Design of a self-diagnosis system for intelligent tobacco feeder

Shaolong Han; Yongqiang Gu; Shangrong Wang; Jianlin Zhang; Ruixu Han

doi:10.1117/12.3014806

4 March 2024 Design of a self-diagnosis system for intelligent tobacco feeder

Shaolong Han, Yongqiang Gu, Shangrong Wang, Jianlin Zhang, Ruixu Han

Proceedings Volume 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023); 129813E (2024) https://doi.org/10.1117/12.3014806
Event: 9th International Symposium on Sensors, Mechatronics, and Automation (ISSMAS 2023), 2023, Nanjing, China

Abstract

Traditional tobacco feeder is equipped with multiple types of sensors, with a large number. Due to the lack of effective technical solutions to detect the sensor status and provide early warnings, the feeding process of the tobacco feeder has many drawbacks such as a high failure rate and a serious impact on product quality improvement. This article proposes a design of an intelligent tobacco feeder system based on self-diagnosis technology. Firstly, all sensor information of the feeder is collected, analyzed, and processed. Then, self-diagnostic algorithms are used to detect, judge, and alert the status of each sensor. Finally, the system reminds relevant personnel to conduct timely inspections and repairs, and when necessary, the system will activate emergency control plans to ensure smooth production. Before production, the fault detection rate increased by 77.43%, during the production process, the fault detection rate increased by 666.87% and the fault rate decreased by 69.57%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shaolong Han, Yongqiang Gu, Shangrong Wang, Jianlin Zhang, and Ruixu Han "Design of a self-diagnosis system for intelligent tobacco feeder", Proc. SPIE 12981, Ninth International Symposium on Sensors, Mechatronics, and Automation System (ISSMAS 2023), 129813E (4 March 2024); https://doi.org/10.1117/12.3014806

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