Health monitoring method of note PC for cooling performance degradation and load assessment

Health monitoring technologies, which can evaluate the performance degradation, load history and degree of fatigue, have the potential to improve the effective maintenance, the reliability design method and the availability in the improper use conditions of digital equipment. In this paper, we propose a method to assess the cooling performance degradation and load history of printed circuit boards in digital equipment by use of a hierarchical Bayes model based on CAE (Computer Aided Engineering) results of thermal stress simulation and experiment data from actual measurements. We applied this method to note PC that can monitor the device load factor and revolution number of cooling fan. It is shown that this method can estimate the temperature and deformation distribution of the printed circuit board from monitoring variables through latent variables such as thermal dissipation of the device and thermal boundary condition by use of the hierarchical Bayes model. And it is confirmed that the statistical load assessment concerning thermal cyclic load and the maximum load distribution can be conducted using the estimated temperature and deformation data. Furthermore, we verified that the cooling performance degradation can be assessed, if the temperature difference per unit thermal value between two suitable points on the printed circuit board can be obtained. It is concluded that the proposed method can be effective to assess the thermal load history and cooling performance degradation.

[1]  高橋 浩之,et al.  Thermal Fatigue Life Simulation for Sn-Ag-Cu Lead-Free Solder Joints , 2004 .

[2]  L. Joseph,et al.  Bayesian Statistics: An Introduction , 1989 .

[3]  M. Pecht,et al.  Precursor Parameter Identification for Insulated Gate Bipolar Transistor (IGBT) Prognostics , 2009, IEEE Transactions on Reliability.

[4]  Bradley P. Carlin,et al.  BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS , 1996, Stat. Comput..

[5]  M.G. Pecht,et al.  Computer Usage Monitoring for Design and Reliability Tests , 2009, IEEE Transactions on Components and Packaging Technologies.

[6]  M. Pecht,et al.  Baseline Performance of Notebook Computers Under Various Environmental and Usage Conditions for Prognostics , 2009, IEEE Transactions on Components and Packaging Technologies.

[7]  T. Louis,et al.  Bayes and Empirical Bayes Methods for Data Analysis. , 1997 .

[8]  P. Lall,et al.  Prognostics and health management of electronics , 2006, 2006 11th International Symposium on Advanced Packaging Materials: Processes, Properties and Interface.

[9]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[10]  石黒 真木夫 On the use of multiparameter models in statistical measurement techniques , 1984 .

[11]  Michael Pecht,et al.  Evaluation of built-in test , 2001 .

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

[13]  Hermann Haken,et al.  Information and Self-Organization: A Macroscopic Approach to Complex Systems , 2010 .

[14]  Takashi Kawakami,et al.  Thermal Fatigue Life of Solder Bumps in BGA , 1998 .

[15]  Michael Pecht,et al.  Physics-of-failure-based prognostics for electronic products , 2009 .

[16]  M. Pecht,et al.  Early Detection of Interconnect Degradation by Continuous Monitoring of RF Impedance , 2009, IEEE Transactions on Device and Materials Reliability.

[17]  Masaki Shiratori,et al.  Statistical Optimization Method , 1970 .