Health prognosisforequipmentisconsi

Health prognosisforequipmentisconsideredasakeyprocessofthecondition-based
maintenance strategy.Thispaperpresentsanintegratedframeworkformulti-sensor
equipmentdiagnosisandprognosisbasedonadaptivehiddensemi-Markovmodel
(AHSMM).Unlikehiddensemi-Markovmodel(HSMM),thebasicalgorithmsinan
AHSMM arefirstmodifiedinorderfordecreasingcomputationandspacecomplexity.
Then, themaximumlikelihoodlinearregressiontransformationsmethodisusedtotrain
the outputanddurationdistributionstore-estimateallunknownparameters.The
AHSMM isusedtoidentifythehiddendegradationstateandobtainthetransition
probabilities amonghealthstatesanddurations.Finally,throughtheproposedhazard
rateequations,onecanpredicttheusefulremaininglifeofequipmentwithmulti-sensor
information.Ourmainresultsareverifiedinrealworldapplications:monitoringhydraulic
pumpsfromCaterpillarInc.Theresultsshowthattheproposedmethodsaremore
effectiveformulti-sensormonitoringequipmenthealthprognosis.

ผลลัพธ์ (ไทย) 1:

สุขภาพโดย prognosisforequipmentisconsideredasakeyprocessoftheconditionกลยุทธ์การบำรุงรักษา เซ็นเซอร์ Thispaperpresentsanintegratedframeworkformultiequipmentdiagnosisandprognosisbasedonadaptivehiddensemi-Markovmodel(AHSMM) Unlikehiddensemi Markovmodel (HSMM), thebasicalgorithmsinanAHSMM arefirstmodifiedinorderfordecreasingcomputationandspacecomplexityแล้ว themaximumlikelihoodlinearregressiontransformationsmethodisusedtotrainoutputanddurationdistributionstore-estimateallunknownparameters ที่AHSMM isusedtoidentifythehiddendegradationstateandobtainthetransitionกิจกรรม amonghealthstatesanddurations ในที่สุด throughtheproposedhazardrateequations, onecanpredicttheusefulremaininglifeofequipmentwithmulti-เซ็นเซอร์ข้อมูล Ourmainresultsareverifiedinrealworldapplications:monitoringhydraulicpumpsfromCaterpillarInc.Theresultsshowthattheproposedmethodsaremoreeffectiveformulti-sensormonitoringequipmenthealthprognosis

ผลลัพธ์ (ไทย) 2:

สุขภาพ prognosisforequipmentisconsideredasakeyprocessofthecondition
ที่ใช้ในการบำรุงรักษา arefirstmodifiedinorderfordecreasingcomputationandspacecomplexity. จากนั้น themaximumlikelihoodlinearregressiontransformationsmethodisusedtotrain outputanddurationdistributionstore-estimateallunknownparameters.The AHSMM isusedtoidentifythehiddendegradationstateandobtainthetransition ความน่าจะเป็น

ผลลัพธ์ (ไทย) 3:

prognosisforequipmentisconsideredasakeyprocessofthecondition จากสุขภาพstrategy.thispaperpresentsanintegratedframeworkformulti-sensor การบํารุงรักษาequipmentdiagnosisandprognosisbasedonadaptivehiddensemi แบบจำลอง( ahsmm ) unlikehiddensemi แบบจำลอง ( hsmm ) thebasicalgorithmsinanahsmm arefirstmodifiedinorderfordecreasingcomputationandspacecomplexity .แล้ว themaximumlikelihoodlinearregressiontransformationsmethodisusedtotrainการ outputanddurationdistributionstore-estimateallunknownparameters.theahsmm isusedtoidentifythehiddendegradationstateandobtainthetransitionความน่าจะเป็น amonghealthstatesanddurations สุดท้าย throughtheproposedhazardrateequations onecanpredicttheusefulremaininglifeofequipmentwithmulti , เซ็นเซอร์ข้อมูล ourmainresultsareverifiedinrealworldapplications : monitoringhydraulictheresultsshowthattheproposedmethodsaremore pumpsfromcaterpillarinc .effectiveformulti sensormonitoringequipmenthealthprognosis .