Interpretable Early Failure Detection via Machine Learning and Trace Checking-based Monitoring

TL;DR

This paper introduces a GPU-accelerated, trace checking-based framework for early failure detection in systems, leveraging machine learning to learn temporal properties and significantly improving efficiency over existing methods.

Contribution

It demonstrates that monitoring certain Signal Temporal Logic fragments can be reduced to polynomial-time trace checking, enabling scalable, interpretable failure detection with GPU acceleration and genetic programming.

Findings

Achieves 2-10% performance improvement over state-of-the-art methods.

Reduces monitoring complexity to polynomial time for specific STL fragments.

Provides a scalable, interpretable framework for early failure detection.

Abstract

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.