An integrated deep learning model for intelligent recognition of long-distance natural gas pipeline features

L Wang, W Guo, J Guo, S Zheng, Z Wang… - Reliability Engineering & …, 2025 - Elsevier
Pipeline feature recognition is crucial for the reliability and safety of long-distance natural
gas pipelines. Utilizing manual or machine learning methods to recognize pipeline features …

Multivariate time series classification with crucial timestamps guidance

D Zhang, J Gao, X Li - Expert Systems with Applications, 2024 - Elsevier
Transformer-based deep learning methods have significantly facilitated multivariate time
series classification (MTSC) tasks. However, due to the inherent operation of self-attention …

Data-driven unsupervised anomaly detection of manufacturing processes with multi-scale prototype augmentation and multi-sensor data

Z **e, Z Zhang, J Chen, Y Feng, X Pan, Z Zhou… - Journal of Manufacturing …, 2024 - Elsevier
Accurate anomaly detection (AD) of machine tools is crucial to ensure the quality and
efficiency of the manufacturing processes. Due to the lack of tool anomaly information, it is …

MCGnet with multi-level gated unit transformations: A time series prediction-based model for unknown pattern abnormality detection

K Fu, H Li - Expert Systems with Applications, 2024 - Elsevier
The task of anomaly detection in data patterns remains challenging due to the diverse of
fault patterns, which often render existing models ineffective when encountered with …

Prediction of Anomalous Events with Data Augmentation and Hybrid Deep Learning Approach

AS Raihan - 2024 - search.proquest.com
In this study, we propose a novel anomaly detection framework designed specifically for
Multivariate Time Series (MTS) data, addressing the prevalent challenges in analyzing such …

Mclcad: Multivariate Convolutional Long Short-Term Memory Cross-Attention Detector for Multivariate Time Series Anomaly Detection

M Lu, H Chen, Q Miao, P Zhang, G Sun, F Qian… - Available at SSRN … - papers.ssrn.com
The reliability and accuracy of time series anomaly detection plays a key role in timely
identification and handling of data anomalies, thereby preventing potential severe issues …