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Optimal filter design with progressive genetic algorithm for local damage detection in rolling bearings

机译：滚动轴承局部损伤检测的渐进遗传算法优化滤波器设计

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摘要

Harsh industrial conditions present in underground mining cause a lot of difficulties for local damage detection in heavy-duty machinery. For vibration signals one of the most intuitive approaches of obtaining signal with expected properties, such as clearly visible informative features, is prefiltration with appropriately prepared filter. Design of such filter is very broad field of research on its own. In this paper authors propose a novel approach to dedicated optimal filter design using progressive genetic algorithm. Presented method is fully data-driven and requires no prior knowledge of the signal. It has been tested against a set of real and simulated data. Effectiveness of operation has been proven for both healthy and damaged case. Termination criterion for evolution process was developed, and diagnostic decision making feature has been proposed for final result determinance.
机译：地下采矿中存在的严酷工业条件为重型机械的局部损坏检测带来了许多困难。对于振动信号，获得具有预期属性（例如清晰可见的信息特征）的信号的最直观方法之一是使用适当准备的过滤器进行预过滤。这种滤波器的设计本身就是非常广阔的研究领域。在本文中，作者提出了一种使用渐进遗传算法的专用优化滤波器设计的新方法。提出的方法是完全由数据驱动的，不需要先验信号知识。已针对一组真实和模拟数据进行了测试。健康和受损病例均已证明手术的有效性。制定了进化过程的终止标准，并提出了诊断决策功能来确定最终结果。

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