| 唐圣学,谭立强,李从宏,严金晶,Muhammad Ehtsham Akram,赵金泽.反激式开关电源故障非侵入式AI诊断方法研究[J].电子测量与仪器学报,2024,38(9):212-222 |
| 反激式开关电源故障非侵入式AI诊断方法研究 |
| Research on non-invasive AI diagnosis method for flybackswitching power supply faults |
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| DOI： |
| 中文关键词: 人工智能 反激式开关电源 时域特征 小波包奇异熵 故障诊断 DNN辨识 |
| 英文关键词:artificial intelligence flyback switching power supply time-domain feature wavelet packet singular entropy fault diagnosis DNN recognition |
| 基金项目:河北省自然科学基金（E2021202068）项目资助 |
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| Author | Institution |
| Tang Shengxue | State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical
Engineering, Hebei University of Technology, Tianjin 300401, China |
| Tan Liqiang | State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical
Engineering, Hebei University of Technology, Tianjin 300401, China |
| Li Conghong | Nanjing Vocational University
of Industry Technology, Nanjing 210023, China |
| Yan Jinjing | State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical
Engineering, Hebei University of Technology, Tianjin 300401, China |
| Muhammad Ehtsham Akram | State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical
Engineering, Hebei University of Technology, Tianjin 300401, China |
| Zhao Jinze | State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Electrical
Engineering, Hebei University of Technology, Tianjin 300401, China |
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| 中文摘要: |
| 将人工智能技术应用到故障诊断领域可以实现电力设备的自动化、智能化诊断，提高诊断精度和效率。以单输入多输出的反激式开关电源为例，针对其因脆弱元件失效而引起的电路工作性能异常的问题，通过分析不同故障模式的信号特性和可分性，提出了融合输入电流和输出电压信息的非侵入式开关电源故障诊断方法。构建了由时域特征及频带小波包奇异熵特征组成的融合时频域信息的多维特征矢量，建立了故障特征与故障模式之间的映射关系。进而，提出了基于人工智能技术的深度神经网络（DNN）故障诊断方法，实时监测反激式开关电源的运行状态，并通过数据分析及时识别故障位置，对潜在故障进行预警。实验结果表明,所提出的方法对单故障和多故障模式均具有良好的诊断效果，诊断准确率可达97.9%，并且，在不同工况下，该方法均可表现出较高的诊断准确率和较强的抗干扰性能。 |
| 英文摘要: |
| The application of artificial intelligence technology to the field of fault diagnosis can realize the automation and intelligent diagnosis of power equipment and improve diagnosis accuracy and efficiency. Taking the single-input multiple-output flyback switching power supply as an example, for the problem of abnormal circuit performance caused by the failure of its fragile components, a non-intrusive switching power supply fault diagnosis method fusing the input current and output voltage information is proposed by analyzing the signal characteristics and divisibility of different fault modes. A multidimensional feature vector fusing time-frequency domain information consisting of time-domain features and frequency-band wavelet packet singular entropy features is constructed, and the mapping relationship between fault features and fault modes is established. Then, a deep neural network (DNN) fault diagnosis method based on artificial intelligence technology is proposed to monitor the operation status of the flyback switching power supply in real time, identify the fault location in time through data analysis, and provide early warning for potential faults. The experimental results show that the method proposed in this paper has a good diagnostic effect on both single-fault and multi-fault modes, the diagnostic accuracy can reach 97.9%, and the method can show high diagnostic accuracy and strong anti-interference performance under different working conditions. |
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