标题： 一种管理工业4.0生产的新方法：通过具有拓扑特征的时间序列预测整体设备效率 显示英文标题

标题： A Novel Method to Manage Production on Industry 4.0: Forecasting Overall Equipment Efficiency by Time Series with Topological Features

摘要： 目的：整体设备效率（OEE）是一个关键的制造绩效指标，但其波动性使得短期预测变得复杂。 本研究提出了一种新的框架，结合时间序列分解和拓扑数据分析，以提高各种设备（如液压压力机系统）的OEE预测。 方法：该方法首先将每小时OEE数据分解为趋势、季节性和残差成分。 残差部分，捕捉短期变化，使用带有外生变量的季节性自回归积分滑动平均模型（SARIMAX）进行建模。 这些外生特征包括相关时间序列的统计描述符和拓扑摘要。 为了处理高维输入空间，我们提出了一种混合特征选择策略，基于统计显著的SARIMAX预测因子进行递归特征消除，并结合BIC引导的粒子群优化。 该框架在来自多个生产系统的实际数据集上进行了评估。 结果：所提出的模型始终优于传统的时间序列模型和先进的基于变压器的方法，实现了显著更低的平均绝对误差和平均绝对百分比误差。 结论：将经典预测与拓扑数据分析相结合提高了OEE预测的准确性，使复杂制造环境中的主动维护和有根据的生产决策成为可能。 显示英文摘要

摘要： Purpose: Overall equipment efficiency (OEE) is a key manufacturing KPI, but its volatile nature complicates short-term forecasting. This study presents a novel framework combining time series decomposition and topological data analysis to improve OEE prediction across various equipment, such as hydraulic press systems. Methods: The approach begins by decomposing hourly OEE data into trend, seasonal, and residual components. The residual, capturing short-term variability, is modeled using a seasonal ARIMA with exogenous variables (SARIMAX). These exogenous features include statistical descriptors and topological summaries from related time series. To manage the high-dimensional input space, we propose a hybrid feature selection strategy using recursive feature elimination based on statistically significant SARIMAX predictors, coupled with BIC-guided particle swarm optimization. The framework is evaluated on real-world datasets from multiple production systems. Results: The proposed model consistently outperforms conventional time series models and advanced transformer-based approaches, achieving significantly lower mean absolute error and mean absolute percentage error. Conclusion: Integrating classical forecasting with topological data analysis enhances OEE prediction accuracy, enabling proactive maintenance and informed production decisions in complex manufacturing environments.