A data-driven adaptive algorithm and decision support design of multisensory information fusion for prognostics and health management applications

Multisensory systems play a critical role in prognostics and health management (PHM), and utilise the information from multi-device synchronous measurements for fault diagnosis and predictive maintenance. But it is not suitable for specific systems with limited bandwidth and energy reservoirs since...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Journal of engineering design Ročník 34; číslo 2; s. 158 - 179
Hlavní autoři: Xie, Tingli, Huang, Xufeng, Park, Hyung Wook, Kim, Heung Soo, Choi, Seung-Kyum
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 01.02.2023
Taylor & Francis Ltd
Témata:
Adaptive algorithms
Artificial neural networks
Data integration
Data-driven adaptive
decision support design
Fault diagnosis
Feature extraction
Functional analysis
Measuring instruments
Multisensor fusion
multisensory information fusion
Predictive maintenance
prognostics and health management
Qualitative analysis
Sensors
Systems analysis
ISSN:0954-4828, 1466-1837
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Multisensory systems play a critical role in prognostics and health management (PHM), and utilise the information from multi-device synchronous measurements for fault diagnosis and predictive maintenance. But it is not suitable for specific systems with limited bandwidth and energy reservoirs since the increased sophistication of measurement devices requires more computation and power resources. This research explores a data-driven analytical framework for multisensory system analysis and design in PHM. The proposed framework provides the optimal subset of reliable sensors to make trade-offs between accuracy demands and system constraints. The integration definition for function modelling method is adopted for modelling and functional analysis of the proposed framework. An adaptive signal conversion algorithm is designed to process the data from all reliable sensors in the system. The convolutional neural network with residual learning is built for automatic feature extraction. Combined with the evaluation rules and expert knowledge, performance analyses are obtained, including qualitative results, fault diagnosis, and the optimal sensor combination. An open-source bearing dataset of the multisensory system with five measurements is conducted to demonstrate the effectiveness and feasibility of the proposed framework.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0954-4828
1466-1837
DOI:10.1080/09544828.2023.2177937