Abstract
Bias Suppression Framework for Detrending Mean of Multi-Output Gaussian Process Regression in LED Remaining Storage Life Prognosis
Highlights
Storage has been a conventional approach to buffer the uncertainty in supply chain, be it to fulfill surge in demand or to address surplus in supply [1, 2]
This paper investigates into the effects of bias in the detrending mean on the mean absolute percentage error (MAPE) while performing multi-output Gaussian process regression (MOGPR) and proposes more versatile approaches to reduce MAPE in the degradation profile
In lieu of the true test mean, the mean of the training data set is often used as an estimate for the true test mean. This premise is on the assumption that the training set for MOGPR should be largely similar in characteristics to the test set
Summary
More disruptive “Black Swan” events have recently been observed, be it a pandemic like COVID-19 or environmental disasters like earthquakes, fires, floods, and super typhoons. These have introduced disruptive demands for emergency response equipment on one hand and led to long-term storage of high value assets like aircrafts in aviation industries during the pandemic on the other hand [3, 4]. Due to the lack of degradation models for such storage condition profiles, the affected industries have to suffer the uncertainty in the reliability of equipment or commodity placed under longterm storage or commit costly resources to inspect and service these unused equipment or commodities in order to preserve their reliability till activation [5] – [7].
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