Enhancing multimodal fault diagnosis in mechanical systems via mixture of experts
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Mechanical wear occurs during the operating cycle of all types of complex machinery. In this paper, the spectral, ferro-spectral, physical, and chemical analyses, along with onboard particle counting characteristics under laboratory conditions, are taken as small sample datasets. Wasserstein Generative Adversarial Network is used as the regeneration algorithm model for raw data, and the composite dataset with richer semantic information is used as input. A one-dimensional representation of the composite data is transformed into a two-dimensional image containing richer static information using the Markov Transfer Field transformation concept. The Mixture of Experts based meritocracy architecture selects different expert systems for various features in the dataset by categorizing the expert systems according to combinatorial principles and setting corresponding weight assignments. ConvNeXt, Bidirectional Transformer (BiTransformer), and Bidirectional Long Short-Term Memory are then employed to capture the image features and perform fault diagnosis on the composite one-dimensional mechanical wear data, respectively. An attention mechanism is added to optimize the algorithm globally, weighting the feature information across multiple dimensions to ensure the reliability and completeness of the results. The final results show that the accuracy of fault diagnosis exceeds 95%, demonstrating ideal performance.
Date Issued
2025-08-26
Date Acceptance
2025-08-10
Citation
Complex & Intelligent Systems, 2025, 11 (8)
ISSN
2199-4536
Publisher
Springer
Journal / Book Title
Complex & Intelligent Systems
Volume
11
Issue
8
Copyright Statement
© The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
License URL
Publication Status
Published
Article Number
ARTN 425