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Attention Transformer-Based Model for Anomaly Detection Across Heterogeneous IoT Sensor Streams in Smart Industrial Systems.

Nader Behdad
Volume 5

Abstract

Smart industrial systems increasingly depend on heterogeneous Internet of Things sensor streams to monitor equipment health, production stability, energy consumption, environmental conditions, and operational safety in real time. However, anomaly detection in such environments remains challenging due to the high dimensionality of sensor data, nonlinear temporal dependencies, irregular sampling rates, noise, missing values, sensor drift, and the coexistence of multiple operating regimes across industrial processes. Traditional statistical monitoring methods and conventional machine learning approaches often fail to capture complex cross-sensor interactions and long-range temporal patterns, particularly when abnormal events are rare, diverse, and difficult to label. This paper proposes an attention Transformer-based model for anomaly detection across heterogeneous IoT sensor streams in smart industrial systems. The proposed framework leverages self-attention mechanisms, temporal positional encoding, multi-head attention layers, and feed-forward representation learning to model both short-term fluctuations and long-term dependencies among multivariate sensor signals. The system integrates data from diverse industrial sensors, including vibration, temperature, pressure, humidity, current, voltage, acoustic emission, flow rate, machine speed, and environmental monitoring devices. A comprehensive preprocessing pipeline was applied, including missing value imputation, noise filtering, temporal synchronization, normalization, sliding-window segmentation, feature embedding, and imbalance-aware training to improve robustness under real-world industrial conditions. The model identifies abnormal behavior by learning normal operating patterns and detecting deviations through reconstruction error, prediction residuals, attention-weight shifts, and anomaly scoring mechanisms. Experimental results demonstrate that the proposed attention Transformer-based framework achieves superior anomaly detection performance compared with traditional control charts, Isolation Forest, One-Class SVM, Random Forest, Autoencoder, LSTM, GRU, and Temporal Convolutional Network baselines. The proposed model records higher accuracy, precision, recall, F1-score, AUC-ROC, and lower false alarm rates across multiple industrial monitoring scenarios. Furthermore, attention-based interpretability analysis was incorporated to identify the most influential sensors and time intervals contributing to detected anomalies, enabling maintenance engineers to localize potential faults and understand abnormal system behavior more effectively. The findings indicate that attention Transformer models provide a scalable, adaptive, and interpretable solution for anomaly detection in heterogeneous IoT sensor streams, offering practical value for predictive maintenance, industrial safety, energy efficiency, fault diagnosis, and intelligent decision support in smart manufacturing and Industry 4.0 environments.

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References

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