In　practical　engineering,　the　presence　of　dependent　evidence　is　not　rare　due　to　various　imperfections.　Misuse　of　such　information　in　reliability　analysis　will　lead　to　conflicting　or　even　erroneous　results.　In　this　paper,　we　propose　a　Bayesian　reliability　approach　for　complex　systems　with　dependent　life　metrics.　Notions　such　as　explicit　evidence　and　implicit　evidence　are　established　based　on　an　identification　of　different　roles　of　multiple　dependent　evidence　in　the　likelihood　construction.　A　likelihood　decomposition　method　is　developed　to　convert　the　overall　likelihood　into　a　product　of　Explicit　Evidence-based　Likelihood　(EEL)　function　and　Implicit　Evidence-based　Likelihood　(IEL)　function.　An　inferential　diagram　is　developed　to　intuitively　generate　the　required　implicit　evidence　taking　both　outer-source　information　and　the　system　configuration　into　consideration.　An　algorithm　is　then　presented　for　implementation.　The　contribution　of　our　work　is　a　systematic　investigation　of　the　role　of　dependent　evidence　in　system　reliability　evaluation　and　a　full　Bayesian　approach　that　is　applied　to　various　system　reliability　models.　Extensive　numerical　cases　and　a　practical　engineering　case　are　demonstrated　for　validation　and　to　illustrate　the　benefits　of　our　approach.