Data-driven condition monitoring is an essential function for power plant because of its potential to enhance asset longevity and reduce the operation and maintenance costs. This article explains the complicated relationship in multiplex power plant data as a mixture of temporal dependency and cross-variable association and proposes a composite anomaly detection system that incorporates the two data relationships on a probabilistic basis for more reliable power plant condition monitoring. It is able to dynamically capture the most significant relationship to develop more reliable normal condition interval, based on which the potential faults can be timely detected and the abnormal variable can be accurately identified. The proposed system was tested on a realistic thermal power plant. The testing results demonstrate its reliable condition monitoring and accurate anomaly detection performance, which necessitates the composite modeling of temporal dependency and cross-variable association in data-driven power plant condition monitoring.

中文翻译：

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用于数据驱动发电厂状态监测的复合异常检测系统


数据驱动的状态监测是发电厂的一项重要功能，因为它有可能延长资产寿命并降低运营和维护成本。本文将多发电厂数据中的复杂关系解释为时间依赖性和跨变量关联的混合，并提出了一种复合异常检测系统，该系统在概率基础上合并这两种数据关系，以实现更可靠的发电厂状态监测。它能够动态捕捉最显着的关系，制定更可靠的正常条件区间，并据此及时发现潜在故障，准确识别异常变量。所提出的系统在真实的火力发电厂上进行了测试。测试结果证明了其可靠的状态监测和准确的异常检测性能，这需要在数据驱动的电厂状态监测中进行时间依赖性和跨变量关联的复合建模。