Empowered by the advancement of smart sensing and machine learning algorithms, predictive maintenance has become one of the main value-adding schemes for industrial 4.0. Predictive maintenance goes beyond time-based preventive maintenance and condition-based maintenance. It harvests the power of industrial big data and reduces the risk of system failure in a proactive manner. To meet requirements of future industrial operations, the concept of predictive maintenance should be further explored and tested in the practical context. This workshop is organized for researchers who rally around the topic of predictive maintenance to overcome several existing challenges:
The list of topics includes, but is not limited to:
Authors are invited to submit original unpublished research papers as well as industrial practice papers. Simultaneous submissions to other conferences are not permitted. Detailed instructions for electronic paper submission, panel proposals, and review process can be found at https://qrs22.techconf.org/submission.
The length of a camera ready paper will be limited to six pages. Each paper should include a title, the name and affiliation of each author, a 150-word abstract, and up to 6 keywords.
All papers must conform to the QRS conference proceedings format (PDF | Word DOCX | Latex) and Submission Guideline set in advance by QRS 2022 co-located workshops. At least one of the authors of each accepted paper is required to pay full registration fee and present the paper at the workshop. Arrangements are being made to publish selected accepted papers in reputable journals. Submissions must be in PDF format and uploaded to the conference submission site.Submission
|Bin Liu||University of Strathclyde|
|Yiliu Liu||Norwegian University of Science and Technology|
|Huixing Meng||Beijing Institute of Technology|
|Rui Peng||Beijing University of Technology|
|Hui Xiao||Southwestern University of Finance and Economics|
|Xiujie Zhao||Tianjin University|