass filter is used to preprocess the VILAES from a liquid medium, and the best frequency band for filtering is determined as being 140 kHz-180 kHz. Then, (i) the standard deviation, (ii) root mean square, (iii) wavelet packet entropy, (iv) peak standard-deviation probability density, and (v) spectrum area are calculated as the VILAES characteristics, and six parameters-the pressure and the five VILAES characteristics-are used as the inputs for the random-forest classification model. Analysis shows that the five VILAES characteristics increase with an increase in the leakage rate. The multi-variable classification model is established by random forest to determine whether the valve leakage is small, medium, or large. The random forest uses many decision trees to predict the final result. For the same experimental data, the accuracy and operating time of the multi-variable classification model are compared with those of a support-vector-machine classification method for the bandpass and wavelet packet filtering preprocessing methods. The results show that the modeling method based on the combination of time-frequency characteristics and random forest has shorter operating time and higher accuracy.
为了利用声发射技术检测阀门内部的泄漏，首先必须对阀内泄漏声发射信号（VILAES）进行数学建模。通过结合时频域特征和随机森林方法，建立了一个多变量分类模型，该模型与不同压力下的 VILAES 特征和泄漏率相关。采用巴特沃斯带通滤波器对液体介质中的 VILAES 进行预处理，确定最佳滤波频段为 140 kHz-180 kHz。然后，计算 VILAES 的（i）标准差、（ii）均方根、（iii）小波包熵、（iv）峰标准差概率密度和（v）谱面积作为 VILAES 特征，将压力和五个 VILAES 特征作为随机森林分类模型的输入。分析表明，随着泄漏率的增加，五个 VILAES 特征都增加了。通过随机森林建立多变量分类模型来确定阀门泄漏是小、中还是大。随机森林使用多个决策树来预测最终结果。对于相同的实验数据，将基于时频特征和随机森林组合的多变量分类模型与带通和小波包滤波预处理方法的支持向量机分类方法的准确性和运行时间进行了比较。结果表明，基于时频特征和随机森林的组合的建模方法具有较短的运行时间和较高的准确性。
