Enabling Unattended Data Logging and Publication by Data Model Change Detection and Environmental Awareness

摘要

This paper presents a novel self-initializing algorithm using Change Detection to achieve self-awareness of unusual conditions without a prior modeling assumptions. Deviations from baseline nominal conditions yield a tip-off and the variation off baseline indicates a novelty to be logged for publication. Incremental processing of the data log enables common transients to be ignored and viewed as nominal. In this framework, only second pass novelties invoke enough interest for publication. The mathematical methods for enabling this exploit both classical control theory transfer functions to model the environment and O(3~n) Volterra series type polynomials as an innovative change detection method without explicit modeling.