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    Development of a low-cost wireless accelerometer sensor platform (WASP) for machine monitoring applications

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    SALEEBY-THESIS-2019.pdf (38.92Mb)
    Date
    2019-04-26
    Author
    Saleeby, Kyle
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    Abstract
    The modern Industrial revolution, or Industry 4.0, has dramatically expanded the capabilities of digital manufacturing. However, modern machines with monitoring capabilities are extremely expensive to purchase and take years of operation to recoup the capital cost. A need exists to provide a low-cost Internet-of-Things for Manufacturing (IoT4MFG) sensor platform that can provide accurate monitoring and analysis capabilities on a machine of any age. The Wireless Accelerometer Sensor Platform (WASP) is an extremely low-cost, wireless, and robust solution to upgrade the monitoring capabilities of manufacturing machines to a modern standard. This platform provides a flexible capability to modularly handle analog and digital sensors of standard communication protocols, as well as a standard set of base sensors including an accelerometer. Additionally, optimal placement of accelerometers on a machine is important for the proper measurement of vibrations. Commercial sensors are commonly fastened to the machine through permanent means without proper verification of positional vibration acquisition. A need exists to verify proper placement and function of these sensors. The WASP implements a live vibration monitor with a mobile phone app through Bluetooth Low Energy communication. Combined with a semi-permanent magnetic mount design, the sensors platform allows for precise placement and convenient adjustment to ensure optimal vibration measurement. The design methodology and verification process pursued to develop the WASP are presented. A case study was completed to demonstrate the technology on a manual lathe where machine vibration was measured. The WASP accelerometer was evaluated for accuracy with a spectrum of known input frequencies and results were compared against a high quality baseline accelerometer.
    URI
    http://hdl.handle.net/1853/62703
    Collections
    • Georgia Tech Theses and Dissertations [23877]
    • School of Mechanical Engineering Theses and Dissertations [4086]

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