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Zedboard based platform for condition monitoring and control experiments

New methods for monitoring the condition of roller element bearings in rotating machinery offer possibilities to reduce repair- and maintenance costs, and reduced use of environmentally harmful lubricants. One such method is sparse representation of vibration signals using matching pursuit with dictionary learning, which so far has been tested on PCs with data from controlled tests. Further testing requires a platform capable of signal processing and control in more realistic experiments. This thesis focuses on the integration of a hybrid CPU-FPGA hardware system with a 16-bit analog-to-digital converter and an oil pump, granting the possibility of collecting real-time data, executing the algorithm in closed loop and supplying lubrication to the machine under test, if need be. The aforementioned algorithm is implemented in a Zynq-7000 System-on-Chip and the analog-to-digital converter as well as the pump motor controller are integrated. This platform enables portable operation of the matching pursuit with dictionary learning in the field under a larger variety of environmental and operational conditions, conditions which might prove difficult to reproduce in a laboratory setup. The platform developed throughout this project can collect data using the analog-to-digital converter and operations can be performed on that data in both the CPU and the FPGA. A test of the system function at a sampling rate of 5 kHz is presented and the input and output are verified to function correctly.

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-70105
Date January 2018
CreatorsAdrielsson, Anders
PublisherLuleå tekniska universitet, Institutionen för system- och rymdteknik
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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