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Wavelet co-efficient of thermal image analysis for machine fault diagnosis

机译:机器故障诊断的小波节省热图像分析

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The ultimate goal of this study is to introduce a new method of machine fault diagnosis using different machine conditions data such as normal, misalignment, mass-unbalance and bearing-fault from infrared thermography (IRT). Using thermal image, it is easy to obtain information about the machine condition rather than other conventional methods of machine condition diagnostic technique. Thermal image technique can be successfully applied in the field electrical and electronics system, mechanical system, energy system and medical diagnosis. To get information from the image many techniques of image processing such as discrete Fourier transformation, discrete cosine transformation, neural networks, wavelet transform and many others methods is being used. In this study, our main focal point is to analysis thermal image by discrete wavelet decomposition and tries to find out significant result of machine condition monitoring. In this work, decomposition level of 2 shows satisfactory result for machine condition diagnosis.
机译:本研究的最终目标是使用不同机器条件数据引入一种新的机器故障诊断方法,例如红外热成像(IRT)的正常,未对准,大规模不平衡和轴承故障。使用热图像,很容易获取有关机器条件的信息而不是其他传统的机器状况诊断技术。热图像技术可以成功应用于现场电气和电子系统,机械系统,能量系统和医学诊断。为了从图像中获取信息的许多图像处理技术,例如离散傅里叶变换,离散余弦变换,神经网络,小波变换和许多方法。在本研究中,我们的主要焦点是通过离散小波分解分析热图像,并试图找出机器状态监测的显着结果。在这项工作中,2的分解水平显示了机器条件诊断的令人满意的结果。

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