A real-time decision-making of maintenance using fuzzy agent |
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| Authors: | Kun-Yung Lu Chun-Chin Sy |
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| Affiliation: | 1. Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA;2. Department of Neurosurgery, Rush University Medical Center, Chicago, Illinois, USA;3. Department of Orthopedic Surgery, Washington University in St. Louis, St. Louis, Missouri, USA;1. Department of Orthopaedic Surgery, NYU Langone Medical Center, New York, NY, USA;2. School of Medicine, Wayne State University, 540 E Canfield St, Detroit, MI 48201, USA;3. Spine Service, Hospital for Special Surgery, 535 E 70th St, New York, NY 10021, USA;4. Orthopaedic Surgery, Rocky Mountain Scoliosis and Spine, 2055 High St, Denver, CO 80205, USA;5. Orthopaedic Surgery, University of California San Francisco, San Francisco, CA, USA;6. Neurosurgery, University of California San Diego School of Medicine, 9500 Gilman Dr, La Jolla, CA 92093, USA;7. Neurosurgery, University of California San Francisco, San Francisco, CA, USA;8. American Jewish Joint Distribution Committee, New York, NY, USA;9. Research, FOCOS Orthopedic Hospital, Teshie St, Pantang, Accra, Ghana;10. Orthopedics, FOCOS Orthopedic Hospital, Teshie St, Pantang, Accra, Ghana;11. International Spine Study Group Foundation, 15480 Iola St, Brighton, CO 80602, USA;1. Department of Chemical and Biological Engineering, University of Wisconsin-Madison, United States;2. Johnson Controls, Inc., Milwaukee, WI, United States |
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| Abstract: | In many manufacturing processes, real-time information can be obtained from process control computers and other monitoring devices. However, production control problems are frequently accompanied by certain and uncertain conditions. Problems with uncertainty conditions generally include difficulty in identifying an optimal solution in real-time using conventional mathematical approaches. This study presents a fuzzy logic approach for decision-making of maintenance. Some linguistic variables and rules-of-thumb are used to form the fuzzy logic models, based on the domain experts’ experiences in production line and maintenance department. The historical production data are used to train and tune the fuzzy models. The tuned fuzzy models are then embedded into an internet-based and event-oriented information system as fuzzy agent. The production controller can easily make suitable production control decisions based on the inference results of fuzzy agents to satisfy the quick response requirement. |
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