A Comprehensive Degradation Modeling Comparison From Statistical to Artificial Intelligence Models for Curing Oven Chains

Abstract

The limitations of physics-based models and the constraints posed by data-driven models have motivated the development of fusion models for degradation modeling. These fusion models are designed to overcome the shortcomings inherent to either type of these models when used in isolation. In reliability analysis, particularly for highly reliable systems or units, the available datasets often exhibit small sample sizes. In such instances, the amount of data may not suffice for training powerful data-driven models, which typically require large datasets. Additionally, physics-based models may fail to capture all relevant information present in the data. This article focuses on addressing small sample-size datasets related to highly reliable systems, exploring various statistical and machine learning models tailored for such datasets, from statistical and AI models to fusion models. Furthermore, to address the challenges of using these models in isolation, a combination approach is presented involving employing simple data-driven models accompanied by essential data preprocessing and a physics-based model. This combination enables the models to capture the majority of pertinent information within the data. Also, a time-windowed multilayer perceptron is adapted to the dataset, showing that a meticulously prepared artificial neural network model might surpass the performance of some robust data-driven and even fusion models.