A Fault Diagnosis Scheme for Gearbox Based on Improved Entropy and Optimized Regularized Extreme Learning Machine
Open Access
- 3 December 2022
- journal article
- research article
- Published by MDPI AG in Mathematics
- Vol. 10 (23), 4585
- https://doi.org/10.3390/math10234585
Abstract
The performance of a gearbox is sensitive to failures, especially in the long-term high speed and heavy load field. However, the multi-fault diagnosis in gearboxes is a challenging problem because of the complex and non-stationary measured signal. To obtain fault information more fully and improve the accuracy of gearbox fault diagnosis, this paper proposes a feature extraction method, hierarchical refined composite multiscale fluctuation dispersion entropy (HRCMFDE) to extract the fault features of rolling bearing and the gear vibration signals at different layers and scales. On this basis, a novel fault diagnosis scheme for the gearbox based on HRCMFDE, ReliefF and grey wolf optimizer regularized extreme learning machine is proposed. Firstly, HRCMFDE is employed to extract the original features, the multi-frequency time information can be evaluated simultaneously, and the fault feature information can be extracted more fully. After that, ReliefF is used to screen the sensitive features from the high-dimensional fault features. Finally, the sensitive features are inputted into the optimized regularized extreme learning machine to identify the fault states of the gearbox. Through three different types of gearbox experiments, the experimental results confirm that the proposed method has better diagnostic performance and generalization, which can effectively and accurately identify the different fault categories of the gearbox and outperforms other contrastive methods.Keywords
Funding Information
- Sponsored Research (JZX7Y20220144100101)
- National Natural Science Foundation of China (52275505)
This publication has 42 references indexed in Scilit:
- Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFISSensors, 2018
- Research of Planetary Gear Fault Diagnosis Based on Permutation Entropy of CEEMDAN and ANFISPublished by MDPI AG ,2018
- Dynamic modeling of gearbox faults: A reviewMechanical Systems and Signal Processing, 2018
- A phase angle based diagnostic scheme to planetary gear faults diagnostics under non-stationary operational conditionsJournal of Sound and Vibration, 2017
- Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical SignalsIEEE Transactions on Biomedical Engineering, 2017
- Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimizationExpert Systems with Applications, 2016
- Dispersion Entropy: A Measure for Time-Series AnalysisIEEE Signal Processing Letters, 2016
- Spur bevel gearbox fault diagnosis using wavelet packet transform and rough set theoryJournal of Intelligent Manufacturing, 2015
- A rule-based intelligent method for fault diagnosis of rotating machineryKnowledge-Based Systems, 2012
- Extreme learning machine: Theory and applicationsNeurocomputing, 2006