标题： 评估机器健康指数数据预测质量的程序 显示英文标题

标题： A procedure for assessing of machine health index data prediction quality

摘要： 本文讨论了评估机器健康指数（HI）数据预测质量的挑战。许多现有的机器健康预测解决方案涉及通过视觉评估预测质量来粗略衡量预测样本与实际样本之间的相似度，缺乏精确的测量或决策方法。在本文中，我们介绍了一种具有多种变体和标准的通用程序。总体概念涉及将预测数据与真实的HI时间序列进行比较，但每种程序变体都通过统计分析确定了特定模式。此外，采用了一个基于统计的阈值来将结果分类为可靠或不可靠的预测。标准包括简单的度量（如均方误差MSE、平均绝对百分比误差MAPE）以及更高级的度量（如空间分位数包含因子、Kupiec的POF和TUFF统计量）。根据所选择的标准，模式和决策过程会有所不同。为了证明该方法的有效性，我们将所提出的程序应用于来自文献中的HI数据，涵盖了警告阶段（线性退化）和关键阶段（指数退化）。尽管该方法产生二元输出，但有可能扩展到多类别分类。此外，经验丰富的用户可以使用以百分比表示的质量度量来进行更深入的分析。 显示英文摘要

摘要： The paper discusses the challenge of evaluating the prognosis quality of machine health index (HI) data. Many existing solutions in machine health forecasting involve visually assessing the quality of predictions to roughly gauge the similarity between predicted and actual samples, lacking precise measures or decisions. In this paper, we introduce a universal procedure with multiple variants and criteria. The overarching concept involves comparing predicted data with true HI time series, but each procedure variant has a specific pattern determined through statistical analysis. Additionally, a statistically established threshold is employed to classify the result as either a reliable or non-reliable prognosis. The criteria include both simple measures (MSE, MAPE) and more advanced ones (Space quantiles-inclusion factor, Kupiec's POF, and TUFF statistics). Depending on the criterion chosen, the pattern and decision-making process vary. To illustrate effectiveness, we apply the proposed procedure to HI data sourced from the literature, covering both warning (linear degradation) and critical (exponential degradation) stages. While the method yields a binary output, there is potential for extension to a multi-class classification. Furthermore, experienced users can use the quality measure expressed in percentage for more in-depth analysis.