Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input
Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input
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DOI：
10.48550/arxiv.2307.13907
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Neelanjana Pal;Diego Manzanas Lopez;Taylor T. Johnson
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文献类型：
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作者：
Neelanjana Pal;Diego Manzanas Lopez;Taylor T. Johnson
基于数据驱动的神经网络（NN）的异常检测和预测性维护是新兴的研究领域。基于NN的时间序列数据分析提供了对过去行为的有价值的见解，并估算了关键参数，例如设备的剩余使用寿命（RUL）和电池最新电池（SOC）。但是，通过传感器，输入时间序列数据可以暴露于故意或无意的噪声中，需要对这些NN进行稳健的验证和验证。本文介绍了使用基于集合的形式方法的时间序列回归NNS（TSREGNN）的鲁棒性验证方法的案例研究。它着重于利用可变长度输入数据来简化输入操作并增强网络体系结构的通用性。该方法应用于预后和健康管理（PHM）应用领域的两个数据集：（1）锂离子电池的SOC估计以及（2）涡轮机发动机的统治估计。使用基于恒星的可及性分析检查NNS的鲁棒性，几项绩效指标评估了输入对网络输出（即未来结果）的有界扰动的影响。总体而言，本文提供了一项全面的案例研究，用于验证和验证现实世界应用中时间序列数据的基于NN的分析，强调了鲁棒性测试对准确和可靠的预测的重要性，尤其是考虑到噪声对未来结果的影响。
Data-driven, neural network (NN) based anomaly detection and predictive maintenance are emerging research areas. NN-based analytics of time-series data offer valuable insights into past behaviors and estimates of critical parameters like remaining useful life (RUL) of equipment and state-of-charge (SOC) of batteries. However, input time series data can be exposed to intentional or unintentional noise when passing through sensors, necessitating robust validation and verification of these NNs. This paper presents a case study of the robustness verification approach for time series regression NNs (TSRegNN) using set-based formal methods. It focuses on utilizing variable-length input data to streamline input manipulation and enhance network architecture generalizability. The method is applied to two data sets in the Prognostics and Health Management (PHM) application areas: (1) SOC estimation of a Lithium-ion battery and (2) RUL estimation of a turbine engine. The NNs' robustness is checked using star-based reachability analysis, and several performance measures evaluate the effect of bounded perturbations in the input on network outputs, i.e., future outcomes. Overall, the paper offers a comprehensive case study for validating and verifying NN-based analytics of time-series data in real-world applications, emphasizing the importance of robustness testing for accurate and reliable predictions, especially considering the impact of noise on future outcomes.