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Multi-sensor Signals Processing and Information Fusion for Structural Health Monitoring of Rotary Machinery with Multiple Faults

Published: 23 April 2024 Publication History

Abstract

With huge load and complex working conditions, different types of faults will generate at rotary machinery under long operation. Therefore, effectively and accurately recognize fault is significant for rotary machinery to ensure its stable and reliable operation. To improve the recognition accuracy, the structural health monitoring (SHM) method is proposed for rotary machinery with multiple faults by fusing multi-sensor information in different level. Firstly, the monitoring signals are processed to obtain standard samples, and the samples collected at the same location are fused on feature-level based on one-dimensional convolutional neural network (1D CNN). By inputting original and feature-level fusion samples, several original results are obtained with 1D CNN. To improve the accuracy of original result, the decision-level fusion method is proposed by weighting its reliability and certainty, and it can recognize multiple faults of rotary machinery under variable working conditions. The proposed SHM method is verified based on the experimental rotary machinery platform with 10 types faults of bearing and shift under three working conditions. The results demonstrate that the proposed method can recognize faults accurately by fusing monitoring signals in feature and decision level, which also has satisfactory performance of working conditions transferring.

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ICCIP '23: Proceedings of the 2023 9th International Conference on Communication and Information Processing
December 2023
648 pages
ISBN:9798400708909
DOI:10.1145/3638884
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Association for Computing Machinery

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Published: 23 April 2024

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Author Tags

  1. Information Fusion
  2. Rolling bearing
  3. Signals processing
  4. Structural health monitoring

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ICCIP 2023

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Overall Acceptance Rate 61 of 301 submissions, 20%

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