STAKgram: a method for optimal demodulation band selection in bearing fault diagnosis under complex interference

Abstract Within rotating machinery, bearings constitute critical and vulnerable components. When they operate in a compromised state, they generate periodic shock pulses. While spectral kurtosis (SK) has been demonstrated as an effective tool for pulse detection, in complex operating environments, the vibration signals of damaged bearings acquired through sensors often contain a variety of interfering pulses, such as ambient background noise, episodic single pulses, which cause SK to produce poor computational results. In this paper, an innovative bearing fault diagnostic method named subband trimmed average kurtogram (STAKgram) is proposed, aiming to minimize the effect of interfering signals on signal processing results, thus ensuring reliable diagnostic results even in complex interference environments. Firstly, the signal is segmented into <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:math> sub-signals by employing a sliding window method. Following, the subband kurtosis is calculated for every sub-signal. Ultimately, the trimmed average kurtosis of the respective sub-band is computed to construct the STAKgram, indicating the optimal demodulation frequency band. It is used for squared envelope demodulation analysis. By simulation analysis and the application of test signals obtained under conditions closely resembling authentic bearing operating conditions, the STAKgram method is proven to provide more accurate results for diagnosing bearing faults in complex interference.