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Title Developing a smart and low cost device for machining vibration analysis
URL
Publication Date
Date Accessioned
Degree MS
Discipline/Department Mechanical Engineering
Degree Level masters
University/Publisher Georgia Tech
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.
Subjects/Keywords Edge computing; Digital manufacturing; Industry 4.0; Machine learning; Vibration analysis; Kernel support vector machine; Linux kernel; Beaglebone black
Language en
Country of Publication us
Record ID handle:1853/60296
Repository gatech
Date Indexed 2020-05-13
Issued Date 2018-07-25 00:00:00
Note [degree] M.S.;

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…study. . . . . . . . . . . . . . . . . . . . . . . 34 4.2 The mechanical adaptor for fix the accelerometer on the band saw. . . . . . 35 4.3 The Beaglebone Black wireless. . . . . . . . . . . . . . . . . . . . . . . . 36 4.4 The cape for the…

…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 CortexA8 processor that runs at 1GHz. This…

…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…

…performed by different chips. Based on this observation, this work tries to implement a real-time data acquisition 1 Figure 1.1: The 4 industrial revolutions. [2] and processing solution on a BeagleBone Black micro-computer. The solution leads to…

…provided and sustained 10 by the boar or chip distributor. In the following the most well-known microprocessors are presented: The Raspberry Pi 3 B+ and the BeagleBone Black (wireless version). Figure 2.4: Architecture of a microprocessor…

BeagleBone Black is a low-cost community supported development platform distributed by the BeagleBoard foundation, project is totally open source, which means that all the schematics and components of the board can be found on line and bought separately. It…

…in the TI-am3358 chip. Connectivity is ensured by 44 In/Out pins, one high speed USB port and 8 analog inputs. The new version of the BeagleBone Black has seen its Ethernet port replaced by a 802.11 b/g/n 2.4 GHz WIFI with also Bluetooth 4.1 and…

…x5D;. The two microprocessors presented above illustrate well two different way to use microprocessors; the BeagleBone Black, thanks to its numerous In/Out pins and its Analog to Digital Converter, is more suitable for sensor and data acquisition…

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