An Online System of Detecting Anomalies and Estimating Cycle Times for Production Lines
- Resource Type
- Conference
- Authors
- Ishizone, Tsuyoshi; Higuchi, Tomoyuki; Okusa, Kosuke; Nakamura, Kazuyuki
- Source
- IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society Industrial Electronics Society, IECON 2022 – 48th Annual Conference of the IEEE. :1-6 Oct, 2022
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Training
Meters
Energy consumption
Power demand
Neural networks
Estimation
Benchmark testing
anomaly detection
key production performance indicator
quasi-periodicity
attention mechanism
smart meter
- Language
- ISSN
- 2577-1647
Energy consumption data of production machines often exhibit quasi-periodicity, and anomalies are observed when deviations from the quasi-periodicity are detected. For such data, it is crucial to quickly estimate the individual cycles at each time point and detect abnormalities. In this study, we propose a system that satisfies these requirements. The proposed system trains a neural network with an attention mechanism and applies the weight vectors in the mechanism to the two tasks. Experimental results demonstrate that the proposed method outperforms benchmark methods for sensor data that mimic power consumption data of production lines.