| 李丽,宁穆怡,李志斌,曾昌健,张志艳,姚莉娜,孔汉.基于Fisher比与改进LSSVM算法的阀冷设备故障诊断研究[J].电子测量与仪器学报,2024,38(11):109-117 |
| 基于Fisher比与改进LSSVM算法的阀冷设备故障诊断研究 |
| Research on fault diagnosis of valve cooling equipment basedon Fisher ratio and improved LSSVM algorithm |
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| DOI： |
| 中文关键词: 阀冷设备 Fisher比 PSO-LSSVM 融合特征 故障诊断 |
| 英文关键词:valve cooling equipment Fisher ratio, PSO-LSSVM fusion feature fault diagnosis |
| 基金项目:河南省科技攻关项目(242102241030)资助 |
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| Author | Institution |
| Li Li | Quanzhou Electric Power Skills Research Institute of Fujian Electric Power Co., Ltd.，Quanzhou 362000,China |
| Ning Muyi | School of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002,China |
| Li Zhibin | China Guangdong Nuclear New Energy Henan Co.，Ltd.，Zhengzhou 450002, China |
| Zeng Changjian | Quanzhou Electric Power Skills Research Institute of Fujian Electric Power Co., Ltd.，Quanzhou 362000,China |
| Zhang Zhiyan | School of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002,China |
| Yao Lina | School of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002,China |
| Kong Han | School of Electric and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002,China |
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| 摘要点击次数: 389 |
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| 中文摘要: |
| 为提高换流站阀冷设备故障诊断的正确率和分类速度，提出了基于Fisher比准则的融合特征算法和粒子群优化最小二乘支持向量机的故障分类模型。首先，分别提取梅尔倒谱系数和逆梅尔倒谱系数的静态参数和动态一阶差分参数作为故障特征量，得到阀冷设备故障的高低频全部信息，然后利用Fisher比准则对阀冷设备故障特征进行两次融合，减少直接叠加信号带来的重复数据与干扰信号。特征信号经两次Fisher比判别后，筛选出1×13维Fisher比值数据作为阀冷设备噪声信号的融合特征量。其次，为了提高LSSVM算法故障识别的准确率和分类速度，利用PSO算法优化LSSVM算法的核函数带宽和惩罚因子，得到两个参数的最优解，建立LSSVM阀冷设备故障分类模型。最后，以阀冷设备间主泵为算例，分别采用不同特征融合算法和故障辨识方法进行对比分析，算例结果验证了所提出方法可以快速准确辨识阀冷设备在不同频率的故障信号,其故障辨识准确率可达96.67%。 |
| 英文摘要: |
| In order to improve the accuracy and classification speed of fault diagnosis of valve cooling equipment in converter station, a fusion feature algorithm based on Fisher ratio criterion and a fault classification model based on particle swarm optimization least squares support vector machine are proposed. Firstly, the static parameters and dynamic first-order difference parameters of Mel cepstrum coefficient and inverse Mel cepstrum coefficient are extracted as fault feature quantities respectively, and all the high and low frequency information of valve cooling equipment fault is obtained. Then, Fisher ratio criterion is used to fuse the fault features of valve cooling equipment twice, so as to reduce the repeated data and interference signal caused by direct superposition signal. The 1×13 dimensional Fisher ratio data is selected as the fusion feature of the noise signal of the valve cooling equipment. Secondly, in order to improve the accuracy and classification speed of LSSVM algorithm fault identification, the PSO algorithm is used to optimize the kernel function bandwidth and penalty factor of LSSVM algorithm, and the optimal solution of the two parameters is obtained, and the LSSVM valve cooling equipment fault classification model is established. Finally, the main pump between the valve cooling equipment is taken as an example, and different feature fusion algorithms and fault identification methods are used for comparative analysis. The results of the example verify that the proposed method can quickly and accurately identify the fault signals of the valve cooling equipment at different frequencies, and the accuracy of fault identification can reach 96.67%. |
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