多变量时间序列预测ASTRI-Horn监测数据：一种正常行为模型
Multivariate time-series forecasting of ASTRI-Horn monitoring data: A Normal Behavior Model
AI总结：
本文提出正常行为模型用于预测ASTRI-Horn监测数据，通过MLP实现多变量时间序列预测，验证了其在异常检测中的有效性。
摘要：
本研究提出了一种正常行为模型（NBM），旨在预测ASTRI-Horn切伦科夫望远镜在正常运行条件下获取的监测时间序列数据。分析重点是2022年9月至2024年7月期间由望远镜控制单元采集的15个物理变量，代表阿兹imuth和仰角电机的传感器测量值。在数据清洗、重采样、特征选择和相关性分析后，数据集被分割成固定长度的区间，在这些区间中，前I个样本代表提供给模型的输入序列，而预测长度T表示要预测的未来时间步数。随后应用滑动窗口技术来增加区间数量。一个多层感知机（MLP）被训练以同时进行多变量预测。模型性能通过均方误差（MSE）和归一化中位绝对偏差（NMAD）进行评估，并与长短期记忆（LSTM）网络进行了对比。MLP模型在不同特征和I-T配置下均表现出一致的结果，并在性能上与LSTM相当，但收敛速度更快。在最佳配置（4层隐藏层，每层720个单元，I-T长度各为300个样本，对应1分钟分辨率下的5小时）下，其在测试集上实现了MSE为0.019±0.003和NMAD为0.032±0.009。将预测时间范围扩展到6.5小时（该配置允许的最大范围）并未降低性能，证实了该模型在提供可靠小时级预测方面的有效性。所提出的NBM为在线ASTRI-Horn监测时间序列中的早期异常检测提供了强大的工具，为未来开发支持预测维护的故障预测与健康管理系统奠定了基础。
英文摘要：
This study presents a Normal Behavior Model (NBM) developed to forecast monitoring time-series data from the ASTRI-Horn Cherenkov telescope under normal operating conditions. The analysis focused on 15 physical variables acquired by the Telescope Control Unit between September 2022 and July 2024, representing sensor measurements from the Azimuth and Elevation motors. After data cleaning, resampling, feature selection, and correlation analysis, the dataset was segmented into fixed-length intervals, in which the first I samples represented the input sequence provided to the model, while the forecast length, T, indicated the number of future time steps to be predicted. A sliding-window technique was then applied to increase the number of intervals. A Multi-Layer Perceptron (MLP) was trained to perform multivariate forecasting across all features simultaneously. Model performance was evaluated using the Mean Squared Error (MSE) and the Normalized Median Absolute Deviation (NMAD), and it was also benchmarked against a Long Short-Term Memory (LSTM) network. The MLP model demonstrated consistent results across different features and I-T configurations, and matched the performance of the LSTM while converging faster. It achieved an MSE of 0.019+/-0.003 and an NMAD of 0.032+/-0.009 on the test set under its best configuration (4 hidden layers, 720 units per layer, and I-T lengths of 300 samples each, corresponding to 5 hours at 1-minute resolution). Extending the forecast horizon up to 6.5 hours-the maximum allowed by this configuration-did not degrade performance, confirming the model's effectiveness in providing reliable hour-scale predictions. The proposed NBM provides a powerful tool for enabling early anomaly detection in online ASTRI-Horn monitoring time series, offering a basis for the future development of a prognostics and health management system that supports predictive maintenance.
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