A multi-model data-fusion based deep transfer learning for improved remaining useful life estimation for IIOT based systems

Abstract

Remaining useful life (RUL) estimation, a key component in predictive maintenance (PdM), aims to reduce maintenance cycles in the prognostic health of mechanical equipment(s). Research directions using deep-learning-based RUL estimation often suffer from limited availability of degradation signals resulting inaccurate predictions. Until now, state-of-the-art models trained on large sets of natural images to classify objects have not been re-used to improve regression-based RUL estimation accuracies of mechanical equipment. Actually, this is a rarely researched topic in PdM. Inspired by transfer learning, we showcase that the knowledge learned by popular pre-trained models can be transferred to improve industrial machinery-based-maintenance decision-making. Accordingly, this paper proposes a novel multi-model data-fusion-based deep transfer learning (MMF-DTL) framework for improved RUL estimation of rolling bearings through degradation images (DI) and pre-trained deep convolutional neural networks (CNNs). After procuring the degradation signals, we obtain DIs incorporating sufficient deterioration information using Markov Transition Fields. Next, these DIs are input into a DTL network comprising three pre-trained CNNs, i.e., DenseNet201, VGG16, and ResNet50, designed in a parallel fashion, where each constituent fine-tunes a different count of layers. Subsequently, features extracted from each component model are fused in a weighted manner and passed onto several fully connected layers. The effectiveness of the proposed framework is validated using the PHM Challenge 2012 bearing degradation dataset. Compared to several state-of-the-art works, our approach improves ∼12.57% on error rate and ∼26.04% on MAE, suggesting it is practically feasible to grasp transferable attributes from a general-purpose related dataset to another with minimal dataset size.