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.