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首页> 外文期刊>Journal of Industrial Engineering International >Analysis of motor fan radiated sound and vibration waveform by automatic pattern recognition technique using “Mahalanobis distance”
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Analysis of motor fan radiated sound and vibration waveform by automatic pattern recognition technique using “Mahalanobis distance”

机译:使用“马哈拉诺比斯距离”的自动模式识别技术分析风扇的辐射声和振动波形

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摘要

In recent years, as the weight of IT equipment has been reduced, the demand for motor fans for cooling the interior of electronic equipment is on the rise. Sensory test technique by inspectors is the mainstream for quality inspection of motor fans in the field. This sensory test requires a lot of experience to accurately diagnose differences in subtle sounds (sound pressures) of the fans, and the judgment varies depending on the condition of the inspector and the environment. In order to solve these quality problems, development of an analysis method capable of quantitatively and automatically diagnosing the sound/vibration level of a fan is required. In this study, it was clarified that the analysis method applying the MT system based on the waveform information of noise and vibration is more effective than the conventional frequency analysis method for the discrimination diagnosis technology of normal and abnormal items. Furthermore, it was found that due to the automation of the vibration waveform analysis system, there was a factor influencing the discrimination accuracy in relation between the fan installation posture and the vibration waveform.
机译:近年来,随着IT设备重量的减轻,对用于冷却电子设备内部的电动机风扇的需求正在上升。检查员的感官测试技术是该领域电机风扇质量检查的主流。这种感官测试需要大量经验才能准确诊断风扇的细微声音(声压)中的差异,并且判断会根据检查员的状况和环境而有所不同。为了解决这些质量问题,需要开发一种能够定量地并且自动地诊断风扇的声音/振动水平的分析方法。在本研究中,我们明确了基于噪声和振动的波形信息的MT系统分析方法比常规的频率分析方法对正常和异常项目的判别诊断技术更为有效。此外,发现由于振动波形分析系统的自动化,存在影响风扇安装姿势与振动波形之间的辨别精度的因素。

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