| 孙志刚,王国涛,高萌萌,郜雷阵,蒋爱平.参数优化支持向量机的密封电子设备
多余物定位方法研究[J].电子测量与仪器学报,2021,35(8):162-174 |
| 参数优化支持向量机的密封电子设备
多余物定位方法研究 |
| Research on localization method of loose particles inside sealed electronicequipment based on parameter-optimized support vector machine |
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| DOI： |
| 中文关键词: 多余物定位实验系统 脉冲提取 脉冲匹配 时频域特征 支持向量机 参数优化 |
| 英文关键词:loose particle localization experimental system pulse extraction pulse matching time domain and frequency domain
features support vector machine parameter optimization |
| 基金项目:国家自然科学基金（51607059）、黑龙江省自然科学基金（QC2017059，JJ2020LH1310）、黑龙江省博士后基金（LBH Z16169）、黑龙江省高校基本科研业务费（HDRCCX 201604，2020 KYYWF 1006）、黑龙江省教育厅科技成果培育（TSTAU C2018016）、黑龙江大学研究生创新科研项目（YJSCX2021 067HLJU）资助 |
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| 中文摘要: |
| 在密封电子设备的生产制造过程中,对多余物进行检测和定位至关重要。 针对设备体积大和多余物位置难以确定的问
题,使用参数优化支持向量机对设备内部的多余物进行定位。 通过设计信号调理电路与多通道信号同步采集电路,调理和采集
微弱的多余物信号,设计两级双门限脉冲提取算法和多通道脉冲匹配算法对信号进行预处理,得到有效的信号数据。 提取和选
择性能优良的时频域特征构建定位数据集,比较不同分类算法在数据集上的性能表现,对更优的支持向量机进行参数优化设
计,将优化后的支持向量机定位模型用于实物测试。 测试结果表明,参数优化支持向量机的定位模型在航天电源内部的多余物
定位测试的平均精度达 82. 58%,定位模型的泛化能力良好,达到航天系统工程的精度要求,该方法理论上可以推广应用于类似
产生机理的碰撞信号定位。 |
| 英文摘要: |
| In the manufacturing process of sealed electronic equipment, it is very important to detect and locate loose particles. Aiming at
the problem of the large size of the equipment and the difficulty of determining the location of loose particles, parameter optimization
Support Vector machines is used to locate the loose particle inside equipment. By designing a signal conditioning circuit and a multichannel signal synchronization acquisition circuit, the weak loose particle signal is processed and collected. By designing a two-stage
dual-threshold pulse extraction algorithm and a multi-channel pulse matching algorithm, the signals are preprocessed to obtain effective
signal data. By extracting and selecting the time domain and frequency domain features with excellent performance, to construct a
locating data set. Comparing the performance of different classification algorithms on the data set, optimizing the inherent parameters of
better-performed support vector machine. And finally using the optimized support vector machine locating model for physical testing.
The test results show that the optimized support vector machine locating model has an average accuracy of 82. 58% in the loose particle
locating test inside the aerospace power supply. The generalization ability of the locating model is good and meets the accuracy
requirements of aerospace system engineering. Theoretically, this method can be extended to the research on the location of collision
signals with similar generation mechanism. |
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