Modelling of gearbox dynamics under time-varying nonstationary load for distributed fault detection and diagnosis
Modelling of gearbox dynamics under time-varying nonstationary load for distributed fault detection and diagnosis
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DOI：
10.1016/j.euromechsol.2010.03.002
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发表时间：
2010-07-01
影响因子：
4.1
通讯作者：
Haddar, Mohamed
中科院分区：
文献类型：
--
作者：
Bartelmus, Walter;Chaari, Fakher;Haddar, Mohamed
时变非平稳机械系统的故障检测与诊断是一个极具挑战性的问题。在过去的二十年左右的研究已经注意到，机器工作在非稳态负载/速度条件下，在他们的正常操作。齿轮箱的诊断功能被发现是负载相关的。为了更好地理解所涉及的现象，并确保仿真和实验结果之间的协议，齿轮箱（固定轴两级齿轮箱和行星齿轮箱）在不同的负载条件下运行的两个模型被提出。这些模型是基于采矿业中使用的两个机械系统，即带式输送机和斗轮挖掘机。提出了一种反映技术条件变化和负载变化的原始传动误差函数，并采用基于能量的参数（信号均方根值或频谱齿轮啮合分量幅值的算术和）作为诊断特征。仿真结果表明，负载值，条件的变化和诊断功能之间有很强的相关性。这些发现是状态监测的关键。由于使用了这些模型，人们可以更好地理解通过分析从真实的机器捕获的振动信号所识别的现象。(c)2010年Elsevier Masson SAS。All rights reserved.
Fault detection and diagnosis in mechanical systems during their time-varying nonstationary operation is one of the most challenging issues. In the last two decades or so researches have noticed that machines work in nonstationary load/speed conditions during their normal operation. Diagnostic features for gearboxes were found to be load dependent. This was experimentally confirmed by a smearing effect in the spectrum.In order to better understand the involved phenomena and to ensure agreement between simulation and experimental results, two models of gearboxes (a fixed-axis two-stage gearbox and a planetary gearbox) operating under varying load conditions are proposed. The models are based on two mechanical systems used in the mining industry, i.e. the belt conveyor and the bucket wheel excavator. An original transmission error function expressing changes in technical condition and load variation is presented.Energy based parameters (the signal RMS value or the arithmetic sum of the amplitudes of spectral gearmesh components) are adopted as the diagnostic features. Simulation results show a strong correlation between load values, changes in condition and the diagnostic features. The findings are key to condition monitoring. Thanks to the use of the models one can better understand the phenomena identified through an analysis of vibration signals captured from real machines. (c) 2010 Elsevier Masson SAS. All rights reserved.