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Exponential Smoothing Model for Condition Monitoring: A Case Study

机译:条件监测指数平滑模型:案例研究

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With the development of advanced technologies, the reliability evaluation of key components or equipments and preventive maintenance decision-making are becoming more and more important. Online reliability assessment and remaining life prediction using degradation data of real-time monitoring have been accounted as a hot trend in development. This article presents a means of conditional reliability evaluation by extracting characteristic parameters, which can represent the performance of the equipment in service. According to the time order, the physical performance measurements are regarded as time series. Exponential smoothing method is adopted to establish time series prediction model by computing exponential smoothing values. Performance parameters over a future time period are gained by the model constructed before. Experiments were carried out on a double row cylindrical roller bearing to get the vibration information and a mathematical model was built to forecast the future performance. The experiments proved the validity of the aforementioned method.
机译:随着先进技术的发展,关键部件或设备的可靠性评估以及预防性维护决策变得越来越重要。在线可靠性评估和使用实时监测的退化数据的剩余寿命预测已被占开发的热点。本文通过提取特征参数提出了一种有条件可靠性评估的手段,该特征参数可以代表服务中设备的性能。根据时间顺序,物理性能测量被认为是时间序列。采用指数平滑方法来建立时间序列预测模型来计算指数平滑值。在未来的时间段内的性能参数由之前构建的模型获得。实验在双排圆柱滚子轴承上进行,以获得振动信息,建立数学模型以预测未来的性能。实验证明了上述方法的有效性。

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