Harry, L. S. L., Duong, P. L. T., Park, H., & Raghavan, N. (2021). Bias Suppression Framework for Detrending Mean of Multi-Output Gaussian Process Regression in LED Remaining Storage Life Prognosis. IEEE Access, 9, 166639–166657. https://doi.org/10.1109/access.2021.3135511
Abstract:
Advancements in storage prognosis tend to be limited by the inherent challenge to collect
suf cient signi cant degradation data over an extensive period. Using only sparse eet data, multi-output
Gaussian process regression (MOGPR) is one of the few techniques that offers a practical data driven
approach to model non-monotonic degradation pro les with low mean absolute percentage error (MAPE).
This accuracy in the storage prognosis context is, however, sensitive to the choice of the detrending mean.
Working with light emitting diodes (LED) sparse lumen degradation data under storage conditions in this
study, the MAPE is observed to be highly correlated to the detrending bias the difference between the
detrending mean and the test mean. We explore various approaches to suppress this bias and advocate a
generic framework for eet storage prognosis. The approaches include detrending using (A) static training
data mean, (B) dynamic observed test data mean, (C) static bounded training data set pairs, (D) dynamic
weighted mean of unbounded training data set pairs and (E) moving average of weighted mean of unbounded
training data set pairs. Our analysis shows that the moving average approach (Method E) of computing
weighted mean of unbounded training data set pairs results in the most stable detrending mean to suppress
detrending bias and helps achieve an MAPE lower than 1%.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the A*STAR - MTC Programmatic
Grant Reference no. : A20H5b0142
This research / project is supported by the Temasek Labs - SEED Project
Grant Reference no. : RTDSS1910011