Bearing Fault Diagnosis And Fpga Realization Based On Morphological Filter And Envelope Analysis

Precision of various types of mechanical equipment is higher and higher, due to the developing requirements of mechanical modernization and intelligence. Once some faults without being noticed appear, the bearing which is one of the most critical parts of equipment will cause serious damage to the entire facility Therefore, a more rapid and more efficient means of real-time monitoring to detect the fault as soon as possible is the primary problem.In this paper, we discusses the proposition from two aspects of algorithm and hardware foundation. For purpose of meeting the requirements of speed and accuracy, FPGA is adopted as the processor. Bearing diagnosis system is constructed with Mathematical Morphology Filter and Envelopment Analysis and there are significant speed advantages in this fault diagnosis system after using relevant data to verify this system we design. This paper is mainly to realize rolling bearing fault diagnosis algorithm based on FPGA. According to the overall framework, the basic principle of Mathematical Morphology Filter, Hilbert Transform and FFT are studied chapter by chapter. Meanwhile their realization based on FPGA device are also studied in turn. There is natural parallel implementation structure in Mathematical Morphology Filter and it mainly contain Boolean Operation. So it’s advantages in speed are very significant compared with Wavelet Analysis, Empirical Mode Decomposition and so on. Through FFT transform, the signal will be launched respectively in time domain and frequency domain. Once the bearing occurred fault, the system could clearly capture the fault frequency.Subsequently, the above algorithm were combined with FPGA device. Then we construct rapid bearing diagnosis system completely with DSP Builder and Quartus II. And software simulation was done to test it’s operation accuracy. In the aspect of hardware, we adopt Altera company’s Stratix? IV GX FPGA(EP4SGX230C2) for debugging. Generally speaking, most of the traditional facility fault diagnosis are based on PC or DSP processor, so these equipments don’t have the advantage of processing speed. Experimental results show that using this method with high speed could provide a guarantee for the speediness of whole rolling bearing fault diagnosis system.Finally, much sets of inner and outer fault data which come from Gamesa 850 wind turbines were used to conduct an hardware-level verification. The result shows that the FPGA system on chip can capture the fault frequency accurately and rapidly. Compare the result from Modelsim simulation with the result of Matlab simulation, we found that the scheme is accurate and reliable.