Zhai Yongjie, Yang Xu, Peng Yani, Wang Xinying, Bai Kang
Department of Automation, North China Electricity Power University, Baoding 071003, China.
Department of Computer, North China Electricity Power University, Baoding 071003, China.
Entropy (Basel). 2020 Jun 19;22(6):685. doi: 10.3390/e22060685.
The equipment condition monitoring based on computer hearing is a new pattern recognition approach, and the system formed by it has the advantages of noncontact and strong early warning abilities. Extracting effective features from the sound data of the running power equipment help to improve the equipment monitoring accuracy. However, the sound of running equipment often has the characteristics of serious noise, non-linearity and instationary, which makes it difficult to extract features. To solve this problem, a feature extraction method based on the improved complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and multiscale improved permutation entropy (MIPE) is proposed. Firstly, the ICEEMDAN is utilized to obtain a group of intrinsic mode functions (IMFs) from the sound of running power equipment. The noise IMFs are then identified and eliminated through mutual information (MI) and mean mutual information (meanMI) of IMFs. Next, the normalized mutual information (norMI) and MIPE are calculated respectively, and norMI is utilized to weigh the corresponding MIPE result. Finally, based on the separability criterion, the weighted MIPE results are feature-dimensionally reduced to obtain the multiscale entropy feature of the sound. The experimental results show that the classification accuracies of the method under the conditions of no noise and 5 dB reach 96.7% and 89.9%, respectively. In practice, the proposed method has higher reliability and stability for the sound feature extraction of the running power equipment.
基于计算机听觉的设备状态监测是一种新型模式识别方法，由其构成的系统具有非接触和预警能力强的优点。从运行中的电力设备声音数据中提取有效特征有助于提高设备监测精度。然而，运行设备的声音往往具有噪声严重、非线性和非平稳等特点，这使得特征提取变得困难。为解决这一问题，提出了一种基于改进的自适应噪声互补总体经验模态分解（ICEEMDAN）和多尺度改进排列熵（MIPE）的特征提取方法。首先，利用ICEEMDAN从运行中的电力设备声音中获取一组本征模态函数（IMF）。然后通过IMF的互信息（MI）和平均互信息（meanMI）识别并消除噪声IMF。接下来，分别计算归一化互信息（norMI）和MIPE，并利用norMI对相应的MIPE结果进行加权。最后，基于可分性准则，对加权后的MIPE结果进行特征降维，得到声音的多尺度熵特征。实验结果表明，该方法在无噪声和5 dB条件下的分类准确率分别达到96.7%和89.9%。在实际应用中，该方法对运行中的电力设备声音特征提取具有较高的可靠性和稳定性。
