# Identification of Defects in Low-Speed and Heavy-Load Mechanical Systems Using Multi-Fusion Analytic Mode Decomposition Method

## TL;DR

This paper introduces a new method to detect faults in low-speed, heavy-load machinery using advanced signal processing techniques.

## Contribution

The novel multi-fusion analytic mode decomposition method improves fault detection in complex mechanical systems.

## Key findings

- The MFAMD method effectively separates fault features from sensor signals.
- The method handles large datasets and high sampling rates efficiently.
- Experiments confirm its suitability for detecting bearing faults in low-speed systems.

## 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.