DMWMN: A Deep Modulation Network for Gearbox Intelligent Fault Detection Under Variable Working Conditions

Convolutional neural network (CNN) has shown great potential in real-time gearbox monitoring. In practical engineering, due to the complex multitooth meshing motions and variable working conditions resulting in gearboxes with multiple excitation sources, and the response signals exhibit amplitude modulation and frequency modulation characteristics, which makes it difficult for CNN to obtain the fault features from complex modulated signals. To tackle these challenges, this article presents a new deep multiscale weighted modulation network (DMWMN) for gearbox fault detection under variable working conditions. First, a new frequency-domain modulation spectrum is proposed as signal processing layer in DMWMN to demodulate the modulation features from complex vibration signals. Thereafter, multibranched structure with different DMWMN slice scales is utilized to obtain fault features. The frequency domain signal-to-noise ratio-based weighted fusion method is employed to optimize the weighted coefficients in DMWMN to enhance the fault feature components. Finally, a CNN is further employed to learn features from the demodulated signals in the DMWMN layer to identify for fault classification. Experimental results prove that the DMWMN has advantages over state-of-the-art algorithms for gearbox fault feature identification under variable working conditions.