Determining Proximity Warning and Action Zones for a Magnetic Proximity Detection System
Public Domain
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2011/10/01
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Series: Mining Publications
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Description:Researchers at the National Institute for Occupational Safety and Health (NIOSH) are developing intelligent software for use with electromagnetic proximity detection systems. The technology accurately locates workers around mining machines in real time. With the accurate locations of the workers around the equipment being known, their safety status can be evaluated. If a worker is located dangerously close to a machine, the machine can be partially or completely disabled to protect the worker from striking, pinning and entanglement hazards according to pre-defined logic. The technology is particularly applicable to mobile underground mining machines which offer difficult safety challenges in that operators generally work in close proximity to these machines in very restricted spaces. With use of the intelligent proximity detection system, nuisance alarms and failures to alarm are also expected to be sharply reduced. An effective proximity warning and action zone scheme is necessary for safe implementation and will improve the acceptance of a magnetic proximity detection system by underground workers. [Description provided by NIOSH]
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ISBN:9781424494989
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ISSN:0197-2618
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NIOSHTIC Number:nn:20039813
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Citation:2011 IEEE Industry Applications Society Annual Meeting: Forty-sixth IAS Annual Meeting, October 9-13, 2011, Orlando, Florida. Piscataway, NJ: Institute of Electrical and Electronics Engineers, 2011 Oct; :641-647
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Contact Point Address:Christopher Jobes, National Institute for Occupational Safety and Health, Pittsburgh Research Laboratory, P.O. Box 18070, 626 Cochrans Mill Road, Pittsburgh, PA 15236
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Email:Christopher.Jobes@cdc.hhs.gov
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Federal Fiscal Year:2012
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Peer Reviewed:False
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Source Full Name:2011 IEEE Industry Applications Society Annual Meeting: Forty-sixth IAS Annual Meeting, October 9-13, 2011, Orlando, Florida
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Main Document Checksum:urn:sha-512:61b1af1ec8860dc550cca73a1b926d847cf74b95461de21b0e7176052ece6414c596f2c0778193f6a4663727f2324629a4c7693c4e26ac895d1d78ff1cd3c83e
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