ETFA 2009 14th IEEE INTERNATIONAL CONFERENCE ON EMERGING - - PowerPoint PPT Presentation

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ETFA 2009 14th IEEE INTERNATIONAL CONFERENCE ON EMERGING - - PowerPoint PPT Presentation

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ETFA 2009 14th IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION September 22-26, 2009, Mallor lorca, ca, Spain C ONTROL SYSTEM AND FAULT DETECTION ALGORITHM FOR A RESTORED TEETH FATIGUE ASSAY MACHINE I.

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ETFA 2009

14th IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION

September 22-26, 2009, Mallor lorca, ca, Spain

CONTROL SYSTEM AND FAULT

DETECTION ALGORITHM FOR A RESTORED TEETH FATIGUE ASSAY MACHINE

  • I. Peñarrocha, M. Orellana

ipenarro@esid.uji.es

  • Dept. d’Enginyeria de Sistemes Industrials i Disseny.

Universitat Jaume I de Castelló, Spain

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SUMMARY

 Introduction  Machine requirements  Machine and control system description  Machine operation  Fault detection algorithm  Experimental results  Conclusions

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ETFA 2009, September 22-26, 2009, Mall

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INTRODUCTION

 Necessities for dental restorations in Dentistry field  Optimal design of the restored teeth  Posts of different material and geometry  Tested with time-varying forces  Knowing which component fails first

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post root stump crown adhesive

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MACHINE REQUIREMENTS

 Test different posts (material and geometry) of

restored teeth

 Emulate the mastication forces  Application of time-varying forces  Number of mastication cycles before the failure of the

tooth

 Fault detection  Knowing which component fails first  Automatic stop of forces application

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FATIGUE ASSAY MACHINE DESCRIPTION

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CONTROL SYSTEM DESCRIPTION

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MACHINE OPERATION

 Force calibration:  Torque reference (uk) vs. Measured force (Fk)

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MACHINE OPERATION

 Specimen fatigue assay  From required force (Fk) to Torque reference (uk)  Measured position (yk) depends on the fault (fk)

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FAULT DETECTION ALGORITHM

 Model-based fault detection  Noisy model:  Prediction model:  Prediction error:  Expected (fault free) prediction error:

 This error can be bounded during the identification process

 Fault detection algorithm:

 Failure occurs with probability of 99.74% if

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FAULT DETECTION ALGORITHM

 Identification of the prediction model:  LTV model

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FAULT DETECTION ALGORITHM

 Prediction error

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FAULT DETECTION ALGORITHM

 FDA:  RLS  Adaptive

threshold

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FAULT DETECTION ALGORITHM

 Tuning process  Forgetting factor:

 As big as possible to detect slow changes on the process  Bigh enough to avoid changes on the parameters due to faults

 Filter pole used to approximate :

 Big enough to approximate properly  Small enough to initiate the application

 False alarm ratio related parameter:

 Small enough to detect the faults as fast as possible  Big enough to avoid false alarms

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EXPERIMENTAL RESULTS

 Prediction error and threshold

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EXPERIMENTAL RESULTS

 Tooth failure

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CONCLUSIONS

 Developement of a fatigue assay machine for studying

the dynamical behaviour of restored teeth

 The control system has been described  The machine operation has been described  The fault detection algorithm has been addressed  The tunning process has been carried out  Experimental results have been discussed

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ETFA 2009

14th IEEE INTERNATIONAL CONFERENCE ON EMERGING TECHNOLOGIES AND FACTORY AUTOMATION

September 22-26, 2009, Mallor lorca, ca, Spain

CONTROL SYSTEM AND FAULT

DETECTION ALGORITHM FOR A RESTORED TEETH FATIGUE ASSAY MACHINE

  • I. Peñarrocha, M. Orellana

ipenarro@esid.uji.es

  • Dept. d’Enginyeria de Sistemes Industrials i Disseny.

Universitat Jaume I de Castelló, Spain