反应堆冷却剂泵多源异类故障表征方法研究

徐仁义; 王岩; 匡成骁; 伍柯霖; 苏舒; 谭鑫

doi:10.13832/j.jnpe.2025.S1.0066

反应堆冷却剂泵多源异类故障表征方法研究

doi: 10.13832/j.jnpe.2025.S1.0066

徐仁义^1,,
王岩^{1, 2, ,},
匡成骁¹,
伍柯霖³,
苏舒¹,
谭鑫¹

1.
中国核动力研究设计院核反应堆技术全国重点实验室，成都，610213
2.
四川大学电子信息学院，成都，610065
3.
浙江大学能源工程学院，杭州，310027

详细信息

作者简介:
徐仁义（1993—），男，博士研究生，现主要从事核动力装置健康管理与故障预测工作，E-mail: 2684943100@qq.com

通讯作者:
王　岩，E-mail: wyxst@163.com

中图分类号: TL363；TH165⁺.3
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- 被引次数: 0
出版历程
- 收稿日期: 2024-08-30
- 修回日期: 2025-03-10
- 刊出日期: 2025-07-09

Research on Multi-source Heterogeneous Fault Characterization Method for Reactor Coolant Pump

Xu Renyi^1
,,
Wang Yan^{1, 2
, ,},
Kuang Chengxiao¹,
Wu Kelin³,
Su Shu¹,
Tan Xin¹

1.
National Key Laboratory of Nuclear Reactor Technology, Nuclear Power Institute of China, Chengdu, 610213, China
2.
College of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, China
3.
College of Energy Engineering, Zhejiang University, Hangzhou, 310027, China

摘要

摘要: 针对核电厂反应堆冷却剂泵（简称主泵）振动等高频传感信号调制、噪声干扰以及单传感器对故障诊断识别率低、证据缺乏的问题，本研究提出了一种基于循环平稳分析和D-S证据理论的主泵设备多源异类故障表征方法。通过使用时域分析和循环平稳分析对采集的高频传感数据进行处理，实现信号的解调和去噪，并计算特征参数，构建特征向量。在此基础上，基于D-S证据理论实现多源传感数据的融合，进而根据融合结果实现主泵设备典型故障的决策级诊断。试验验证结果表明，通过融合多源传感信息能够显著提高主泵设备典型故障的诊断识别率，并提高诊断结果的可解释性，相关研究成果能够为主泵设备的预测性维护提供参考依据，进而提升核电厂主泵设备的运行可靠性和智能化运维水平。
- 反应堆冷却剂泵 /
- 循环平稳分析 /
- D-S证据理论 /
- 故障检测与诊断
Abstract: Aiming at the problems of high-frequency sensing signal modulation, noise interference, and the low fault recognition rate and lack of evidence in single-sensor fault diagnosis for nuclear power plant reactor coolant pumps (RCPs), this paper proposes a method of multi-source heterogeneous fault characterization for RCPs based on cyclic stationary analysis and D-S evidence theory. By using time domain analysis and cyclic stationary analysis to process the high frequency sensor data, the signal demodulation and denoising are realized, and the feature parameters are calculated to construct the feature vector. And then, multi-source sensing data is fused based on D-S evidence theory, and the typical fault diagnosis of RCPs is realized at decision level according to the fusion results. The experimental verification results show that the fusion of multi-source sensing information can significantly improve the diagnosis rate of typical RCP faults, and improve the interpretability of diagnosis results. The relevant research results can provide a reference for the predictive maintenance of RCPs, and improve the operation reliability and intelligent operation and maintenance level of RCPs in nuclear power plants.
- Reactor coolant pump /
- Cyclic stationary analysis /
- D-S evidence theory /
- Fault detection and diagnosis

HTML全文

图 1 多源异类故障表征方法总体技术流程图

Figure 1. Technical Flow Chart of Multi-source Heterogeneous Fault Characterization Method

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图 2 试验平台

Figure 2. Test Platform

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图 3 振动加速度传感器布置位置图

Figure 3. Layout of Vibration Acceleration Sensors

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图 4 转子碰摩故障的声振信号的时域图和频域图

Figure 4. Time-domain Waveform and Amplitude Spectrum of Vibration and Noise Signal under Rotor Collision Fault

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图 5 转子碰摩故障的声振信号增强包络谱

Figure 5. Enhanced Envelope Spectrum of Vibration and Noise Signal under Rotor Rubbing Fault

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图 6 轴承损伤故障的声振信号时域图和频域图

Figure 6. Time-domain Waveform and Amplitude Spectrum of Vibration and Noise Signal under Bearing Damage Fault

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图 7 轴承损伤故障的声振信号增强包络谱

Figure 7. Enhanced Envelope Spectrum of Vibration and Noise Signal under Bearing Damage Fault

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图 8 故障类型判断准确率

Figure 8. Accuracy of Fault Type Determination

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图 9 异常鉴别正确率

Figure 9. Accuracy of Anomaly Identification

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表 1 试验数据集合

Table 1. Test Data Set

工况	样本数量
工况	转子碰摩F₁	轴承损伤F₂	密封泄漏F₃	无故障M	总体
工况1	800	800	800	800	3200
工况2	800	800	800	800	3200
工况3	800	800	800	800	3200
总和	2400	2400	2400	2400	9600

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表 2 故障频率幅值与轴频幅值的比值

Table 2. Ratio of Fault Frequency Amplitude to Shaft Frequency Amplitude

类型	频谱			增强包络谱
类型	轴频幅值	故障频率幅值	幅值比值	轴频幅值	故障频率幅值	幅值比值
振动信号	0.1486	0.0009	0.0061	0.3656	0.4415	1.2076
声信号	0.0389	0.0004	0.0103	0.1654	0.2012	1.2164

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表 3 传感器1的参考向量

Table 3. Reference Vector for Sensor 1

故障类型	A₁	A₂	A₃	A₄	A₅	A₆	A₇
F₁	1.525	71.42	0.2098	56.32	241.9	282.4	145.5
F₂	1.894	53.87	0.2943	31.99	252.8	291.8	145.9
F₃	1.259	11.20	0.0542	3.124	247.3	287.5	146.5
M	1.262	11.84	0.1769	3.096	247.1	287.4	146.6

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表 4 转子碰摩故障下的测试向量

Table 4. Test Vector under Rotor Rubbing Fault

传感器编号	B₁	B₂	B₃	B₄	B₅	B₆	B₇
1号	1.512	57.22	0.1750	40.15	243.0	283.4	145.8
2号	1.548	76.41	−0.3312	54.59	244.6	284.8	146.0
3号	1.481	49.24	−0.4147	31.82	239.8	281.2	147.0
4号	1.318	24.14	0.1672	6.518	236.9	278.9	147.1
5号	1.397	19.41	−0.1572	7.227	238.1	279.7	146.7
6号	1.378	32.79	−0.2377	11.71	240.3	281.6	146.9
7号	1.259	7.487	0.3033	3.331	240.3	283.3	149.9
8号	1.254	6.313	0.3844	3.103	240.3	283.3	149.9

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表 5 转子碰摩故障下所有传感器的BPA

Table 5. BPA for All Sensors under Rotor Rubbing Fault

证据	F₁	F₂	F₃	M
m₁	0.8135	0.0136	0.0788	0.0939
m₂	0.7644	0.0316	0.0930	0.1107
m₃	0.8801	0.0348	0.0401	0.0448
m₄	0.8767	0.0384	0.0366	0.0481
m₅	0.8422	0.0450	0.0445	0.0681
m₆	0.8321	0.0533	0.0517	0.0628
m₇	0.6491	0.1091	0.1521	0.0895
m₈	0.6179	0.1014	0.1885	0.0920

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表 6 转子碰摩故障下的证据融合结果

Table 6. Evidence Fusion Results under Rotor Rubbing Fault

融合证据	F₁	F₂	F₃	M
m₁₂	0.9814	0.0048	0.0064	0.0072
m₁₂₃	0.9984	0.0003	0.0005	0.0006
m₁₂₃₄	0.9998	0	0	0
m₁₂₃₄₅	0.9999	0	0	0
m₁₂₃₄₅₆	0.9999	0	0	0
m_1234567	0.9999	0	0	0
m_12345678	0.9999	0	0	0

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