The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery
The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery
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DOI：
10.1016/j.ymssp.2010.11.007
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发表时间：
2011-05-01
影响因子：
8.4
通讯作者：
Kostopoulos, V.
中科院分区：
文献类型：
--
作者：
Loutas, T. H.;Roulias, D.;Kostopoulos, V.
使用各种非破坏性技术监测齿轮的渐进磨损，以及在采集的记录上使用先进的信号处理技术，以指导更有效的诊断方案，是本工作的范围。为此，在单级实验室规模的变速箱中对健康的齿轮进行了数小时的测试，直到它们严重损坏。在试验中实现了三种在线监测技术。利用振动和声发射记录以及来自润滑油油屑监测(ODM)的数据来评估齿轮的状况。通过常规(在时间和频率域)和非常规(基于小波的)信号处理技术，从采集的波形中提取过多的参数/特征。数据融合是在从所有三种测量技术提取的特征中最具代表性的特征集成到单个数据矩阵中的水平上完成的。利用主成分分析(PCA)对数据矩阵进行降维，利用独立成分分析(ICA)进一步识别数据中的独立成分，并将其与齿轮箱的不同损伤模式进行关联。将振动、声发射和ODM数据相结合，提高了状态监测方案的诊断能力和可靠性，得到了非常有意义的结果。本工作总结了两个研究小组为更可靠地对旋转机械和变速箱进行状态监测所做的共同努力。(C)2010爱思唯尔有限公司。保留所有权利。
The monitoring of progressive wear in gears using various non-destructive technologies as well as the use of advanced signal processing techniques upon the acquired recordings to the direction of more effective diagnostic schemes, is the scope of the present work. For this reason multi-hour tests were performed in healthy gears in a single-stage lab scale gearbox until they were seriously damaged. Three on-line monitoring techniques are implemented in the tests. Vibration and acoustic emission recordings in combination with data coming from oil debris monitoring (ODM) of the lubricating oil are utilized in order to assess the condition of the gears. A plethora of parameters/features were extracted from the acquired waveforms via conventional (in time and frequency domain) and non-conventional (wavelet-based) signal processing techniques. Data fusion was accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix. Principal component analysis (PCA) was utilized to reduce the dimensionality of the data matrix whereas independent component analysis (ICA) was further applied to identify the independent components among the data and correlate them to different damage modes of the gearbox. Finally heuristic rules based on characteristic values of the resulted independent components were set, realizing thus a health monitoring scheme for gearboxes.The integration of vibration, AE and ODM data increases the diagnostic capacity and reliability of the condition monitoring scheme concluding to very interesting results. The present work summarizes the joint efforts of two research groups towards a more reliable condition monitoring of rotating machinery and gearboxes specifically. (C) 2010 Elsevier Ltd. All rights reserved.