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Exponential smoothing model for condition monitoring: A case study

机译:用于状态监测的指数平滑模型:一个案例研究

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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。使用实时监控的退化数据进行在线可靠性评估和剩余寿命预测已被视为发展的热门趋势。本文提出了一种通过提取特征参数来进行条件可靠性评估的方法,这些参数可以代表在役设备的性能。根据时间顺序,物理性能测量被视为时间序列。通过计算指数平滑值,采用指数平滑方法建立时间序列预测模型。通过之前构建的模型可以获得将来一段时间内的性能参数。在双列圆柱滚子轴承上进行了实验,以获取振动信息,并建立了数学模型来预测未来的性能。实验证明了上述方法的有效性。

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