|dc.description.abstract||Internet of Thing (IoT) is receiving an enormous attention especially when it comes to monitor machining operations. However, current technology must continue to evolve in order to reduce cost and to improve data analytics1. More importantly, IoT devices often raise security concerns, as they transfer a considerable amount of data to the cloud.
Simultaneously, the computational power of embedded platforms has increased, giving the ability to process data locally; thus, edge computing is able to reduce the security problem as they minimize the quantity of information transferred to the cloud. Therefore, these problems can be addressed by developing a truly smart low-cost device that takes advantage
of fog computing as opposed to cloud computing. Frameworks have been developed to demonstrate the capability to remotely monitor machine health using cloud computing, the objective of this thesis is to associate those frameworks to the computational power of low-cost embedded platforms to process data
locally and in real-time. For this work a BeagleBone Black is used. It is powered by an AM335x ARM Cortex-A8 processor that runs at 1GHz. This computer is associated with an analog accelerometer
through its Analog to Digital Converter. The system is monitoring vibrations on a bandsaw, as it is running Linux it does not have deterministic-sampling capabilities; therefore, the Industrial I/O subsystem is used to enable hardware interrupts on the Linux Kernel space. The vibrations generated by the cutting of different materials are recorded and used to train a machine learning algorithm on an external computer. Training will use a Kernel Support Vector Machine algorithm. Once the algorithms are trained they are will be implemented
locally on the BeagleBone Black so that the analytics of the data are done at the ”edge”. The final goal is to be able to determine the nature of the material that is being cut by the bandsaw.||