A multi-model data-fusion based deep transfer learning for improved remaining useful life estimation for IIOT based systems. (March 2023)

• Record Type: Journal Article
• Authors: Behera, Sourajit; Misra, Rajiv
• Is Part Of: Engineering applications of artificial intelligence. Volume 119(2023)
• Publication Date: 2023-03
• Subjects: Remaining useful life (RUL) -- Rolling bearings -- Predictive maintenance (pdM) -- Convolutional Neural Network (CNN) -- Transfer learning (TL); Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105712
• Languages: English
• ISSNs: 0952-1976

Abstract

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.