SRT: A Skip-Range Transformer for Detecting Anomalies in Multiattribute Industrial Time Series Data
Honghao Gao, Wangyang Jiang, Qionghuizi Ran, K. Wang, Xiaoning Ma, Yueshen Xu · IEEE Internet of Things Journal · 2025
In the field of the industrial Internet, monitoring data from industrial equipment exhibit characteristics of high concurrency, high throughput, and high-frequency time series. Anomaly detection can accurately analyze the health status of equipment and enhance the monitoring capabilities of industrial Internet devices. To address the complex relationships between multi-attribute data and the need for anomaly detection, this paper presents an unsupervised multi-attribute industrial anomaly detection approach called the skip-range transformer (SRT). This approach learns anomaly features through parallel segmentation and skip-range attention to guide anomaly detection. First, each data point in the time series is transformed into a waveform graph, represented as a data graph representation (DGR), to capture key features such as temporal trends, periodicity, and abnormal points. By modeling multi-attribute time series data in parallel through the use of data graphs, the visual relationships, structures, and patterns among multi-attribute data are obtained. Second, our proposed approach jointly takes advantage of skip attention and range attention mechanisms to learn features from time series. Skip attention allows the model to capture dependencies by sampling at specified intervals, whereas range attention focuses on dividing the time series data within a specified time span, enabling the model to learn intricate features better. Third, the graph and data features are concatenated to form new features based on the new data generated from the self-attention mechanism, and then, anomalies are detected by evaluating the reconstruction error between the ground-truth time series data and the generated data. Finally, the experimental results demonstrate that the proposed approach outperforms the baseline methods, highlighting its ability to detect anomalies in industrial time series.