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Identification of the defective transmission devices using the wavelet transform

机译:使用小波变换识别有缺陷的传输设备

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In this paper, a system is described that uses the wavelet transform to automatically identify the particular failure mode of a known defective transmission device. The problem of identifying a particular failure mode within a costly failed assembly is of benefit in practical applications. In this system, external acoustic sensors, instead of intrusive vibrometers, are used to record the acoustic data of the operating transmission device. A skilled factory worker, who is unfamiliar with statistical classification, helps to determine the feature vector of the particular failure mode in the feature extraction process. In the automatic identification part, an improved learning vector quantization (LVQ) method with normalizing the inputting feature vectors is proposed to compensate for variations in practical data. Some acoustic data, which are collected from the manufacturing site, are utilized to test the effectiveness of the described identification system. The experimental results show that this system can identify the particular failure mode of a defective transmission device and find out the causes of failure successfully.
机译:在本文中,描述了一种系统,该系统使用小波变换自动识别已知故障传输设备的特定故障模式。在昂贵的故障组件中识别特定故障模式的问题在实际应用中是有益的。在该系统中,使用外部声学传感器代替侵入式振动计来记录正在运行的传输设备的声学数据。熟练的工厂工人不熟悉统计分类,可以帮助确定特征提取过程中特定故障模式的特征向量。在自动识别部分,提出了一种对输入的特征向量进行归一化的改进的学习向量量化(LVQ)方法,以补偿实际数据中的变化。从制造现场收集的一些声学数据被用来测试所描述的识别系统的有效性。实验结果表明,该系统可以识别出故障传输设备的特定故障模式,并成功找出故障原因。

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