This　paper　proposes　a　novel　online　sparse　least　square　support　vector　regression　without　bias　for　forecasting　capacitive　type　pressure　transducer　remaining　useful　life　prediction　(RUL).　The　proposed　approach　is　based　on　mechanism　and　statistical　knowledge.　The　impedance　variables　are　applied　to　calibrate　the　correlation　between　pressure　capacitance　capacity　degradation　and　the　resistance　value　to　improve　the　prediction　precision.　Furthermore,　a　particle　filter　algorithm　is　proposed　to　estimate　the　pressure　capacitance　impedance　fade　parameters,　and　then　the　probability　density　function　of　the　predicted　RUL　value　is　obtained　by　Gaussian.　Lastly,　experimental　results　consider　#5　and　#6.　The　pressure　capacitance　data　set　verifies　the　hybrid　RUL　prognostics　strategy　with　high　accuracy　and　robust　stability.　©　2025　Institute　of　Physics　Publishing.　All　rights　reserved.