Abstract
This paper introduced an adaptive stochastic resonance (AdSR) signal processing technique to extract fault feature of machining accuracy decay in boring and milling machine providing a vibration time-frequency distribution with adaptable precision. The AdSR uses a correlation coefficient of the input signals and noise as a weight to construct the weighted kurtosis (WK) index. The influence of high frequency noise is alleviated and the index used in traditional SR is improved accordingly. The AdSR with WK can obtain optimal parameters adaptively. In addition, through the secondary utilization of noise, AdSR makes the signal output waveform smoother and the fluctuation period more obvious. It has been found that AdSR appears to be a better tool compared to fast Fourier transform for fault characterization extraction in boring and milling machine in experiment case. It has been concluded that AdSR based signal processing technology successfully diagnosis the fault of machining accuracy decay in three-axis boring and milling machine.
Highlights
The three-axis boring and milling machine are key devices in the modern manufacturing industry
The study of large parameter stochastic resonance methods become necessary, and several achievements have been obtained during the past few years, such as modulated stochastic resonance (MSR) [16], rescaling frequency stochastic resonance (RFSR) [17], frequency-shifted and re-scaling stochastic resonance (FRSR) [18] and so on
When the amplitude of the periodic signal is smaller than the noise intensity, detection effect of the input signal using single stochastic resonance is not satisfactory, and the output signal still contains a certain amount of noise and the feature of the useful signal is not significant
Summary
The three-axis boring and milling machine are key devices in the modern manufacturing industry. It is difficult to detect fault features because the structures of three-axis boring and milling machines are complex The factors such as the influence of transmission path, the transmission medium, the ambient environment, etc., degrade the measured signals. The study of large parameter stochastic resonance methods become necessary, and several achievements have been obtained during the past few years, such as modulated stochastic resonance (MSR) [16], rescaling frequency stochastic resonance (RFSR) [17], frequency-shifted and re-scaling stochastic resonance (FRSR) [18] and so on All of these non-classical SR methods have greatly enlarged its application areas. In order to further improve the detection effect of the weak signals, stochastic resonance enhancement methods have been studied, such as cascade stochastic resonance [23], coupled stochastic resonance [13] and so on. The effectiveness of the proposed method is confirmed by the application result
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