| 何冬,黄国勇,钱恩丽,李锶宇.基于总变差降噪与RQA的单向阀故障诊断[J].电子测量与仪器学报,2021,35(2):65-72 |
| 基于总变差降噪与RQA的单向阀故障诊断 |
| Check valve fault diagnosis based on total variation de nosing and recurrence quantification analysis |
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| DOI： |
| 中文关键词: 总变差降噪 递归定量分析 加权K近邻分类器 单向阀 故障诊断 |
| 英文关键词:total variation denoising recurrence quantification analysis weighted K nearest neighbor classifier check valve fault diagnosis |
| 基金项目:国家自然科学基金（61663017）项目资助 |
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| Author | Institution |
| He Dong | 1Faculty of Information Engineering & Automation, Kunming University of Science and Technology, Kunming 650500, China |
| Huang Guoyong | 1Faculty of Information Engineering & Automation, Kunming University of Science and Technology, Kunming 650500, China;2Faculty of Civil Aviation & Aeronautics, Kunming University of Science and Technology, Kunming 650500, China |
| Qian Enli | 1Faculty of Information Engineering & Automation, Kunming University of Science and Technology, Kunming 650500, China |
| Li Siyu | 1Faculty of Information Engineering & Automation, Kunming University of Science and Technology, Kunming 650500, China |
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| 中文摘要: |
| 针对单向阀振动信号易被噪声淹没和故障表征不明显的问题，提出了一种基于总变差降噪(TVD)和递归定量分析(RQA)的单向阀故障诊断方法。首先利用总变差降噪方法对振动信号进行降噪，提高信号的信噪比；然后对降噪后的信号绘制递归图，通过递归定量分析方法提取递归图中的非线性特征参数，并对所提取特征参数进行敏感度分析，找出敏感度较高的特征参数构成特征向量；最后将得到的特征向量输入加权K近邻分类器(WKNN)完成单向阀故障类型的识别。实验结果表明，该方法在降低背景噪声、表征故障信息和保证故障诊断准确性方面具有明显的效果，具有一定的工程应用价值。 |
| 英文摘要: |
| Aiming at the problem that the vibration signal of the check valve is easily overwhelmed by noise and the fault characterization is not obvious. A fault diagnosis method for the check valve based on the total variation denoising and recurrence quantification analysis (TVD RQA) was proposed. Firstly, the total variation de noising method was used to de noise vibration signals and improve their signal to noise ratios; Then, draw a recurrence plot on the denoised signal, extract the nonlinear characteristic parameters in the recurrence plot through the recurrence quantification analysis method, and perform sensitivity analysis on the extracted feature parameters to find out the feature vectors with higher sensitivity; Finally, the obtained feature vector is input into the weighted K nearest neighbor classifier (WKNN) to complete the identification of the check valve failure type. Experimental results show that the method has obvious effects in reduce background noise, digging fault information, and ensuring the accuracy of fault diagnosis, and has certain engineering application value. |
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