用可解释的实时监控，提前发现系统故障
Interpretable Early Failure Detection via Machine Learning and Trace Checking-based Monitoring
- 将信号时序逻辑监测转为向量化追踪检查，提升效率
- 在历史数据上学习时间性质，实现早期故障检测
- 支持GPU加速，适合需要可解释性的工业系统
监控是一种运行时验证技术，用于检查系统当前计算过程（部分轨迹）是否满足给定公式。传统方法需构建双指数级于公式大小的确定性自动机，限制了实用性。本文表明，在有限离散轨迹下，纯过去（共）安全片段的信号时序逻辑（STL）监控可简化为向量化追踪检查，其时间复杂度为公式大小与轨迹长度的多项式函数。基于此，我们提出一个基于遗传编程从历史轨迹数据中学习时间性质的GPU加速框架，实现可解释的早期故障检测。该框架在关键性能指标上相比现有最佳方法有2-10%的净提升。
原文摘要 · Abstract (English)
Monitoring is a runtime verification technique that allows one to check whether an ongoing computation of a system (partial trace) satisfies a given formula. It does not need a complete model of the system, but it typically requires the construction of a deterministic automaton doubly exponential in the size of the formula (in the worst case), which limits its practicality. In this paper, we show that, when considering finite, discrete traces, monitoring of pure past (co)safety fragments of Signal Temporal Logic (STL) can be reduced to trace checking, that is, evaluation of a formula over a trace, that can be performed in time polynomial in the size of the formula and the length of the trace. By exploiting such a result, we develop a GPU-accelerated framework for interpretable early failure detection based on vectorized trace checking, that employs genetic programming to learn temporal properties from historical trace data. The framework shows a 2-10% net improvement in key performance metrics compared to the state-of-the-art methods.
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