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基于多尺度数据融合的故障监测研究
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2009-06-17
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传统的同步数据融合算法不能在计算量和估计精确度上达到整体“优”，而现有的异步融合算法大都存在时间延迟以及计算量大等问题。本文针对传统融合算法研究存在的问题，致力于新的融合算法的设计，本文将(多尺度)数据融合理论与故障监控相结合，提出一种基于(多尺度)数据融合理论的故障监控策略。最后给出了具体应用实例。
关键词：数据融合；多尺度；故障监控
Abstract: The traditional data fusion algorithms based on synchronous data can’t get the
holistic ’Best’ in the computation and estimation accuracy. And the existing asynchronous fusion algorithms have been mostly several questions, for instance time delay, the large computation and so forth. According to the existing questions of traditional algorithms, we take up with the design of the new fusion algorithm. Combing Multi-scale data fusion theory with process monitoring, we present a new process monitoring method based on(Multi-scale)data fusion, and finally present a application example.
Key words：Data Fusion; Multi-scale; Fault Monitoring
关键词：数据融合；多尺度；故障监控
Abstract: The traditional data fusion algorithms based on synchronous data can’t get the
holistic ’Best’ in the computation and estimation accuracy. And the existing asynchronous fusion algorithms have been mostly several questions, for instance time delay, the large computation and so forth. According to the existing questions of traditional algorithms, we take up with the design of the new fusion algorithm. Combing Multi-scale data fusion theory with process monitoring, we present a new process monitoring method based on(Multi-scale)data fusion, and finally present a application example.
Key words：Data Fusion; Multi-scale; Fault Monitoring
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