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首页> 外文期刊>Journal of Industrial Mathematics >Cross Correlation for Condition Monitoring of Variable Load and Speed Gearboxes
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Cross Correlation for Condition Monitoring of Variable Load and Speed Gearboxes

机译：互相关用于变速箱和变速箱状态监测

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The ability to identify incipient faults at an early stage in the operation of machinery has been demonstrated to provide substantial value to industry. These benefits for automated, in situ, and online monitoring of machinery, structures, and systems subject to varying operating conditions are difficult to achieve at present when they are run in operationally constrained environments that demand uninterrupted operation in this mode. This work focuses on developing a simple algorithm for this problem class; novelty detection is deployed on feature vectors generated from the cross correlation of vibration signals from sensors mounted on disparate locations in a power train. The behavior of these signals in a gearbox subject to varying load and speed is expected to remain in a commensurate state until a change in some physical aspect of the mechanical components, presumed to be indicative of gearbox failure. Cross correlation will be demonstrated to generate excellent classification results for a gearbox subject to independently changing load and speed. It eliminates the need to analyze the highly complex dynamics of this system; it generalizes well across untaught ranges of load and speed; it eliminates the need to identify and measure all predominant time-varying parameters; it is simple and computationally inexpensive.
机译：已经证明了在机械操作的早期阶段识别出初期故障的能力为工业提供了巨大的价值。目前，当机器，结构和系统在运行受限的环境中运行时，需要对这些设备，结构和系统进行自动，现场和在线监控，这些好处很难在这种模式下连续运行。这项工作着重于为该问题类别开发一种简单的算法。将新颖性检测部署在特征向量上，这些特征向量是由来自安装在动力传动系统不同位置的传感器的振动信号的互相关产生的。这些信号在变速箱中承受变化的负载和速度的行为，预计将保持相称的状态，直到机械组件的某些物理方面发生变化（假定是变速箱故障的指示）为止。互相关性将得到证明，可在独立改变负载和速度的情况下为变速箱生成出色的分类结果。它消除了分析该系统高度复杂的动力学的需要;它在负载和速度的未受控制的范围内都能很好地概括;它消除了识别和测量所有主要时变参数的需要;它简单且计算便宜。

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