As　a　powerful　tool　of　mode　component　extraction,　variational　mode　decomposition　(VMD)　has　found　its　wide　application　in　fault　diagnosis.　However,　VMD　still　suffers　from　an　unsolved　problem.　To　be　specific,　how　to　adaptively　determine　the　decomposition　number,　the　initial　center　frequencies　and　the　variable　penalty　factors　in　VMD　according　to　the　characteristics　of　vibration　signal　is　still　an　unsolved　problem.　To　address　this　problem,　the　authors　propose　a　novel　mode　component　extraction　method,　namely,　the　power　information　guided-variational　mode　decomposition　(PIVMD).　The　essential　idea　of　the　proposed　method　is　using　the　enveloped　auto-regressive　(AR)　power　spectrum　of　raw　vibration　signal　to　determine　the　VMD　parameters　(decomposition　number,　initial　center　frequencies　and　variable　penalty　factors).　After　determining　these　VMD　parameters,　an　iterative　search　is　conducted　to　obtain　the　optimal　solution　for　constructed　variational　model.　A　noise　reduction　envelope　spectrum　is　further　used　to　enhance　the　weak　fault　features　contained　in　the　extracted　mode　component.　A　set　of　simulation　signals　representing　the　faulty　characteristics　of　rolling　bearing　with　inner/outer　race　faults　is　analyzed　by　the　proposed　PIVMD　method　to　test　its　mode　extraction　ability.　Then,　an　open　dataset　from　Case　Western　Reserve　University　and　a　measured　dataset　from　a　steel　mill　in　Ma　Steel　Co.　as　well　as　an　experimental　dataset　collected　from　the　authors＇　laboratory　are　analyzed　to　verify　the　fault　diagnosis　capability　of　the　proposed　PIVMD　method.　Meanwhile,　the　analyzed　results　are　compared　with　those　obtained　from　other　mode　decomposition　methods　to　highlight　the　superiority　of　the　proposed　PIVMD　method　in　terms　of　extracting　mode　components　with　rich　fault　information.　©　2022　Elsevier　Inc.