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Intelligent condition monitoring of a gearbox using artificial neural network

机译：使用人工神经网络的变速箱状态智能监控

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摘要

This paper concentrates on a new procedure which experimentally recognises gears and bearings faults of a typical gearbox system using a multi-layer perceptron neural network. Feature vector which is one of the most significant parameters to design an appropriate neural network was innovated by standard deviation of wavelet packet coefficients. The gear conditions were considered to be normal gearbox and slight- and medium-worn and broken-teeth gears faults and a general bearing fault which were five neurons of output layer with the aim of fault detection and identification. A downscaled 2-layer multi-layer perceptron neural-network-based system with great accuracy was designed to carry out the task. In this research, vibration signals were recognised as the most reliable source to extract the feature vector which were synchronised by piecewise cubic hermite interpolation (PCHI) and pre-processed using the standard deviation of wavelet packet coefficients.
机译：本文着重研究一种新的程序，该程序使用多层感知器神经网络通过实验识别典型变速箱系统的齿轮和轴承故障。利用小波包系数的标准偏差，对设计合适的神经网络最重要的参数之一即特征向量进行了创新。齿轮状态被认为是正常的齿轮箱，轻微，中度磨损和断齿的齿轮故障以及一般的轴承故障，它们是输出层的五个神经元，目的是进行故障检测和识别。设计了一种高精度的基于缩减规模的2层多层感知器神经网络的系统来执行此任务。在这项研究中，振动信号被认为是提取特征向量的最可靠来源，特征向量通过分段三次Hermite插值（PCHI）进行同步，并使用小波包系数的标准偏差进行了预处理。

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