| 基于多特征融合的电磁换向阀故障模式识别 |
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| 引用本文： | 马栋,刘志浩,高钦和,黄通.基于多特征融合的电磁换向阀故障模式识别[J].北京航空航天大学学报,2023,49(4):913-921. |
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| 作者姓名： | 马栋 刘志浩 高钦和 黄通 |
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| 作者单位： | 火箭军工程大学 兵器发射理论与技术国家重点学科实验室，西安 710025 |
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| 基金项目： | 国家自然科学基金(51905541)；陕西省自然科学基础研究计划(2020JQ487)；陕西省高校科协青年人才托举计划(20190412) |
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| 摘 要： | 为提高基于驱动端电流检测的电磁换向阀故障诊断方法的可靠性和识别准确度，开展了电磁换向阀故障模式识别方法研究。提出一种基于多特征融合的方法对电流信号时频分析和时域参数的特征值提取融合；通过设计电磁换向阀驱动端电流信号的采集实验，获取电磁换向阀驱动端电流的时域信号和二阶变化率的多特征曲线，提取时域参数及二阶变化率相应频带能量作为特征值，构建多特征融合的特征向量；采用基于径向基核函数的多分类支持向量机对电磁换向阀进行模式识别。结果表明：基于多特征融合的支持向量机较基于能量特征值的支持向量机可提升8.7%的识别精度和42.11%的验证准确率。
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| 关 键 词： | 电磁阀 故障诊断 电流检测 小波包分解 主元分析 多特征融合 C参数支持向量机 |
| 收稿时间： | 2021-07-01 |
Solenoid directional control valve fault pattern recognition based on multi-feature fusion |
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| Affiliation: | National Key Discipline Laboratory of Armament Launch Theory & Technology，Rocket Force University of Engineering，Xi’an 710025，China |
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| Abstract: | In order to further improve the reliability and recognition accuracy of the solenoid valve fault diagnosis method based on current detection at the drive end, a research was conducted on the solenoid valve fault pattern recognition method. First, a method for extracting eigenvalues based on time-frequency analysis of current signals and time-domain parameters was proposed; then, through designing an acquisition experiment of the current signal at the solenoid valve drive end, the time domain signal of the solenoid valve drive end current and the multi-characteristic curve of the second-order rate of change were obtained. Meanwhile, the time-domain parameters and the frequency band energy corresponding to the second-order rate of change were extracted as the characteristic value, in order to construct the feature vector of multi-feature fusion. Finally, a multi-class support vector machine based on the radial basis kernel function was used to identify the electromagnetic directional valve pattern. The research results showed that compared with the support vector machine based on energy eigenvalues, the support vector machine based on multi-feature fusion can improve the recognition accuracy by 8.7% and the verification accuracy by 42.11%. |
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