Artificial neural network and fuzzy logic based boiler tube leak detection systems
    1.
    发明授权
    Artificial neural network and fuzzy logic based boiler tube leak detection systems 失效
    人工神经网络和基于模糊逻辑的锅炉管道泄漏检测系统

    公开(公告)号:US06567795B2

    公开(公告)日:2003-05-20

    申请号:US09726516

    申请日:2000-12-01

    IPC分类号: G06F1518

    摘要: Power industry boiler tube failures are a major cause of utility forced outages in the United States, with approximately 41,000 tube failures occurring every year at a cost of $5 billion a year. Accordingly, early tube leak detection and isolation is highly desirable. Early detection allows scheduling of a repair rather than suffering a forced outage, and significantly increases the chance of preventing damage to adjacent tubes. The instant detection scheme starts with identification of boiler tube leak process variables which are divided into universal sensitive variables, local leak sensitive variables, group leak sensitive variables, and subgroup leak sensitive variables, and which may be automatically be obtained using a data driven approach and a leak sensitivity function. One embodiment uses artificial neural networks (ANN) to learn the map between appropriate leak sensitive variables and the leak behavior. The second design philosophy integrates ANNs with approximate reasoning using fuzzy logic and fuzzy sets. In the second design, ANNs are used for learning, while approximate reasoning and inference engines are used for decision making. Advantages include use of already monitored process variables, no additional hardware and/or maintenance requirements, systematic processing does not require an expert system and/or a skilled operator, and the systems are portable and can be easily tailored for use on a variety of different boilers.

    摘要翻译: 电力行业锅炉管故障是美国实行有效停电的主要原因,每年出现约41,000个管道故障,每年耗资50亿美元。 因此,早期的管泄漏检测和隔离是非常需要的。 早期检测允许安排修理而不是遭受强制中断,并显着增加防止相邻管损坏的机会。 即时检测方案开始于锅炉管泄漏过程变量的识别,分为通用敏感变量,局部泄漏敏感变量,组泄漏敏感变量和子组泄漏敏感变量,并且可以使用数据驱动方法自动获得, 泄漏灵敏度功能。 一个实施例使用人工神经网络(ANN)来学习适当的泄漏敏感变量与泄漏行为之间的映射。 第二种设计理念将ANN与使用模糊逻辑和模糊集的近似推理相结合。 在第二种设计中,ANN用于学习,而近似推理和推理引擎则用于决策。 优点包括使用已经监控的过程变量,无需额外的硬件和/或维护要求,系统处理不需要专家系统和/或技术熟练的操作员,并且系统是便携式的,并且可以容易地定制以用于各种不同的 锅炉