Next Article in Journal
A Fast Response Highly Selective Probe for the Detection of Glutathione in Human Blood Plasma
Previous Article in Journal
An Alternative Approach to Control Measurements of Crane Rails
Article Menu

Export Article

Open AccessArticle
Sensors 2012, 12(5), 5919-5939; doi:10.3390/s120505919

An Intelligent Diagnosis Method for Rotating Machinery Using Least Squares Mapping and a Fuzzy Neural Network

1
Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie 514-8507, Japan
2
College of Engineer Science and Technology, Shanghai Ocean University, No. 999 Hucheng Ring Road, Lingang New City, Shanghai 201306, China
*
Author to whom correspondence should be addressed.
Received: 27 March 2012 / Revised: 2 May 2012 / Accepted: 3 May 2012 / Published: 8 May 2012
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [811 KB, uploaded 21 June 2014]   |  

Abstract

This study proposes a new condition diagnosis method for rotating machinery developed using least squares mapping (LSM) and a fuzzy neural network. The non-dimensional symptom parameters (NSPs) in the time domain are defined to reflect the features of the vibration signals measured in each state. A sensitive evaluation method for selecting good symptom parameters using detection index (DI) is also proposed for detecting and distinguishing faults in rotating machinery. In order to raise the diagnosis sensitivity of the symptom parameters the synthetic symptom parameters (SSPs) are obtained by LSM. Moreover, possibility theory and the Dempster & Shafer theory (DST) are used to process the ambiguous relationship between symptoms and fault types. Finally, a sequential diagnosis method, using sequential inference and a fuzzy neural network realized by the partially-linearized neural network (PLNN), is also proposed, by which the conditions of rotating machinery can be identified sequentially. Practical examples of fault diagnosis for a roller bearing are shown to verify that the method is effective. View Full-Text
Keywords: condition diagnosis; least squares mapping; possibility theory; Dempster & Shafer theory; fuzzy neural network condition diagnosis; least squares mapping; possibility theory; Dempster & Shafer theory; fuzzy neural network
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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

Li, K.; Chen, P.; Wang, S. An Intelligent Diagnosis Method for Rotating Machinery Using Least Squares Mapping and a Fuzzy Neural Network. Sensors 2012, 12, 5919-5939.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top