Acoustic monitoring method for mechanical faults based on accumulative real time series-parallel transformation algorithm
Aiming at the problem of stamping machine fault monitoring based on the Internet of Things(IoT),in order to reduce the computational complexity of stamping machine fault monitoring and improve the accuracy of low-frequency identification,a real-time mechanical fault acoustic monitoring method without machine learning technology was proposed,which was based on the cumulative real-time series-parallel transformation algorithm for mechanical fault acoustic monitoring.Firstly,the necessity of acoustic low-frequency analysis of stamping machine tool in the scene of Internet of Things was studied,and the expression of acoustic signal was given.Then,aiming at the difficulty of parameter estimation caused by the overlapping of multiple periodic signals on the frequency axis,an accumulative real-time serial-parallel transformation algorithm was proposed.The input sampling sequence was fed into a plurality of serial-parallel converters with different output ports,and the maximum absolute value was detected and compared from the accumulated waveforms.Finally,by dividing the sample time slots,the cumulative real-time serial-parallel transformation algorithm was applied to mechanical fault monitoring.The feasibility of the proposed algorithm in mechanical acoustic monitoring and real-time mechanical fault acoustic monitoring method were verified by simulation and real machine test of stamping machine.The research results show that the cumulative real-time serial-parallel transformation algorithm is beneficial to improve the identification accuracy of low-frequency band without a large number of signal samples.In the aspect of histogram correlation,the cumulative real-time series-parallel transform algorithm and Morlet wavelet transform have the same performance,and both are obviously better than short-time Fourier transform.At the same time,although the cumulative real-time serial-parallel transform algorithm needs 2.5 times more additions than Morlet wavelet transform,the total number of multiplications is reduced by20 447%,which greatly reduces the computational complexity.
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维普
