文章基本信息

标题：Product Quality Driven Auto-Prognostics: Low-Cost Digital Solution for SMEs
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作者：Amit Kumar Jain ; Maharshi Dhada ; Ajith Kumar Parlikad 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：3
页码：78-83
DOI：10.1016/j.ifacol.2020.11.012
语种：English
出版社：Elsevier
摘要：AbstractSetting out existing prognostics solutions in small and medium enterprises (SMEs) is accompanied by challenges. These include employing expensive sensors, acquisition systems; and attending geometric limitations. Additionally, these solutions call for a specialist to take on feature engineering, machine learning algorithm selection, etc. Presented in this paper is a low-cost digital solution (intelligently integrate cost-cutting off-the-shelf technologies) for SMEs via product quality driven auto-prognostics. First, we develop upon existing solutions by addressing their drawbacks viz. cost, geometric limitations via a new product quality-centered condition monitoring strategy. Every SME must investigate the quality of their products, and therefore the authors believe this to be a low-cost solution. Next, the proposed solution integrates automated machine learning via Auto-WEKA, an off-the-shelf open-source technology. Lastly, the practical advantages of the proposed solution over the existing sensor-based solution were investigated via a case study. Results depict that this low-cost prognostics solution is vital for maintenance planning in SMEs.
关键词：KeywordsPrognosticsqualitydigital manufacturinglow-cost solutionsautomated machine learning