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GEAR FAULT DETECTION WITH THE ENERGY OPERATOR AND ITS VARIANTS

机译:齿轮故障检测与能量运营商及其变体

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Vibration analysis is currently the most efficient, non-invasive way to monitor the condition of the gears. Faults in gears can be of two distinct types, distributed or local. Many fault detection methods are effective for one type of fault or the other but not both. Also, many methods have the inconvenience that they are not simple and/or require initial information about the state of gear. In this paper, several methods are proposed with the objective of finding a filter-free, simple and efficient method for the detection of both types of faults. The calculus enhanced energy operator (CEEO), previously designed for fault detection in bearings, is proposed here for the first time on gears. Two new methods, the EO123 and EO23, based on the original energy operator are also proposed and evaluated. All the proposed methods are filter free, computationally simple and can handle a certain level of noise and interference. With the exception of low rotational frequencies of the gears, it is demonstrated via simulated and experimentally obtained signals that the CEEO method can handle noise better than the other proposed methods and that the EO23 method can handle interference better than the others. Different conditions determine the effectiveness of the methods.
机译:振动分析目前是监测齿轮状况的最有效,非侵入性的方式。齿轮中的故障可以是两个不同的类型,分布式或本地。许多故障检测方法对于一种类型的故障是有效的,而不是两者都是有效的。此外,许多方法对它们并不简单和/或需要有关档位状态的初始信息具有不便。在本文中,提出了几种方法,其目的是找到无滤波,简单,有效的方法来检测两种类型的故障。在齿轮上第一次提出了先前为轴承故障检测设计用于故障检测的微积分增强能量操作员(CEEO)。还提出了两种新方法,基于原始能量运算符的EO123和EO23,并评估。所有提出的方法都无滤波,计算简单,可以处理一定程度的噪声和干扰。除了齿轮的低旋转频率外,通过模拟和实验获得的信号证明了CEEO方法可以比其他提出的方法更好地处理噪声,并且EO23方法可以比其他方法更好地处理干扰。不同的条件决定了方法的有效性。

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