2024/05/14 by Mazen Alamir, Alamir, Mazen, Raphaël Dion +1
Computer Science · Engineering · #Anomaly Detection Techniques and Applications #FOS: Electrical engineering #Fault Detection and Control Systems #Systems and Control (eess.SY) #Time Series Analysis and Forecasting #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2405.08349
openalex publication_date 2024/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
In this paper, a new model-free anomaly detection framework is proposed for time-series induced by industrial dynamical systems.The framework lies in the category of conventional approaches which enable appealing features such as a learning with reduced amount of training data, a high potential for explainability as well as a compatibility with incremental learning mechanism to incorporate operator feedback after an alarm is raised and analyzed. Although these are crucial features towards acceptance of data-driven solutions by industry, they are rarely considered in the comparisons that generally almost exclusively focus on performance metrics. Moreover, the features engineering step involved in the proposed framework is inspired by the time-series being implicitly governed by physical laws as it is generally the case in industrial time-series. Two examples are given to assess the efficiency of the proposed approach.