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基于人機共融的包裝機故障診斷系統的設計與實(shí)現
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1.河南中煙黃金葉生產(chǎn)制造中心 河南 鄭州 京東數智工業(yè)科技有限責任公司 北京 杭州首域萬(wàn)物互聯(lián)有限責任公司 浙江 杭州;2.京東工業(yè)

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TP277

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Packing Machine Fault Diagnosis System based on Human Machine Integrated Approach
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    摘要:

    隨著(zhù)工業(yè)4.0和物聯(lián)網(wǎng)時(shí)代的來(lái)臨,基于經(jīng)驗和手冊的設備維修方式已不能滿(mǎn)足復雜設備維修的要求。而傳統的設備診斷系統往往只注重從物理傳感器采集數據,缺少引入人的經(jīng)驗,難以擁有自學(xué)習能力。本文以ZB45煙草包裝機為例,提出一種具有自學(xué)習能力的人機共融新型故障診斷系統。系統采用貝葉斯網(wǎng)絡(luò ),實(shí)現對傳感器數據的自動(dòng)推理。通過(guò)自然語(yǔ)言處理模塊與用戶(hù)交互,學(xué)習用戶(hù)的維修經(jīng)驗,并用來(lái)改進(jìn)診斷效果。提出了基于凸優(yōu)化的標簽選擇方法,根據觀(guān)察到的現象推薦合適的標簽,以快速確定最可能的故障,實(shí)現快速找到報警號碼對應的故障源。生產(chǎn)現場(chǎng)的實(shí)測數據表明,本系統可以有效降低萬(wàn)箱故障次數,有效提高故障診斷精度,降低故障排查時(shí)間。

    Abstract:

    With the advent of Industry 4.0 and the Internet of Things era, equipment maintenance methods based on experience and manuals can no longer meet the requirements of complex equipment maintenance. However, traditional equipment diagnosis systems often only focus on collecting data from physical sensors, lacking the experience of introducing the human factors, and it is difficult to have self-learning capabilities. Taking ZB45 tobacco packaging machine as an example, a new man-machine integrated fault diagnosis system with self-learning ability is proposed. The system uses Bayesian network to realize automatic reasoning of sensor data. Through the natural language processing module to interact with the user, learn the user"s maintenance experience, and use it to improve the diagnosis effect. A label selection method based on convex optimization is proposed, and suitable labels are recommended according to the observed phenomena to quickly determine the most probable faults, and to quickly find the fault source corresponding to the alarm number. The measured data on the producing spot shows that the system can effectively reduce the number of faults for every 10000 boxes, effectively improve the accuracy of fault diagnosis and reduce the troubleshooting time.

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張明琰,宋震,方世杰.基于人機共融的包裝機故障診斷系統的設計與實(shí)現計算機測量與控制[J].,2022,30(6):23-31.

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  • 收稿日期:2021-11-26
  • 最后修改日期:2021-12-22
  • 錄用日期:2021-12-31
  • 在線(xiàn)發(fā)布日期: 2022-06-21
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