Collaborative and trustworthy fault diagnosis for mechanical systems based on probabilistic neural network with decision-level information fusion

Xu, Z, Zhao, K, Zhang, W, Miao, W, Sun, K, Wang, J and Bashir, M (2025) Collaborative and trustworthy fault diagnosis for mechanical systems based on probabilistic neural network with decision-level information fusion. Journal of Industrial Information Integration, 46. ISSN 2452-414X

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Abstract

Fault diagnosis is a critical component of prognostics and health management, enhancing machinery reliability and ensuring operational efficiency by enabling proactive maintenance strategies. However, achieving this requires high data fidelity to accurately predict the full spectrum of faults and structural degradation for reliable assessments. AI-driven fault diagnostics based on machine learning often face challenges in reliability due to uncertainties arising from variations in data distribution, caused by changing operating conditions and noise interference. These factors undermine the trustworthiness of such methods. To address these challenges in accuracy and reliability for mechanical systems, such as gearboxes, this study proposes a Trustworthy Intelligent Diagnostic (TID) model. The TID model incorporates a multi-scale probabilistic neural network, and a decision fusion module based on uncertainty quantification (UQ). Specifically, three UQ-based decision fusion strategies are introduced to enhance diagnostic reliability by effectively managing uncertainty in fault diagnosis. Building upon the TID model, a cooperative fault diagnosis framework is further proposed to facilitate fault knowledge sharing and alleviate the limitations posed by data scarcity. The proposed approach is validated using both experimental data and real-world wind turbine gearbox failure datasets, demonstrating significant improvements in diagnostic accuracy and a notable reduction in false alarm rates. These results highlight the effectiveness, reliability, and superiority of the proposed method.

Item Type:	Article
Uncontrolled Keywords:	4605 Data Management and Data Science; 46 Information and Computing Sciences; 4007 Control Engineering, Mechatronics and Robotics; 40 Engineering; Machine Learning and Artificial Intelligence; 7 Affordable and Clean Energy; 4014 Manufacturing engineering; 4609 Information systems
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Engineering
Publisher:	Elsevier
Date of acceptance:	7 March 2025
Date of first compliant Open Access:	18 June 2025
Date Deposited:	18 Jun 2025 16:06
Last Modified:	18 Jun 2025 16:15
DOI or ID number:	10.1016/j.jii.2025.100854
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26613

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