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.

中文翻译：

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使用基于剩余免维护运行时间（RMFOP）的方法来刺激复杂操作系统的剩余寿命分布

状态监测研究领域的最新发展已针对预测机械健康状况以进行预防性维护。通常，已发表的研究使用从理想化实验室环境中工作的旋转组件（轴承，切削工具等）收集的数据作为预测算法验证的案例研究。但是，工业上的操作实施仍然非常零星，主要是由于缺乏适当的数据，无法全面发展成熟的综合方法。本文介绍的预后方法论通过采用新的预后时间段并实施随机系数回归模型来弥合学术研究与工业实施之间的差距。首先提出了剩余的免维护运行时间（RMFOP）的定义，这有助于从不存在故障的数据中转换出易于获得的降级数据的有用性。随后在适合两个回归模型（线性或指数）中的任何一个之前，从原始退化数据中提取退化模式。然后，通过估计模型中的参数统计信息来计算和更新系统剩余寿命分布。这种基于RMFOP的方法论已使用从整个英国的多个运营铁路道岔系统中收集的实际退化数据进行了验证。结果表明，线性模型和指数模型都可以产生剩余寿命分布，并且对于该特定应用具有足够的预测精度。指数模型可以提供更好的预测，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期超速驾驶故障影响的交换机系统的运行状况。