The temperature fluctuation modeling and compensation for the degradation data of super-luminescent diode

The environmental factor (such as temperature, etc.) is an important error sources of product performance degradation data. In practice, the raw degradation data usually contain some noise terms caused by the fluctuation of environmental factor. Therefore, it is necessary to extract the real degradation trend before degradation modeling and life prediction. In this research, the Grey relational analysis method is utilized to quantitatively describe the correlational relationship between temperature fluctuation and raw degradation data. Then a novel temperature compensation model based on least squares support vector machine (LS-SVM) is proposed, which can be used to compensate the influence of environmental fluctuation on degradation data. An engineering case study on the degradation data of a super-luminescent diode (SLD) is employed to verify the effectiveness of the proposed method.