RESEARCH PAPER
Axle box bearing fault diagnosis via an adaptive feature mode decomposition based on dual-index fusion
1
school of mechanical engineering, lanzhou jiaotong university, China
Submission date: 2025-08-03
Final revision date: 2025-08-30
Acceptance date: 2025-11-19
Online publication date: 2025-12-15
Publication date: 2025-12-15
Eksploatacja i Niezawodność – Maintenance and Reliability 2026;28(2):214468
HIGHLIGHTS
- A new fusion index is established to evaluate the sensitive mode component.
- The POFMD method is presented for axle box bearing early fault diagnosis.
- POFMD verify via different types of axle box bearings, beats VMD and Autogram.
KEYWORDS
Parameter optimizationAxle box bearingsFeature mode decompositiondual-index fusionEarly fault diagnosis
TOPICS
ABSTRACT
Considering that the early fault characteristics contained in the vibration signal of axle box bearings are easily affected by complex transmission paths and wheelset impact noise, a parameter optimization feature mode decomposition (POFMD) method based on dual-index fusion is proposed. Firstly, the square envelope of the time-domain signal autocorrelation function is introduced into the calculation of traditional Gini index and defined as ACSESGI. Secondly, the weight values of ACSESGI and the weight values of squared envelope kurtosis of different mode components obtained from FMD decomposition are calculated respectively. Finally, the above two weight values are added together and utilized as the objective function for parameter optimization. The effectiveness of presented method is verified through early fault diagnosis results of scaled-down axle box bearings and full-size axle box bearings, and its superiority was further validated by comparing with variational mode decomposition and Autogram.
ACKNOWLEDGEMENTS
This study is sponsored by the National Natural Science Foundation of China (No. 72561016) and the Innovation Fund Project of Lanzhou Jiaotong University and Tianjin University (No. LH2024006).
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CITATIONS (1):
1.
A noise-resistant fault diagnosis method based on adaptive feature mode decomposition and dual-stream context-aware network
Hairui Feng, Runfang Hao, Kun Yang, Yongqiang Cheng, Mingyu Wang, Yanxing Bu
Engineering Research Express
Hairui Feng, Runfang Hao, Kun Yang, Yongqiang Cheng, Mingyu Wang, Yanxing Bu
Engineering Research Express
| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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