Modified genetic algorithm for extracting thermal profiles from infrared image data

摘要

Abstract: Analysis of the thermal profile of an electronic circuit card during warm-up can be useful in detecting malfunctioning components on the card. Extracting the thermal profile can require the processing of 10 to 15 images of 600,000 bytes each. By extracting the heat transient associated with the heat sources on the circuit card, this problem of characterizing the thermal transient can be reduced to one of modeling the peak temperatures associated with a handful of components. The thermal profile of each component can be modeled as a function of four parameters: three are functions of the heat dissipation characteristics of the circuit card, and the fourth is proportional to the power consumption of the component generating the heat. Extraction of the parameters was achieved through a modified genetic algorithm. The genetic algorithm was employed when traditional techniques of Newton, non-linear regression, gradient search, and binary search proved to be slow, unstable, and unreliable. In traditional genetic algorithm implementations, improvement in performance ceases when improvement requires a simultaneous mutation of two or more variables. We seem to have circumvented the difficulty by expressing the problem in the differential domain, and coupling the genetic algorithm with a cooperative `follow the leader' approach to optimization. The extracted power consumption parameters are then employed to distinguish between `good' and `bad' cards. !4