Rolling　bearings　are　critical　components　in　modern　mechanical　equipment,　and　the　health　monitoring　and　predictive　maintenance　of　bearings　are　crucial　for　the　normal　operation　of　machinery.　Hence,　there　is　a　compelling　need　to　delve　into　advanced　methodologies　for　enhancing　the　detection　of　fault　characteristics　in　bearings.　Faulty　bearings　produce　periodic　impulses　during　constant-speed　rotation,　which　can　typically　be　detected　through　envelope　analysis.　However,　in　some　complex　conditions,　the　relevant　fault　frequencies　may　be　hidden　within　interfering　components.　This　paper　presents　an　amplitude　modulation　technique　called　the　hyperbolic　tangent　Gaussian　(HTG)　transformation,　designed　to　extract　weak　fault　components　from　signals.　Firstly,　a　family　of　amplitude　modulation　functions,　known　as　the　HTG　functions,　is　constructed.　These　functions　modulate　signals　with　normalized　amplitudes　to　obtain　a　series　of　modulated　signals.　Simultaneously,　a　frequency　domain　amplitude　ratio　metric　is　used　for　the　automatic　selection　of　the　optimal　components.　Finally,　the　HTGgram　is　introduced,　a　spectral　decomposition　method　based　on　trend　components,　aiming　to　identify　the　best　combination　of　filtering　and　modulation　components.　Simulations　with　multi-component　bearing　fault　signals　and　experimental　signals　with　composite　bearing　faults　demonstrate　that　this　method　not　only　highlights　fault　features　and　suppresses　noise　interference　but　also　adaptively　selects　frequency　bands　related　to　faults,　enhancing　fault　information.　This　approach　exhibits　excellent　adaptability　and　effectiveness　in　complex　operating　conditions　with　multiple　interference　components.