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A fuzzy transition based approach for fault severity prediction in helical gearboxes

机译：基于模糊过渡的螺旋齿轮箱故障严重程度预测方法

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Rotating machinery is an important device supporting manufacturing processes, and a wide research works are devoted to detecting and diagnosing faults in such machinery. Recently, prognosis and health management in rotating machinery have received high attention as a research area, and some advances in this field are focused on fault severity assessment and its prediction. This paper applies a fuzzy transition based model for predicting fault severity conditions in helical gears. The approach combines Mamdani models and hierarchical clustering to estimate the membership degrees to fault severity levels of samples extracted from historical vibration signals. These membership degrees are used to estimate the weighted fuzzy transitions for modelling the evolution along the fault severity states over time, according to certain degradation path. The obtained fuzzy model is able of predicting the one step-ahead membership degrees to the severity levels of the failure mode under study, by using the current and the previous membership degrees to the severity levels of two available successive input samples. This fuzzy predictive model was validated by using real data obtained from a test bed with different damages of tooth breaking in the helical gears. Results show adequate predictions for two scenarios of fault degradation paths. (c) 2016 Elsevier B.V. All rights reserved.
机译：旋转机械是支持制造过程的重要设备，并且广泛的研究工作致力于检测和诊断这种机械中的故障。近年来，旋转机械的预后和健康管理作为研究领域受到了高度重视，该领域的一些进展集中在故障严重性评估及其预测上。本文应用基于模糊过渡的模型来预测斜齿轮的故障严重性状况。该方法结合了Mamdani模型和分层聚类，以估计从历史振动信号中提取的样本的故障严重性级别的隶属度。这些隶属度用于估计加权模糊过渡，以根据某些退化路径对沿故障严重性状态随时间的演变进行建模。通过使用当前和先前对两个可用连续输入样本的严重性等级的隶属度，所获得的模糊模型能够预测正在研究的故障模式的严重性等级的一个逐步超前隶属度。通过使用从试验台获得的真实数据验证了该模糊预测模型，该试验台的斜齿轮齿断裂受到不同的损害。结果显示出对于两种故障降级路径方案的充分预​​测。 （c）2016 Elsevier B.V.保留所有权利。

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