Temporal anomaly detection on IIoT-enabled manufacturing

Along with the coming of industry 4.0 era, industrial internet of things (IIoT) plays a vital role in advanced manufacturing. It can not only connect all equipment and applications in manufacturing processes closely, but also provide oceans of sensor data for real-time work-in-process monitoring. Considering the corresponding abnormalities existing in these sensor data sequences, how to effectively implement temporal anomaly detection is of great significance for smart manufacturing. Therefore, in this paper, we proposed a novel time series anomaly detection method, which can effectively recognize corresponding abnormalities within the given time series sequences by standing on the hierarchical temporal representation. Extensive comparison experiments on the benchmark datasets have been conducted to demonstrate the superiority of our method in term of detection accuracy and efficiency on IIOT-enabled manufacturing.

Automated summary

With the advent of Industry 4.0 era, Industrial Internet of Things (IIoT) is playing a crucial role in advanced manufacturing. This study proposed a novel time series anomaly detection method that effectively recognizes anomalies within sensor data sequences by hierarchical temporal representation, demonstrating superior detection accuracy and efficiency in IIOT-enabled manufacturing through extensive comparison experiments.