Part Reliability Assessment in Data Centers

The risks to telecom equipment due to failure and degradation of parts need to be evaluated in order to assess component and system reliability in telecom equipment and data centers. This chapter provides rules to identify the reliability risks in parts under select existing or emerging energy efficient cooling methods, and then discusses handbook-based reliability prediction methods, analyzing their applicability for the cooling methods. This chapter also provides methods to assess the reliability of parts under the cooling conditions.

[1]  Mo-Yuen Chow,et al.  Neural-network-based motor rolling bearing fault diagnosis , 2000, IEEE Trans. Ind. Electron..

[2]  Michael Osterman,et al.  Prognostics Assessment of Aluminum Support Structure on a Printed Circuit Board , 2006 .

[3]  P. Lall,et al.  A physics-of-failure (POF) approach to addressing device reliability in accelerated testing of MCMs , 1995, Proceedings of 1995 IEEE Multi-Chip Module Conference (MCMC-95).

[4]  Michael G. Pecht,et al.  A fusion prognostics method for remaining useful life prediction of electronic products , 2009, 2009 IEEE International Conference on Automation Science and Engineering.

[5]  Anthony J. Rafanelli,et al.  Plastic Encapsulated Microelectronics; Materials, Processes, Quality, Reliability, and Application , 1997 .

[6]  C. James Li,et al.  DIAGNOSTIC RULE EXTRACTION FROM TRAINED FEEDFORWARD NEURAL NETWORKS , 2002 .

[7]  Michael Pecht,et al.  Lead-free Electronics , 2006 .

[8]  M.G. Pecht,et al.  Prognostics and health management of electronics , 2008, IEEE Transactions on Components and Packaging Technologies.

[9]  D. Gillblad,et al.  Fault-tolerant incremental diagnosis with limited historical data , 2008, 2008 International Conference on Prognostics and Health Management.

[10]  M. White Scaled CMOS Technology Reliability Users Guide , 2010 .

[11]  Joseph Mathew,et al.  A COMPARISON OF AUTOREGRESSIVE MODELING TECHNIQUES FOR FAULT DIAGNOSIS OF ROLLING ELEMENT BEARINGS , 1996 .

[12]  D. K. Ranaweera,et al.  Application of radial basis function neural network model for short-term load forecasting , 1995 .

[13]  Michael Pecht,et al.  Handbook of Electronic Package Design , 1991 .

[14]  M. Farid Golnaraghi,et al.  Prognosis of machine health condition using neuro-fuzzy systems , 2004 .

[15]  Michael Pecht,et al.  A life consumption monitoring methodology for electronic systems , 2003 .

[16]  George J. Vachtsevanos,et al.  A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .

[17]  M. Pecht,et al.  Guidebook for managing silicon chip reliability , 1998 .

[18]  Michael Pecht,et al.  Prognostics-based product qualification , 2009, 2009 IEEE Aerospace conference.

[19]  M. J. Cushing,et al.  Comparison of electronics-reliability assessment approaches , 1993 .

[20]  Jin Chen,et al.  Neuro-fuzzy Based Condition Prediction of Bearing Health: , 2009 .

[21]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[22]  Michael G. Pecht,et al.  Sensor Systems for Prognostics and Health Management , 2010, Sensors.

[23]  Michael Pecht,et al.  Product Reliability, Maintainability, and Supportability Handbook , 1995 .

[24]  Nagi Gebraeel,et al.  Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.

[25]  Peter W. Tse,et al.  Prediction of Machine Deterioration Using Vibration Based Fault Trends and Recurrent Neural Networks , 1999 .

[26]  C. James Li,et al.  Automatic structure and parameter training methods for modeling of mechanical systems by recurrent neural networks , 1999 .

[27]  Michael Pecht,et al.  Electronic Packaging Materials and Their Properties , 1998 .

[28]  M. Pecht,et al.  Material failure mechanisms and damage models , 1991 .

[29]  George J. Vachtsevanos,et al.  Machine Remaining Useful Life Prediction Based on Adaptive Neuro-Fuzzy and High-Order Particle Filtering , 2010 .

[30]  M. Pecht,et al.  A fusion approach to IGBT power module prognostics , 2009, EuroSimE 2009 - 10th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems.

[31]  Bin Zhang,et al.  Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering , 2011, IEEE Transactions on Industrial Electronics.

[32]  Michael J. Roemer,et al.  Machine health monitoring and life management using finite-element-based neural networks , 1996 .

[33]  E. Bogatin,et al.  Signal integrity parameters for health monitoring of digital electronics , 2008, 2008 International Conference on Prognostics and Health Management.

[34]  Zhigang Tian,et al.  A neural network approach for remaining useful life prediction utilizing both failure and suspension histories , 2010 .

[35]  Takashi Hiyama,et al.  Predicting remaining useful life of rotating machinery based artificial neural network , 2010, Comput. Math. Appl..