Next Article in Journal
Study on Components Determination and Performance Evaluation of LS Pre-Maintenance Agent
Next Article in Special Issue
An Intelligent Fault Diagnosis Approach Considering the Elimination of the Weight Matrix Multi-Correlation
Previous Article in Journal
RNA–CTMA Dielectrics in Organic Field Effect Transistor Memory
Previous Article in Special Issue
Fault Detection and Isolation for Redundant Inertial Measurement Unit under Quantization
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Appl. Sci. 2018, 8(6), 888; https://doi.org/10.3390/app8060888

Research of Feature Extraction Method Based on Sparse Reconstruction and Multiscale Dispersion Entropy

The State Key Lab of Fluid Power Transmission and Controls, Zhejiang University, No.38, Zheda Rd, Hangzhou 310027, China
*
Author to whom correspondence should be addressed.
Received: 16 April 2018 / Revised: 15 May 2018 / Accepted: 24 May 2018 / Published: 29 May 2018
(This article belongs to the Special Issue Fault Detection and Diagnosis in Mechatronics Systems)
Full-Text   |   PDF [13794 KB, uploaded 29 May 2018]   |  

Abstract

As one of the most important components in rotating machinery, it’s necessary and essential to monitor the rolling bearing operating condition to prevent equipment failure or accidents. However, in vibration signal processing, the bearing initial fault detection under background noise is quite difficult. Therefore, in this paper a new feature extraction method combining sparse reconstruction and Multiscale Dispersion Entropy (MDErms) is proposed. Firstly, the Sliding Matrix Sequences (SMS) truncation and sparse reconstruction by Hankel-matrix are applied to the vibration signal. Then MDErms is utilized as a characteristic index of vibration signal, which is suitable for a short time series. Additionally, the MDErms is employed in the sparse reconstructed matrix sequences to achieve the Multiscale Fusion Entropy Value Sequence (MFEVS). The MFEVS keeps the fault potential feature information in different scales and is superior in distinguishing fault periodic impulses from heavy background noise. Finally, the designed FIR bandpass filter based on the MFEVS, shows prominent features in denoising and detecting weak bearing faults, which is separately verified by simulation studies and artificial fault experiments in different cases. By comparison with traditional methods like EEMD, Wavelet Packet (WP), and fast kurtogram, it can be concluded that the proposed method has a remarkable ability in removing noise and detecting rolling bearing faint fault. View Full-Text
Keywords: fault diagnosis; feature extraction; rolling bearing; signal processing fault diagnosis; feature extraction; rolling bearing; signal processing
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Zhang, Y.; Tong, S.; Cong, F.; Xu, J. Research of Feature Extraction Method Based on Sparse Reconstruction and Multiscale Dispersion Entropy. Appl. Sci. 2018, 8, 888.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top