Internet of Things – A Predictive Maintenance Tool for General Machinery, Petrochemicals and Water Treatment
نویسندگان
چکیده
Improper and unnecessary maintenance actions can result in a waste of resources, time, and money. Training and education play an important role in the successful implementation of a maintenance strategy. The Center for Predictive Maintenance (CPM) at the University of South Carolina has developed a demonstration methodology and tool that can be used to educate and train users extending from maintainers to leadership on the maintenance process from fault to maintenance action. This overall methodology has been developed so that it can be applied to a variety of industries including general machinery, petroleum and petro chemicals, and water treatment. All of these industries have a need for successful implementation of predictive maintenance programs and this demonstration methodology can be used to train and educate users. This demonstration tool will walk an audience through the maintenance process starting with the collection of sensor and historical data. Then it will show the analysis of the data through modeling and statistical analysis techniques. Finally, the data and results are displayed in unique dashboards that provide personnel with the information needed to make educated decisions on the condition and maintenance of their system. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Global Science and Technology Forum Pte Ltd
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