Abnormal Data Classification Using Time-Frequency Temporal Logic
Luan Viet Nguyen, James P. Kapinski, Xiaoqing Jin, Jyotirmoy V. Deshmukh, Kenneth Butts, Taylor Thomas Johnson · 2017
We present a technique to investigate abnormal behaviors of signals in both time and frequency domains using an extension of time-frequency logic that uses the continuous wavelet transform. Abnormal signal behaviors such as unexpected oscillations, called hunting behavior, can be challenging to capture in the time domain; however, these behaviors can be naturally captured in the time-frequency domain. We introduce the concept of parametric time-frequency logic and propose a parameter synthesis approach that can be used to classify hunting behavior. We perform a comparative analysis between the proposed algorithm, an approach based on support vector machines using linear classification, and a method that infers a signal temporal logic formula as a data classifier. We present experimental results based on data from a hydrogen fuel cell vehicle application and electrocardiogram data extracted from the MIT-BIH Arrhythmia Database.