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A model based approach to fault detection for the reverse path of cable television networks

机译:有线电视网络反向路径的基于模型的故障检测方法

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We present a model based method for reliably detecting faults in the reverse path of cable amplifier networks. This method has the advantage over traditional fixed-bound fault detection techniques in that it is able to accurately detect changes in signal behaviour while tracking signal changes due to environmental effects. The resulting method provides an increase in the fault detection sensitivity while simultaneously providing a decrease in the false alarm rate. We have implemented a general approach based an using a modeling engine to capture the reverse pilot signal behaviour of cable television amplifiers. Two modeling specific engines were developed for this purpose. The first one is based on the use of feedforward neural networks; the second one is based on the use of statistical analysis techniques. The resulting fault detection system, when employing either modeling engine, was able to provide good temporal localization of the onset of fault conditions along with a clear indication of the presence of the fault through its duration.
机译:我们提出了一种基于模型的方法,用于可靠地检测电缆放大器网络反向路径中的故障。与传统的固定边界故障检测技术相比,此方法的优势在于,它能够在跟踪由于环境影响而引起的信号变化的同时,准确地检测信号行为的变化。所得到的方法提高了故障检测的灵敏度,同时降低了误报率。我们已经实现了一种基于通用的方法,该方法使用建模引擎来捕获有线电视放大器的反向导频信号行为。为此,开发了两个建模专用引擎。第一个是基于前馈神经网络的使用。第二个是基于统计分析技术的使用。当采用任何一种建模引擎时,最终的故障检测系统都能够提供故障状况开始的良好时间定位,以及在整个故障期间内清晰显示故障的存在。

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