Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing

A production status monitoring method based on edge computing is proposed for traditional machining offline equipment to address the deficiencies that traditional machining offline equipment have, which cannot automatically count the number of parts produced, obtain part processing time information,...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2022-12, Vol.23 (1), p.402
Main Authors: Wang, Yuguo, Shen, Miaocong, Zhu, Xiaochun, Xie, Bin, Zheng, Kun, Fei, Jiaxiang
Format: Article
Language:English
Subjects:
calibration
current signal
Deep learning
Distributed control systems
edge computing
Energy consumption
Energy efficiency
Fault diagnosis
Interfaces
Internet of Things
Machining
Manufacturing
Monitoring
Monitoring systems
operational status
Process controls
Production management
real-time monitoring
Segments
Sensors
Signal processing
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Summary:A production status monitoring method based on edge computing is proposed for traditional machining offline equipment to address the deficiencies that traditional machining offline equipment have, which cannot automatically count the number of parts produced, obtain part processing time information, and discern anomalous operation status. Firstly, the total current signal of the collected equipment was filtered to extract the processing segment data. The processing segment data were then used to manually calibrate the feature vector of the equipment for specific parts and processes, and the feature vector was used as a reference to match with the real-time electric current data on the edge device to identify and obtain the processing start time, processing end time, and anomalous marks for each part. Finally, the information was uploaded to further obtain the part processing time, loading and unloading standby time, and the cause of the anomaly. To verify the reliability of the method, a prototype system was built, and extensive experiments were conducted on many different types of equipment in an auto parts manufacturer. The experimental results show that the proposed monitoring algorithm based on the calibration vector can stably and effectively identify the production information of each part on an independently developed edge device.
ISSN:1424-8220
1424-8220
DOI:10.3390/s23010402