Mechanical fault diagnosis system based on genetic algorithm optimization and multi-sensor data fusion
1
College of Electromechanical Engineering, Jiaozuo University, Jiaozuo, Henan, 454000, China
2
College of Artificial Intelligence, Jiaozuo University, Jiaozuo, Henan, 454000, China
Submission date: 2025-08-08
Final revision date: 2025-12-08
Acceptance date: 2025-12-17
Online publication date: 2025-12-18
Publication date: 2025-12-18
Corresponding author
Diagnostyka 2025;26(4):2025410
KEYWORDS
TOPICS
ABSTRACT
Industrial machinery frequently experiences machine failures during operation. Promptly diagnosing these failures can improve industrial operational efficiency to a certain extent. Existing research has many shortcomings. This paper considers two issues: data noise and the weight of multi-sensor data fusion, and designs a GA-MSDFD(Genetic Algorithm-based Multi-Sensor Data Fusion Diagnosis) method. This method first uses manually defined indicators to filter noise and uses a custom method to eliminate it. Feature extraction is then performed on the evolved data, and a genetic algorithm is used for multi-objective feature selection. This algorithm has inherent advantages over other machinery because its design considers the impact of noisy data. Experimental results show that our model achieves a fault diagnosis accuracy of 96.8%, far exceeding several other machinery models. The model also far outperforms these models in noise robustness, noise resistance, and convergence performance. The proposed model is of great significance for the maintenance of industrial machinery operations.
FUNDING
This research received no external funding.
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