Dynamic Neural Network Architecture Design for Predicting Remaining Useful Life of Dynamic Processes
Journal Title: Journal of Data Science and Intelligent Systems - Year 2024, Vol 2, Issue 3
Abstract
The prediction of remaining useful life is critical in predictive health management. This is done to reduce the expenses associated with operation and maintenance by avoiding errors and failures in dynamic processes. Recently, the abilities of feature classification and automated extraction of neural networks in its convolutional forms have shown fascinating performance when used for estimating the remaining useful life of dynamic processes using deep learning structures. This was accomplished by putting these talents to the task of predicting how long the procedures would be beneficial. Existing network topologies, on the other hand, virtually entirely extract features at a single scale while neglecting important information at other sizes. Meanwhile, because of the architecture of a single network path, the comprehensiveness of the features discovered by these tools is limited. To address these concerns, the authors propose a network structure based on a feature fusion strategy on a parallel multiscale architecture. This structure is then utilized to compute the remaining useful life. This prototype is divided into two sections: the first is a multiscale feature extraction module designed to extract local information features, and the second is a causal convolution module designed to extract global information features by combining multi-layer causal convolution with average pooling. The multiscale feature extraction module is intended for the extraction of local information features, while the causal convolution module is intended for the extraction of global information features. Finally, the two distinct paths are joined to create a fully integrated layer. The simulations and results show that this method has the potential to improve the efficiency and accuracy of estimating the remaining useful life index. Furthermore, the advantages of the established strategy are shown by comparing the results obtained with those produced by applying cutting-edge techniques on a well-known data-set depicting a simulated turbofan engine.
Authors and Affiliations
Silvio Simani, Yat Ping Lam, Saverio Farsoni, Paolo Castaldi
Keywords
Related Articles
- EP ID EP752186
- DOI 10.47852/bonviewJDSIS3202967
- Views 28
- Downloads 0
Chemical Engineering Numerical Analysis with R: Peng-Robinson Equation of State
Likely, many text on MATLAB, C++, FORTRAN and Python programming languages exist in chemical engineering libraries, discussing their applications for chemical engineering numerical analysis. R programming language, which...
Models and Techniques for Domain Relation Extraction: A Survey
As the significant subtask of information extraction, relation extraction (RE) aims to identify and classify semantic relations between pairs of entities and is widely adopted as the foundation of downstream applications...
Comparative Assessment of Colon Cancer Classification Using Diverse Deep Learning Approaches
Colon cancer is a general form of avoidable cancer, which is also widely spread across the globe. It is also a leading cancer and considered as big killer among all kinds of cancers. In recent times, significant advances...
Analytic Network Process (ANP) Method: A Comprehensive Review of Applications, Advantages, and Limitations
Nowadays, multi-criteria decision-making (MCDM) methods possess manifold applications in many areas from engineering to supply chain and management. The analytic network process (ANP) method is one of the most widely use...
The Evolving Landscape of Oil and Gas Chemicals: Convergence of Artificial Intelligence and Chemical-Enhanced Oil Recovery in the Energy Transition Toward Sustainable Energy Systems and Net-Zero Emissions
Chemical-enhanced oil recovery (EOR) is a field of study that can gain significantly from artificial intelligence (AI), addressing uncertainties such as mobility control, interfacial tension reduction, wettability altera...