Adaptive　intelligent　fault　diagnosis　technology　can　improve　the　automatic　status　monitoring　capability　and　the　success　rate　of　fault　diagnosis.　This　article　proposed　an　adaptive　mode　decomposition　and　noise　reduction　algorithm　(VAMD)　that　use　a　variable　spectral　segmentation　framework　to　optimize　analytical　mode　decomposition　(AMD)　to　automatically　decompose　the　mode　information　in　rotating　machinery　signals.　The　framework　relies　on　the　variability　of　the　window　width　and　envelope　estimation　characteristics　of　the　order　statistics　filter　(OSF)　to　increase　the　diversity　of　the　center　frequencies　and　bandwidth.　A　novel　harmonic　correlation　index　(HCI)　is　designed　to　identify　the　characteristics　of　rotating　machinery　faults　from　various　levels　of　results　and　improve　the　usability　in　mechanical　equipment　fault　diagnosis.　The　proposed　method　has　successfully　achieved　fault　diagnosis　of　rolling　bearing.