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首页> 外文期刊>SAE International Journal of Vehicle Dynamics, Stability, and NVH >Value of Optimal Wavelet Function in Gear Fault Diagnosis
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Value of Optimal Wavelet Function in Gear Fault Diagnosis

机译:最优小波函数在齿轮故障的价值诊断

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Gear fault diagnosis is important in the vibration monitoring of any rotating machine. When a localized fault occurs in gears, the vibration signals always display non-stationary behavior. In early stage of gear failure, the gear mesh frequency (GMF) contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. This paper presents the value of optimal wavelet function for early detection of faulty gear. The Envelope Detection (ED) and the Energy Operator are used for gear fault diagnosis as common techniques with and without the proposed optimal wavelet to verify the effectiveness of the optimal wavelet function. Kurtosis values are determined for the previous techniques as an indicator parameter for the ability of early gear fault detection. The comparative study is applied to real vibration signals. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an envelope analysis enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers introduce the load on the output joint shaft flanges. The gearbox used for experimental measurements is the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive.
机译:齿轮振动故障诊断是非常重要的监控任何旋转的机器。局部故障发生在齿轮振动信号总是显示不稳定的行为。齿轮的早期故障,齿轮啮合(GMF)包含很少的能量和频率往往是被噪声和更高级的宏观结构振动。处理方法将需要删除这种腐败噪音和干扰。提出了最优小波的价值函数齿轮故障早期检测的。包络检波(ED)和能量算子用于齿轮故障诊断是常见的吗有或没有提出最优技术小波来验证的有效性最优小波函数。前技术作为一个决定早期齿轮的指标参数的能力故障检测。真正的振动信号。与干扰的频率相关振动,振动信号过滤带通滤波器由Morlet小波基于最大的参数优化峰度。周期性带内噪声和亮点冲动的功能,一个信封分析增强算法应用于过滤信号。测;内燃机的输出测介绍上的负载输出关节轴法兰。实验测量结果是大多数的类型在现代中小型常用客车横向安装动力系统和前轮驱动。

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