Field-Programmable Gate Array (FPGA) technology has been applied widely in electronic engineering and computing industries, but it has not had the same level of reception in other disciplines including mechanical engineering [1]. The purpose of this paper is to examine FPGA implementations of signal processing techniques that are used in the context of bearing condition monitoring. As the number of bearings can be large sparse sensor arrays are used to locate and detect their condition. The demands of realtime process monitoring [2] [3] can place a heavy burden upon the monitoring system. Field-Programmable Gate Array (FPGA) technology [4] in this application makes it possible to implement more sophisticated algorithms. These exploit its high-speed, parallel, reconfigurable architecture. Bring forth the advantages of FPGA technology to condition monitoring. The techniques covered are: cross-correlation, digital signal processing (DSP) Infinite Impulse Response (IIR) filters, neural networks and signature matching. The implemented designs are optimised for both execution time and the amount of logic area consumed. Results were obtained from each technique and were assessed and compared in terms of execution time and also the amount of logic consumed on the FPGA. Over the past 15 years FPGA technology has been applied extensively in electronic engineering but its scope has not been as vastly in mechanical engineering. The objective of this paper was to examine an application in mechanical engineering. Ideally this would be done with a mechanical engineering compatible approach, giving rise to a methodology, which would allow FPGA programming [5] to become a transferable skill.

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