Joint extraction-based multivariate chirp mode decomposition and its application to fault diagnosis of rotating machinery under variable speed conditions

Abstract

Vibration signals from rotating machinery during variable speed conditions exhibits non-stationary characteristics, posing a challenge for fault diagnosis. When a machine fails, the vibration signal of any component connected to the bearing carries valuable information about the equipment’s status. Processing multichannel variable speed signals at the same time avoids losing part of information and improves fault diagnosis. Therefore, this study put up a novel approach called joint extraction-based multivariate chirp mode decomposition (JMCMD) to analyze multichannel signals of rotating machinery with varying speeds. The approach comprises the joint extraction framework and the multichannel signal instantaneous frequency initialization (MSIFI). First, JMCMD employs a joint component extraction framework, which can precisely and simultaneously extract complicated characteristic components close to each other in multiple channels. Second, this study extends the parameterized demodulation (PD) technique to multiple channels to find the initialized instantaneous frequency of multichannel signals. Through the joint extraction strategy’s integration with MSIFI, JMCMD is capable of efficaciously extracting all significant characteristic components of multichannel signals. Additionally, the high-resolution time–frequency representation (TFR) constructed from JMCMD’s output can clearly reveal the time-varying fault characteristic frequency of the rotor-bearing system. Simulation analysis and actual cases demonstrate that the suggested approach is an effective way to identify fault feature components, making it highly applicable in signal decomposition and fault detection.