Session:Â 14A, Thursday, 28 January 2010, 1015-1215
Moderator:Â Sanford (Sandy) Liebesman, Ph.D., Consultant
This session includes a broad range of models and methods for reliability and safety assessment. Included are: A new approach to include oil aging in reliability modeling; The use of Support Vector Machines to provide more accurate results than Artificial Neural Networks (ANN); An ANN approach that uses both failure and suspension condition monitoring histories; and a methodology supporting the qualitative and quantitative safety analysis of complex embedded systems.
Papers:
14A1 [0060] ESTIMATING THE LIFETIME OF GEAR LUBRICANTS
by Christian Maisch, Dipl.-Ing., Daniel Kirschmann, Bernd Bertsche, Dr.-Ing., Universitaet Stuttgart
Nowadays the prediction of the system reliability is the main goal in order to minimize warranty costs, meet quality standards and ensure the customers satisfaction. In this article a new approach is presented how to include oil aging into reliability modeling.
14A2 [0253] APPLICATIONS OF MODELING AND SIMULATIONS WITH PROBABILISTIC METHOD TO PREDICT RELIABILITY AT HIGH CONFIDENCE LEVEL by Ramdev Kanapady, Ph. D, MSC Works, Inc., and Ron Adib,  Ph.D, P.E., RMA Consulting Group, LLC
This paper present a technique in which computer simulation is used in place of field testing to determine the physics of failure and thereby the item reliability with high confidence level in conjunction with Probabilistic method. This approach subsetantially reduces test cost and decrease the development time table by several order of magnitude.
14A3 [0078] AN ANN APPROACH FOR REMAINING LIFE PREDICTION USING SUSPENSION HISTORIES by Zhigang Tian, Ph.D., Concordia University
Artificial neural network (ANN) methods have shown great promises in achieving more accurate equipment remaining useful life prediction. However, most reported ANN methods only utilize condition monitoring data from failure histories. In this paper, we develop an ANN approach utilizing both failure and suspension condition monitoring histories. The proposed approach is validated using real-world vibration monitoring data collected from pump bearings in the field.
14A4 [0169] DEPENDABILITY EVALUATION OF COMPLEX EMBEDDED SYSTEMS AND MICROSYSTEMS by Olaf Malasse, Gregory Buchheit, A3SI, Arts et Metiers ParisTech, Michael Pock, LRR, TU-Munchen, and Hicham Belhadaoui, A3SI, Arts et Metiers ParisTech
The evaluation of the dependability performance (RAMS) of complex embedded systems requires the development of new approaches. In software-intensive systems, the dependability structure of the functions depends on the software. The search of fault sequences must involve software and hardware. The proposed method contributes to the qualitative and quantitative safety analysis of systems and micro-systems.
Reliability and Safety Models and Assessment
Session:Â 14A, Thursday, 28 January 2010, 1015-1215
Moderator:Â Sanford (Sandy) Liebesman, Ph.D., Consultant
This session includes a broad range of models and methods for reliability and safety assessment. Included are: A new approach to include oil aging in reliability modeling; The use of Support Vector Machines to provide more accurate results than Artificial Neural Networks (ANN); An ANN approach that uses both failure and suspension condition monitoring histories; and a methodology supporting the qualitative and quantitative safety analysis of complex embedded systems.
Papers:
14A1 [0060] ESTIMATING THE LIFETIME OF GEAR LUBRICANTS
by Christian Maisch, Dipl.-Ing., Daniel Kirschmann, Bernd Bertsche, Dr.-Ing., Universitaet Stuttgart
Nowadays the prediction of the system reliability is the main goal in order to minimize warranty costs, meet quality standards and ensure the customers satisfaction. In this article a new approach is presented how to include oil aging into reliability modeling.
14A2 [0253] APPLICATIONS OF MODELING AND SIMULATIONS WITH PROBABILISTIC METHOD TO PREDICT RELIABILITY AT HIGH CONFIDENCE LEVEL by Ramdev Kanapady, Ph. D, MSC Works, Inc., and Ron Adib,  Ph.D, P.E., RMA Consulting Group, LLC
This paper present a technique in which computer simulation is used in place of field testing to determine the physics of failure and thereby the item reliability with high confidence level in conjunction with Probabilistic method. This approach subsetantially reduces test cost and decrease the development time table by several order of magnitude.
14A3 [0078] AN ANN APPROACH FOR REMAINING LIFE PREDICTION USING SUSPENSION HISTORIES by Zhigang Tian, Ph.D., Concordia University
Artificial neural network (ANN) methods have shown great promises in achieving more accurate equipment remaining useful life prediction. However, most reported ANN methods only utilize condition monitoring data from failure histories. In this paper, we develop an ANN approach utilizing both failure and suspension condition monitoring histories. The proposed approach is validated using real-world vibration monitoring data collected from pump bearings in the field.
14A4 [0169] DEPENDABILITY EVALUATION OF COMPLEX EMBEDDED SYSTEMS AND MICROSYSTEMS by Olaf Malasse, Gregory Buchheit, A3SI, Arts et Metiers ParisTech, Michael Pock, LRR, TU-Munchen, and Hicham Belhadaoui, A3SI, Arts et Metiers ParisTech
The evaluation of the dependability performance (RAMS) of complex embedded systems requires the development of new approaches. In software-intensive systems, the dependability structure of the functions depends on the software. The search of fault sequences must involve software and hardware. The proposed method contributes to the qualitative and quantitative safety analysis of systems and micro-systems.