CyberPhysical systems (CPS) must be closely monitored to identify and potentially mitigate emergent problems that arise during their routine operations. However, the multivariate time-series data which they typically produce can be complex to understand and analyze. While formal product documentation often provides example data plots with diagnostic suggestions, the sheer diversity of attributes, critical thresholds, and data interactions can be overwhelming to non-experts who subsequently seek help from discussion forums to interpret their data logs. Deep learning models, such as Long Short-term memory (LSTM) networks can be used to automate these tasks and to provide clear explanations of diverse anomalies detected in real-time multivariate data-streams. In this paper we present RESAM, a requirements process that integrates knowledge from domain experts, discussion forums, and formal product documentation, to discover and specify requirements and design definitions in the form of time-series attributes that contribute to the construction of effective deep learning anomaly detectors. We present a case-study based on a flight control system for small Uncrewed Aerial Systems and demonstrate that its use guides the construction of effective anomaly detection models whilst also providing underlying support for explainability. RESAM is relevant to domains in which open or closed online forums provide discussion support for log analysis.
翻译:必须密切监测网络物理系统(CPS),以查明并可能减轻日常运行过程中出现的突发问题。然而,它们通常产生的多变时间序列数据可能十分复杂,难以理解和分析。虽然正式产品文件往往提供带有诊断建议的数据图样,但特性、临界阈值和数据互动的多样性对非专家来说可能非常广泛,因为非专家随后寻求讨论论坛帮助解释其数据日志。深度学习模型,如长期短期内存(LSTM)网络,可以用来使这些任务自动化,并清楚解释实时多变数据流中发现的各种异常现象。在本文件中,我们介绍RESAM,这是一个将来自域专家、讨论论坛和正式产品文件的知识综合起来的要求过程,以发现和具体要求,并设计有助于构建有效的深层学习异常探测器的时间序列属性。我们根据小型未加密航空系统飞行控制系统(LSTM)网络进行案例研究,并表明其使用有助于构建有效的异常探测模型,同时为开放性在线论坛提供基础支持。