A　generalised　Warblet　tensor　rank-1　decomposition　(GWTTR1)　method　is　developed　in　this　study　to　realise　location　diagnosis　for　bearing　outer　raceway　defects　in　low　signal-to-noise　ratio　(SNR)　scenarios.　Firstly,　a　novel　third-order　tensor　model　(channel-time-frequency)　with　comprehensive　information　is　established　to　reveal　key　information　hidden　in　multidimensional　signals　and　realise　feature　fusion　among　two-channel　signals.　Secondly,　the　defect　characteristic　frequency　and　correlation　coefficient　between　rank-1　tensors　are　introduced　as　optimal　selection　indices　for　the　factor　matrix　to　decompose　the　tensor　model　effectively　and　overcome　the　inherent　shortcomings　of　decomposition.　Thirdly,　a　novel　location　diagnosis　dimensionless　index,　namely,　the　horizontal-vertical　synchronisation　factor　(HVSF),　is　proposed　for　the　optimised　tensor　model.　Finally,　the　performance　of　the　proposed　GWTTR1　method　and　novel　HVSF　index　in　location　diagnosis　for　bearing　outer　raceway　defects　and　noise　interference　elimination　is　evaluated　comprehensively　with　low　SNR　dynamic　simulation　and　experimental　signals.　The　comparison　results　reveal　that　the　GWTTR1　method　outperforms　existing　noise　reduction　techniques.