Cao Ruifeng, Yunusa-Kaltungo Akilu
Department of Mechanical, Aerospace and Civil Engineering, University of Manchester, Manchester M13 9PL, UK.
Sensors (Basel). 2021 Apr 23;21(9):2957. doi: 10.3390/s21092957.
The feasibility and usefulness of frequency domain fusion of data from multiple vibration sensors installed on typical industrial rotating machines, based on coherent composite spectrum (CCS) as well as poly-coherent composite spectrum (pCCS) techniques, have been well-iterated by earlier studies. However, all previous endeavours have been limited to rotor faults, thereby raising questions about the proficiency of the approach for classifying faults related to other critical rotating machine components such as gearboxes. Besides the restriction in scope of the founding CCS and pCCS studies on rotor-related faults, their diagnosis approach was manually implemented, which could be unrealistic when faced with routine condition monitoring of multi-component industrial rotating machines, which often entails high-frequency sampling at multiple locations. In order to alleviate these challenges, this paper introduced an automated framework that encompassed feature generation through CCS, data dimensionality reduction through principal component analysis (PCA), and faults classification using artificial neural network (ANN). The outcomes of the automated approach are a set of visualised decision maps representing individually simulated scenarios, which simplifies and illustrates the decision rules of the faults characterisation framework. Additionally, the proposed approach minimises diagnosis-related downtime by allowing asset operators to easily identify anomalies at their incipient stages without necessarily possessing vibration monitoring expertise. Building upon the encouraging results obtained from the preceding part of this approach that was limited to well-known rotor-related faults, the proposed framework was significantly extended to include experimental and open-source gear fault data. The results show that in addition to early established rotor-related faults classification, the approach described here can also effectively and automatically classify gearbox faults, thereby improving the robustness.
早期研究已充分阐述了基于相干复合谱（CCS）以及多相干复合谱（pCCS）技术，对安装在典型工业旋转机械上的多个振动传感器的数据进行频域融合的可行性和实用性。然而，此前所有的努力都局限于转子故障，这就引发了对于该方法在对诸如齿轮箱等其他关键旋转机械部件相关故障进行分类时的有效性的质疑。除了CCS和pCCS对转子相关故障的研究在范围上的限制外，它们的诊断方法是手动实施的，而在面对多部件工业旋转机械的日常状态监测时，这可能不切实际，因为这种监测通常需要在多个位置进行高频采样。为了缓解这些挑战，本文引入了一个自动化框架，该框架包括通过CCS生成特征、通过主成分分析（PCA）进行数据降维和使用人工神经网络（ANN）进行故障分类。自动化方法的结果是一组表示各个模拟场景的可视化决策图，这简化并说明了故障特征框架的决策规则。此外，所提出的方法通过允许资产运营商在不具备振动监测专业知识的情况下轻松识别早期异常，将与诊断相关的停机时间降至最低。基于该方法前一部分在仅限于众所周知的转子相关故障方面所取得的令人鼓舞的结果，所提出的框架得到了显著扩展，以纳入实验和开源齿轮故障数据。结果表明，除了早期确定的转子相关故障分类外，本文所述方法还能有效且自动地对齿轮箱故障进行分类，从而提高了鲁棒性。
