Hierarchical multiscale permutation entropy-based feature extraction and fuzzy support tensor machine with pinball loss for bearing fault identification
Authors:
- Cheng YangCYCheng Yang
- Minping JiaMJMinping Jia
- 1 February 2021
- journal article
- research article
- Published by Elsevier in Mechanical Systems and Signal Processing
- Vol. 149
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (51675098)
This publication has 33 references indexed in Scilit:
- A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropyMechanical Systems and Signal Processing, 2018
- Rolling bearing fault feature learning using improved convolutional deep belief network with compressed sensingMechanical Systems and Signal Processing, 2018
- Generalized composite multiscale permutation entropy and Laplacian score based rolling bearing fault diagnosisMechanical Systems and Signal Processing, 2018
- Wind turbine bearing fault diagnosis based on adaptive local iterative filtering and approximate entropyProceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2016
- One-Class Support Tensor MachineKnowledge-Based Systems, 2016
- Support Tensor Machines for Classification of Hyperspectral Remote Sensing ImageryIEEE Transactions on Geoscience and Remote Sensing, 2016
- A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearingsMeasurement, 2015
- Semi-supervised tensor learning for image classificationMultimedia Systems, 2014
- Multiscale Permutation Entropy of Physiological Time Series2005
- Permutation Entropy: A Natural Complexity Measure for Time SeriesPhysical Review Letters, 2002