Intelligent Maintenance Systems and Predictive Manufacturing

【Author】 Lee, Jay; Ni, Jun; Singh, Jaskaran; Jiang, Baoyang; Azamfar, Moslem; Feng, Jianshe

【Source】JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME

【影响因子】3.952

【Abstract】With continued global market growth and an increasingly competitive environment, manufacturing industry is facing challenges and desires to seek continuous improvement. This effect is forcing manufacturers to squeeze every asset for maximum value and thereby calls for high-equipment effectiveness, and at the same time flexible and resilient manufacturing systems. Maintenance operations are essential to modern manufacturing systems in terms of minimizing unplanned down time, assuring product quality, reducing customer dissatisfaction, and maintaining advantages and competitiveness edge in the market. It has a long history that manufacturers struggle to find balanced maintenance strategies without significantly compromising system reliability or productivity. Intelligent maintenance systems (IMS) are designed to provide decision support tools to optimize maintenance operations. Intelligent prognostic and health management tools are imperative to identify effective, reliable, and cost-saving maintenance strategies to ensure consistent production with minimized unplanned downtime. This article aims to present a comprehensive review of the recent efforts and advances in prominent methods for maintenance in manufacturing industries over the last decades, identifying the existing research challenges, and outlining directions for future research.

【Keywords】intelligent maintenance systems; prognostics and health management; maintenance scheduling; system bottleneck; stream of variations; E-manufacturing; industry 4; 0; Internet of things; big data; cloud computing; fog computing; cyber-physical system; digital twin; industrial AI; blockchain technology; inspection and quality control; machine tool dynamics; plant engineering and maintenance; sensing; monitoring; and diagnostics; sensors

【发表时间】2020 1-Nov

【收录时间】2022-01-02

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