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GEAR FAULT DETECTION WITH THE ENERGY OPERATOR AND ITS VARIANTS

机译:能量运算符及其变量的齿轮故障检测

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摘要

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:振动分析是目前监视齿轮状况的最有效,最无创的方法。齿轮故障可以有两种不同的类型,即分布式故障或局部故障。许多故障检测方法对一种故障或另一种故障均有效,但对两种故障均无效。而且,许多方法的不便之处在于它们不简单和/或需要有关档位状态的初始信息。本文提出了几种方法,目的是找到一种无滤波器,简单而有效的方法来检测两种类型的故障。以前专门为轴承中的故障检测而设计的微积分增强能量算子(CEEO)首次在齿轮上提出。还提出并评估了基于原始能量算子的两种新方法EO123和EO23。所有提出的方法都是无滤波器的,计算简单,并且可以处理一定水平的噪声和干扰。除了齿轮的低旋转频率外,通过仿真和实验获得的信号证明,CEEO方法比其他建议方法可以更好地处理噪声,而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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