Design and Development of Reactor Coolant Pump Intelligent Monitoring and Prognosis System for Nuclear Power Plants
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摘要: 为了提升核电厂设备的智能化运维水平，有效预防和减少设备停机，本研究针对反应堆冷却剂泵（简称主泵）设计开发了一套集数据采集与储存、状态监测、故障诊断、趋势预测、故障治理措施与防治决策支持等功能于一体的核电厂主泵智能监测与诊断系统。试验结果表明，该系统能够实时跟踪主泵运行状态，并在故障工况下对主泵异常信息进行及时检测与故障模式的准确识别，进而基于设备当前状态和参数趋势预测结果给出故障治理的措施与运维决策指导。因此，本系统能够跟踪并及时识别主泵的运行状态，达到提升核动力设备状态监测能力和智能化运维水平的目的。Abstract: In order to improve the intelligent operation and maintenance level of nuclear power plants, and effectively prevent and reduce equipment downtime, this paper designs and develops an intelligent monitoring and prognosis system for reactor coolant pumps (RCPs). The system integrates data acquisition and storage, online monitoring, fault diagnosis, trend prediction, fault treatment measures and prevention decision support. The verification results show that the system can track the running status of the RCP in real-time. Under fault conditions, the abnormal information of the RCP can be detected timely, and the fault mode is identified accurately. Then the O&M guidance is provided based on the current equipment status and parameter trend prediction results. Therefore, the system can track and identify the running state of reactor coolant pump in time to achieve the purpose of improving the condition monitoring capabilities and intelligent O&M level of nuclear power equipment.
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图 2 PMPS数据流和运行逻辑
TCP/IP—传输控制协议/因特网互联协议（网络通讯协议）；OPC/UA—开放平台通信统一架构。
Figure 2. Data Flow and Logic Diagram of PMPS
图 7 不同故障程度的内圈轴承故障信号分析结果
Figure 7. Analysis Results of Inner Ring Fault Bearing Signal with Different Fault Degrees
图 8 轴承故障信号的循环平稳分析结果
Figure 8. Cyclostationary Analysis Results of Bearing Fault Signals
表 1 PMPS软件模块开发表
Table 1. PMPS Software Module Development
序号 软件模块 开发工具/语言 功能 1 数据监测分发端 Visual Studio 2019/C# 数据的获取、转换、校验和分发等功能 2 智能监测与诊断模块（前端） Webstorm/HTML+JavaScript +CSS 人机界面的展示，数据的实时可视化 3 智能监测与诊断模块（后端） IDEA/Java 业务逻辑和计算的实现，包括算法调用计算、数据调用与存储等功能 4 智能监测与诊断模块算法端 Anaconda/Python 监测诊断算法的集成、运算等功能 5 MongoDB、MySQL数据库 实时和历史数据的存储 
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表 2 试验故障模拟方式及传感采集形式
Table 2. Simulation Methods of Test Faults and Forms of Sensor Data Acquisition
序号 故障模式 故障模拟方式 传感采集形式 1 转子动不平衡 在联轴器上加装螺钉，增加不平衡的质量 振动传感器和麦克风，其采样频率为25.6 kHz，采样时间为10 s 2 转子不对中 在电机基座下添加垫片，改变不同的垫高程度 3 转子碰磨 主泵口环侧面加装螺钉，通过旋紧螺钉将叶轮抵向口环，使两者发生接触 4 轴承故障 通过线切割的方式在轴承外圈制造划痕
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表 3 多源数据融合诊断结果
Table 3. Diagnostic Results Based on Multi-Source Data Fusion
融合证据 F1 F2 F3 F4 N m1&2 0 0.0006 0.4988 0.3243 0.1763 m1&2&3 0 0 0.8001 0.1740 0.0259 m1&2&3&4 0 0 0.9513 0.0416 0.0070 m1&2—1号和2号振动传感器的融合证据，其余同。
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