Process Monitoring Using Maximum Sequence Divergence

TL;DR

This paper presents a method for process monitoring that uses maximum sequence divergence and probabilistic measures to detect and evaluate system deviations based on temporal sequence analysis.

Contribution

It introduces a novel approach combining sequence discretization, Markov models, and Jensen-Shannon Divergence for effective system change detection.

Findings

Successfully detects system deviations

Quantifies deviation significance probabilistically

Effective in monitoring dynamic systems

Abstract

Process Monitoring involves tracking a system's behaviors, evaluating the current state of the system, and discovering interesting events that require immediate actions. In this paper, we consider monitoring temporal system state sequences to help detect the changes of dynamic systems, check the divergence of the system development, and evaluate the significance of the deviation. We begin with discussions of data reduction, symbolic data representation, and the anomaly detection in temporal discrete sequences. Time-series representation methods are also discussed and used in this paper to discretize raw data into sequences of system states. Markov Chains and stationary state distributions are continuously generated from temporal sequences to represent snapshots of the system dynamics in different time frames. We use generalized Jensen-Shannon Divergence as the measure to monitor changes of the stationary symbol probability distributions and evaluate the significance of system deviations. We prove that the proposed approach is able to detect deviations of the systems we monitor and assess the deviation significance in probabilistic manner.