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The real-time monitoring of events in an industrial plant is an advanced technique that presents the real conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes various rotating machinery condition monitoring techniques of the machine to determine the conditions of failure. To increase the operational reliability and to reduce preventive maintenance, it is necessary an efficient tool for analysis and process monitoring, in real time, enabling the detection of incipient faults for rolling bearings. Over the past few years there has been a major technological developments related to digital system, including innovations in both hardware and software. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades and expansions of the designed system. This paper presents a study of new design tools for embedded digital systems based on open hardware architecture with reconfigurable logic. Will be discussed a case study in the area of fault detection of rolling bearings, as well as its implementation and testing.

This paper deals with a top down design methodology to DSP algorithms based on FPGA. Additionally, DSP algorithms, such as FIR filters, fast Fourier transform (FFT), and the high-frequency resonance technique (HFRT), are implemented in the hardware embedded in the FPGA using a MATLAB/SIMULINK model. The principle idea of this approach is the advantage of a MATLAB/SIMULINK model created to be generic, flexible and can be easily changed at the end user. To validate our approach, an HFRT algorithm to identification the fault in rolling bearings is implemented in FPGA.