Skip-patching spatial–temporal discrepancy-based anomaly detection on multivariate time series

Xu，Yinsong1,2,3; Ding，Yulong1,3; Jiang，Jie4; Cong，Runmin5,6; Zhang，Xuefeng9; Wang，Shiqi2; Kwong，Sam7; Yang，Shuang Hua3,8

2024-12-07

10.1016/j.neucom.2024.128428

Neurocomputing   影响因子和分区

0925-2312

1872-8286

Anomaly detection in the Industrial Internet of Things (IIoT) is a challenging task that relies heavily on the efficient learning of multivariate time series representations. We introduce Skip-patching and Spatial–Temporal discrepancy mechanisms to improve the efficiency of detecting anomalies. Traditional feature extraction is hindered by redundant information in limited datasets. The situation is that feature generation from stable operational processes results in low-quality representations. To address this challenge, we propose the Skip-Patching mechanism. This approach involves selectively extracting features from partial data patches, prompting the model to learn more meaningful knowledge through self-supervised learning. It also effectively doubles the training sample size by creating independent sub-groups of patches. Despite the complex spatial and temporal relationships in IIoT systems, existing methods mainly extracted features from a single domain, either temporal or spatial (sensor-wise), or simply cascaded two features, i.e., one after one, which limited anomaly detection capabilities. To address this, we introduce the Spatial–Temporal Association Discrepancy component, which leverages discrepancies between spatial and temporal features to enhance latent representation learning. Our Skip-Patching Spatial–Temporal Anomaly Detection (SSAD) framework combines these two components to provide a more diverse and comprehensive learning process. Tested across four multivariate time series anomaly detection benchmarks, SSAD demonstrates superior performance, confirming the efficacy of combining Skip-patching and Spatial–Temporal features to enhance anomaly detection in IIoT systems.

Anomaly detection Industrial Internet of Things Multivariate time series Self-supervised learning

SCI ; EI

英语

第一

COMPUTER SCIENCE

2-s2.0-85202540746

Scopus

1.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,China
2.Department of Computer Science,City University of Hong Kong,Hong Kong,China
3.Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet,Southern University of Science and Technology,Shenzhen,Guangdong,China
4.College of Artificial Intelligence,China University of Petroleum (Beijing),Beijing,China
5.School of Control Science and Engineering,Shandong University,Jinan,250061,China
6.Key Laboratory of Machine Intelligence and System Control,Ministry of Education,Jinan,China
7.Department of Computing and Decision Sciences,Lingnan University,Hong Kong,China
8.Department of Computer Science,University of Reading,Reading,RG6 6AH,United Kingdom
9.College of Sciences,Northeastern University,Shenyang,110819,China

Xu，Yinsong,Ding，Yulong,Jiang，Jie,等. Skip-patching spatial–temporal discrepancy-based anomaly detection on multivariate time series[J]. Neurocomputing,2024,609.

Xu，Yinsong.,Ding，Yulong.,Jiang，Jie.,Cong，Runmin.,Zhang，Xuefeng.,...&Yang，Shuang Hua.(2024).Skip-patching spatial–temporal discrepancy-based anomaly detection on multivariate time series.Neurocomputing,609.

Xu，Yinsong,et al."Skip-patching spatial–temporal discrepancy-based anomaly detection on multivariate time series".Neurocomputing 609(2024).