Next Article in Journal
Quantification of a Low-Cost Stretchable Conductive Sensor Using an Expansion/Contraction Simulator Machine: A Step towards Validation of a Noninvasive Cardiac and Respiration Monitoring Prototype
Next Article in Special Issue
Rotating Electrical Machine Condition Monitoring Automation—A Review
Previous Article in Journal
Root Cause Identification of Machining Error Based on Statistical Process Control and Fault Diagnosis of Machine Tools
Previous Article in Special Issue
Drilling Rig Hoisting Platform Security Monitoring System Design and Application
Article Menu

Export Article

Open AccessReview
Machines 2017, 5(4), 21; doi:10.3390/machines5040021

A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing

1
Mechanical Engineering Department, Diponegoro University, Semarang 50275, Indonesia
2
School of Mechanical, Materials and Mechatronic Engineering, University ofWollongong, Wollongong, NSW 2522, Australia
3
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
*
Author to whom correspondence should be addressed.
Received: 9 August 2017 / Revised: 16 September 2017 / Accepted: 19 September 2017 / Published: 27 September 2017
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
View Full-Text   |   Download PDF [5781 KB, uploaded 29 September 2017]   |  

Abstract

This paper presents an empirical study of feature extraction methods for the application of low-speed slew bearing condition monitoring. The aim of the study is to find the proper features that represent the degradation condition of slew bearing rotating at very low speed (≈ 1 r/min) with naturally defect. The literature study of existing research, related to feature extraction methods or algorithms in a wide range of applications such as vibration analysis, time series analysis and bio-medical signal processing, is discussed. Some features are applied in vibration slew bearing data acquired from laboratory tests. The selected features such as impulse factor, margin factor, approximate entropy and largest Lyapunov exponent (LLE) show obvious changes in bearing condition from normal condition to final failure. View Full-Text
Keywords: vibration-based condition monitoring; feature extraction; low-speed slew bearing vibration-based condition monitoring; feature extraction; low-speed slew bearing
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 alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Caesarendra, W.; Tjahjowidodo, T. A Review of Feature Extraction Methods in Vibration-Based Condition Monitoring and Its Application for Degradation Trend Estimation of Low-Speed Slew Bearing. Machines 2017, 5, 21.

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]
Machines EISSN 2075-1702 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top