Ma, Liwei (xian jiao tong university), Wang, Yu (xian jiao tong university), Zhu, Cheng (China North Engine Research Institute), Zhang, Mingquan (xian jiao tong university), Hu, Ruijie (China North Engine Research Institute), Hongrui, Cao (State Key Laboratory for Manufacturing Systems Engineering, Xi'a)

Autocorrelation-Based Signal Reconstruction for Multi-Source Time Series Anomaly Detection

Scheduled for presentation during the Invited session "Estimation and Prediction" (FrB1), Friday, November 1, 2024, 11:10−11:30, Room T1

7th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, Oct 30 - Nov 1, 2024, Dalian, China

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

In the quest to enhance IoT device performance and expand cross-domain system functionalities, pinpointing anomalies in multi-source time series data becomes crucial. Traditional deep learning approaches often overlook the complex patterns in data. Our innovative method integrates time series decomposition into anomaly detection, thus revealing intrinsic periodicities vital for advanced engine and powertrain modeling. By employing a joint loss function, our approach not only improves anomaly detection accuracy and stability but also aligns with essential aspects of interconnected and automated vehicle control. Our method, proven superior through extensive comparison across datasets and algorithms, significantly advances engine and powertrain supervision, management, diagnostics, and modeling, showcasing a notable contribution to these fields.