Abstract
In view of the higher requirements of modern machinery for multi-sensor information acquisition and fusion technology, this paper proposes a novel multi-fusion analytic mode decomposition (MFAMD) method to separate and demodulate fault features in signals. In low-speed and heavy-load equipment, the signals collected by multiple sensors contain unknown and unequal fault features and interference. Quaternion-based frequency domain fusion technology and analytically based modal extraction technology can offer novel approaches to processing large data sets in parallel while handling lengthy signals and high sampling rates. The trend spectrum segmentation method based on quaternions optimizes the hysteresis of the binary frequency. The experimental signal verifies that the proposed method is suitable for low-speed and heavy-load bearing faults.