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Fault Detection and Diagnosis in Gears Using Wavelet Analysis Techniques and Comparison on their Diagnostic Capability

机译:使用小波分析技术的齿轮故障检测和诊断及其诊断能力的比较

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The concept of vibration based condition monitoring technology has been developing at a rapid stage in the recent years suiting to the maintenance of sophisticated and complicated machines. Nowadays, wavelet analysis based signal processing technique is applied as effective tool for condition monitoring. The experimental studies were conducted on the gear testing apparatus to obtain the vibration signal from a healthy gear and an induced faulty gear. In this paper, two different techniques using Laplace wavelet as base function are used to characterize the fault in the gear signals, specifically wavelet enveloped power spectrum and wavelet kurtosis. The wavelet parameters are optimized using genetic algorithm to select most fault related features. A comparative study detailing features of fault characterization is also given in order to understand the effectiveness of both the wavelet based signal processing methods and their fault diagnosis capability.
机译:近年来,振动的条件监测技术的概念在近年来的快速阶段旨在维护精密和复杂的机器。 如今,基于小波分析的信号处理技术应用于条件监测的有效工具。 在齿轮试验装置上进行实验研究,以从健康齿轮和诱导的故障齿轮获得振动信号。 在本文中,使用Laplace小波作为基础功能的两种不同的技术用于表征齿轮信号中的故障,特别是小波包络功率谱和小波峰氏症。 使用遗传算法优化小波参数来选择大多数故障相关的功能。 还给出了故障表征的比较研究,以便了解基于小波的信号处理方法及其故障诊断能力的有效性。

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