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Faulted Gear Identification Of A Rotating Machinery Based On Wavelet Transform And Artificial Neural Network

机译：基于小波变换和人工神经网络的旋转机械故障齿轮识别

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摘要

In this paper, a condition monitoring and faults identification technique for rotating machineries using wavelet transform and artificial neural network is described. Most of the conventional techniques for condition monitoring and fault diagnosis in rotating machinery are based chiefly on analyzing the difference of vibration signal amplitude in the time domain or frequency spectrum. Unfortunately, in some applications, the vibration signal may not be available and the performance is limited. However, the sound emission signal serves as a promising alternative to the fault diagnosis system. In the present study, the sound emission of gear-set is used to evaluate the proposed fault diagnosis technique. In the experimental work, a continuous wavelet transform technique combined with a feature selection of energy spectrum is proposed for analyzing fault signals in a gear-set platform. The artificial neural network techniques both using probability neural network and conventional back-propagation network are compared in the system. The experimental results pointed out the sound emission can be used to monitor the condition of the gear-set platform and the proposed system achieved a fault recognition rate of 98% in the experimental gear-set platform.
机译：本文介绍了一种基于小波变换和人工神经网络的旋转机械状态监测与故障识别技术。用于旋转机械状态监测和故障诊断的大多数常规技术主要基于分析时域或频谱中振动信号幅度的差异。不幸的是，在某些应用中，可能无法获得振动信号，并且性能受到限制。但是，声音发射信号可以作为故障诊断系统的有希望的替代方法。在本研究中，齿轮组的声音发射用于评估所提出的故障诊断技术。在实验工作中，提出了一种结合能量谱特征选择的连续小波变换技术来分析齿轮组平台中的故障信号。系统中比较了使用概率神经网络和常规反向传播网络的人工神经网络技术。实验结果表明，该声发射可用于监测齿轮组平台的状况，所提出的系统在实验齿轮组平台上的故障识别率达到98％。

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