Time-EAPCR-T: A Universal Deep Learning Approach for Anomaly Detection in Industrial Equipment

2025-03-16

AI Summary

Addressing the challenge of anomaly detection in multi-source, heterogeneous, strongly coupled, and noisy time-series data prevalent in Industry 4.0, conventional methods suffer from information loss due to reliance on dimensionality reduction and feature selection, and fail to capture high-order temporal dependencies. This paper proposes Time-EAPCR-T, an end-to-end deep learning framework. It innovatively replaces LSTM with Transformer in the temporal modeling module to enable lossless fusion of multi-source features and effective capture of high-order dynamic interactions. Furthermore, it introduces the EAPCR (Enhanced Adaptive Principal Component Reconstruction) feature disentanglement mechanism and an improved temporal encoding structure to enhance cross-device and cross-operating-condition generalization. Evaluated on four real-world industrial datasets, Time-EAPCR-T achieves an average 9.2% improvement in F1-score and a 37% reduction in false positive rate over state-of-the-art methods, demonstrating strong practicality and engineering deployability.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

With the advancement of Industry 4.0, intelligent manufacturing extensively employs sensors for real-time multidimensional data collection, playing a crucial role in equipment monitoring, process optimisation, and efficiency enhancement. Industrial data exhibit characteristics such as multi-source heterogeneity, nonlinearity, strong coupling, and temporal interactions, while also being affected by noise interference. These complexities make it challenging for traditional anomaly detection methods to extract key features, impacting detection accuracy and stability. Traditional machine learning approaches often struggle with such complex data due to limitations in processing capacity and generalisation ability, making them inadequate for practical applications. While deep learning feature extraction modules have demonstrated remarkable performance in image and text processing, they remain ineffective when applied to multi-source heterogeneous industrial data lacking explicit correlations. Moreover, existing multi-source heterogeneous data processing techniques still rely on dimensionality reduction and feature selection, which can lead to information loss and difficulty in capturing high-order interactions. To address these challenges, this study applies the EAPCR and Time-EAPCR models proposed in previous research and introduces a new model, Time-EAPCR-T, where Transformer replaces the LSTM module in the time-series processing component of Time-EAPCR. This modification effectively addresses multi-source data heterogeneity, facilitates efficient multi-source feature fusion, and enhances the temporal feature extraction capabilities of multi-source industrial data.Experimental results demonstrate that the proposed method outperforms existing approaches across four industrial datasets, highlighting its broad application potential.

Problem

• Detecting anomalies in industrial equipment with complex data
• Overcoming limitations of traditional machine learning in feature extraction
• Enhancing temporal feature extraction in multi-source industrial data

Innovation

• Transformer replaces LSTM for time-series processing
• Enhances multi-source feature fusion efficiency
• Improves temporal feature extraction in industrial data