A local modulation signal bispectrum for multiple amplitude and frequency modulation demodulation in gearbox fault diagnosis

Abstract

This paper proposes a novel multiple amplitude modulation and frequency modulation (AM–FM) demodulation method based on local modulation signal bispectrum (LMSB), which can demodulate the fault features of different components from the gearbox signal with multi-mesh frequency bands and multi-modulation components. Firstly, the collected measurement signal is decomposed into a series of sub-band signals, and the generated sub-band signals are demodulated by modulation signal bispectrum (MSB), which realizes the simultaneous demodulation of multi-mesh frequency bands and multi-modulation components. Subsequently, each sub-band signal resulting from MSB demodulation is synchronously averaged to generate the LMSB, which removes random noise and interference frequencies from the sub-band signals. Finally, the periodic modulation intensity (PMI) is utilized to evaluate the ability of the LMSB to demodulate multi-component signals with AM–FM components. The effectiveness of LMSB in processing multi-component signals with AM–FM components is demonstrated by numerical simulation signals and experimental analysis. Analysis results demonstrate the superiority of LMSB in multiple AM–FM demodulation compared with MSB, envelope analysis (EA) and Teager energy operator (TEO). This research provides a novel perspective for gearbox fault detection.

Enhancing response estimation and system identification in structural health monitoring through data-driven approaches

“…This is accomplished via the application of the Nonlinear Least Squares method coupled with the Trust-Region algorithm. 34,35 This combined framework furnishes the means to compute all parameters associated with the fitting model. Notably, the focal dataset employed in this study comprises temporal vibration and acoustic measurements stemming from a building subjected to unknown excitation.…”

“…30 Additionally, intelligent fault diagnosis has been continuously concerned using different advanced techniques such adaptive variational mode extraction method, 31 data-driven dictionary learning method, 32 Synchroextracting frequency chirplet transform, 33 and data reconstruction methods. 6,34 This study introduces a simplified data-driven approach, incorporating ML-based techniques, to identify unknown vibrations in a multi-story building. Section 2 provides a brief overview of the forced vibration formulation, while Section 3 delves into the core concept and methodology of the proposed approach.…”

“…30 Additionally, intelligent fault diagnosis has been continuously concerned using different advanced techniques such adaptive variational mode extraction method, 31 data-driven dictionary learning method, 32 Synchroextracting frequency chirplet transform, 33 and data reconstruction methods. 6,34…”