School of Engineering, the University of Birmingham, Birmingham B15 2TT, UK.
Mechanical School, Yangzhou University, Yangzhou 225127, China.
Sensors (Basel). 2020 Sep 25;20(19):5504. doi: 10.3390/s20195504.
Recent developments in the area of condition monitoring research have been targeted towards predicting machinery health condition for the purpose of preventative maintenance. Typically, published research uses data collected from rotating components (bearings, cutting tools, etc.) working in an idealized lab environment as the case study for prognosis algorithm validations. However, the operational implementation in industry is still very sporadic, mainly owing to the lack of proper data allowing sufficiently mature development of comprehensive methodologies. The prognosis methodology presented herein bridges the gap between academic research and industrial implementations by employing a novel time period for prognosis and implementing random coefficients regression models. The definition of the remaining maintenance-free operating period (RMFOP) is proposed first, which helps to transform the usefulness of the degradation data that is readily available from data short of failure. Degradation patterns are subsequently extracted from the original degradation data, before fitting into either of two regression models (linear or exponential). The system residual life distributions are then computed and updated by estimating the parameter statistics within the model. This RMFOP-based methodology is validated using real-world degradation data collected from multiple operational railway switch systems across Great Britain. The results indicate that both the linear model and the exponential model can produce residual life distributions with a sufficient prediction accuracy for this specific application. The exponential model gives better predictions, the accuracy of which also improves as more of system life percentage has elapsed. By using the RMFOP methodology, switch system health condition affected by an incipient overdriving fault is recognized and predicted.
近年来，状态监测研究领域的发展一直致力于预测机械健康状况，以实现预防性维护。通常，已发表的研究使用从在理想实验室环境中工作的旋转部件（轴承、刀具等）收集的数据作为预测算法验证的案例研究。然而，在工业中的实际应用仍然非常零星，主要是由于缺乏适当的数据，无法充分成熟地开发全面的方法。本文提出的预测方法通过采用新的预测时间段和实施随机系数回归模型，弥合了学术研究和工业实施之间的差距。首先提出了剩余无故障运行期 (RMFOP) 的定义，这有助于转化从数据不足的故障中获得的退化数据的有用性。随后，从原始退化数据中提取退化模式，然后拟合到两种回归模型（线性或指数）之一。然后通过估计模型内的参数统计数据来计算和更新系统剩余寿命分布。使用从英国多个运营铁路道岔系统收集的真实退化数据对基于 RMFOP 的方法进行了验证。结果表明，线性模型和指数模型都可以为特定应用产生具有足够预测精度的剩余寿命分布。指数模型给出了更好的预测，随着系统寿命百分比的增加，其准确性也会提高。通过使用 RMFOP 方法，可以识别和预测受初始超速故障影响的道岔系统健康状况。
