Machine monitoring is one of the major concerns in modern industry in order to guarantee the overall efficiency during the production process. Several monitoring techniques for machinery failure detection have been developed, being vibration analysis one of the most important techniques. The typical equipment used for vibration analysis is a general purpose single channel spectrum analyzer that most of the cases is not well suited for the specific task and lacks from the capability of simultaneous multiple channel analysis and it is not specifically designed for vibration analysis. The contribution of this work is to present the development of a low-cost FPGA based 3-axis simultaneous vibration analyzer for embedded machinery monitoring with the novelty of a post-processing stage that can be designed and implemented into the same FPGA for automatic on-line detection of specific machinery failures thanks to its reconfigurability. The vibration analyzer has three stages: vibration monitoring with a MEMS accelerometer as sensor, three parallel 1024-point FFT cores and one post-processor for the analysis of the specific vibration related failure, which can be reconfigured to attend the specific task. One of the most important failures in induction motors is the broken bar condition and the developed vibration analyzer was tested to detect this condition on several motors for different failure severities, giving good detection results. Other vibration analysis can be performed by the three channel FFT cores with the reconfiguration of the post-processing unit to detect or enhance a specific characteristic under study

Preventive maintenance is one of the major concerns in modern industry where failure detection on motors increases the useful life cycle on the machinery. Bar breakage is one of the most common failures on motors and their condition monitoring is a mandatory task for industries. Previous works for bar breakage detection are based on off-line current or vibration analysis through the spectrum, but their detectability is compromised under certain operating conditions. The novelty of this work is the proposal of a correlation algorithm that combines current and vibration spectra to enhance detectability where other works fail. Current and vibration monitoring are non-invasive methods and they are preferred for on-line analysis. The proposed correlation between current and vibration allows to detect the broken bar condition on induction motors when no load is applied to the motor, which is the condition where other reported works fail to detect. The contribution of this work is that the proposed algorithm, though complex in computational load, is implemented into a low-cost FPGA (Spartan-3 XC3S1000) that gives a special purpose SOC solution for on-line operation, thanks to the development of a special purpose hardware signal processing unit. The developed FPGA based algorithm was tested for different bar breakage conditions in the induction motors and with several loads, giving as a result a better detectability for the motor failure under conditions where other methodologies fail, with the additional advantage of being a low-cost SOC solution for on-line detection