Simplified machine diagnosis techniques using AR model of absolute deterioration factor with weight
Simplified machine diagnosis techniques using AR model of absolute deterioration factor with weight
复制标题
使用绝对劣化因子与权重的 AR 模型简化机器诊断技术
DOI：
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发表时间：
2009
期刊：
影响因子：
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通讯作者：
Y. lshii
中科院分区：
文献类型：
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作者：
K. Takeyasu;Y. lshii
在钢铁制造等拥有大型设备的大规模生产行业中，由于机器故障而导致的生产过程突然停止可能会导致严重的问题。为了防止这种情况，机器诊断技术发挥着重要作用。已经开发了许多方法来关注这个主题。旋转机械故障的早期检测是机械故障检测领域的一个重要课题，本文提出了一种旋转机械故障的早期检测方法。介绍了一种计算自相关函数的简化方法，并将其用于阿尔马模型辨识。此外，一个绝对恶化因子，如双相干。这种带权系统参数距离的简化计算方法可用于机械故障诊断。数值算例表明，该方法是一种实用的机械故障诊断指标。
In mass production industries such as steel making that have large equipment, sudden stops of production process due to machine failure can cause severe problems. To prevent such situations, machine diagnosis techniques play important roles. Many methods have been developed focusing on this subject. In this paper, we propose a method for the early detection of the failure on rotating machine, which is the most common theme in the machine failure detection field. A simplified method of calculating autocorrelation function is introduced and is utilized for ARMA model identification. Furthermore, an absolute deterioration factor such as Bicoherence is introduced. Machine diagnosis can be executed by this simplified calculation method of system parameter distance with weight. Proposed method proved to be a practical index for machine diagnosis by numerical examples.