电子测量与仪器学报

谈恩民,沈彦飞.基于优化矩阵扰动分析的模拟电路故障诊断[J].电子测量与仪器学报,2024,38(5):90-97

基于优化矩阵扰动分析的模拟电路故障诊断

Fault diagnosis of analog circuits based on optimal matrix disturbance analysis

DOI：

英文关键词:matrix perturbation analog circuit fault diagnosis parameter identification

基金项目:中文基金项目国家自然科学基金（61741403）项目资助

作者	单位
谈恩民	桂林电子科技大学电子工程与自动化学院桂林541000
沈彦飞	桂林电子科技大学电子工程与自动化学院桂林541000

Author	Institution
Tan Enmin	School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China
Shen Yanfei	School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China

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中文摘要:

在现有的模拟电路故障诊断算法中，人工智能故障诊断算法训练数据量大、训练时间长，且难以实现参数辨识。传统电路分析方法所需测试点多，计算复杂。基于此，提出了一种基于优化矩阵扰动分析的模拟电路故障诊断算法。首先，采用拉普拉斯（Laplace）算子卷积被测电路的输出响应矩阵，从而增强矩阵元素与电路元件参数之间的扰动规律。其次，选取矩阵的迹和谱半径作为故障特征，并利用这种扰动规律建立矩阵模型。然后，利用改进的诊断算法，在Sallen_Key带通滤波器电路和跳蛙低通滤波器电路上进行实例验证。结果表明，所提方法在仅使用一个测点的情况下，可实现故障元件的参数辨识。其故障诊断率达100%，参数辨识误差控制在1%内，且计算时间控制在毫秒级别。因此该方法容易实现在线测试，且适用于要求高定位准确率、高精度参数辨识的场合。

英文摘要:

In the existing algorithm for fault diagnosis in analog circuits, artificial intelligence-based fault diagnosis algorithms require a large amount of training data and long training time, making it difficult to achieve parameter identification. Traditional circuit analysis methods require multiple test points and involve complex calculations. Based on this, a fault diagnosis algorithm for analog circuits based on optimized matrix perturbation analysis is proposed. Firstly, the Laplace operator is used to convolve the output response matrix of the tested circuit, thereby enhancing the perturbation pattern between matrix elements and circuit component parameters. Secondly, the trace and spectral radius of the matrix are selected as fault characteristics, and a matrix model is established using this perturbation pattern. Then, an improved diagnostic algorithm is used to verify examples in Sallen_Key bandpass filter circuits and leapfrog low-pass filter circuits. The results show that with only one test point, the proposed method can achieve parameter identification of faulty components. The fault diagnosis rate reaches 100%, with parameter identification error controlled within 1%, and computation time controlled at millisecond level. Therefore, this method is easy to implement for online testing and suitable for situations requiring high accuracy in fault localization and precise parameter identification.

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