Under　actual　operating　conditions,　vibration　signals　of　rotating　machinery　often　contain　complex　close-spaced　components　and　strong　background　noise,　which　increases　the　difficulty　of　intrinsic　chirp　component　decomposition　(ICCD)　to　extract　the　fault　characteristic　components　of　rotating　machinery.　To　tackle　the　above　problem,　a　novel　method,　named　single-trend　component　extraction　(STCE),　is　developed　in　this　article.　First,　a　new　decomposition　framework　is　proposed　by　adopting　a　new　penalty　term　designed　in　consideration　of　the　low　variation　characteristic　of　instantaneous　amplitudes　to　modify　the　optimization　function　of　the　ICCD,　which　improves　the　efficient　distribution　of　energy　between　close-spaced　components.　Second,　an　instantaneous　frequency　(IF)　estimation　theory　is　proposed　to　obtain　the　IFs　of　the　signal.　Finally,　a　time–frequency　representation　with　high　energy　concentration　is　obtained　to　reveal　fault　characteristic　frequencies　of　rotating　machinery.　Both　the　simulation　and　experimental　cases　have　confirmed　the　productiveness　of　the　STCE　in　fault　diagnosis　of　rotating　machinery.　©　2025　Elsevier　Ltd