Journal ArticleDOI
Using the correlation dimension for vibration fault diagnosis of rolling element bearings—ii. selection of experimental parameters
David Logan,Joseph Mathew +1 more
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In this paper, the correlation dimension from chaotic time series data is determined by reconstruction of the attractor using the time delay method from the raw time series, followed by computation of the correlation dimensions from the phase space vectors.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 1996-05-01. It has received 72 citations till now. The article focuses on the topics: Correlation dimension & Attractor.read more
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A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
Akhand Rai,S. H. Upadhyay +1 more
TL;DR: In this article, the authors have presented the various signal processing methods applied to the fault diagnosis of rolling element bearings with the objective of giving an opportunity to the examiners to decide and select the best possible signal analysis method as well as the excellent defect representative features for future application in the prognostic approaches.
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Approximate Entropy as a diagnostic tool for machine health monitoring
Ruqiang Yan,Robert X. Gao +1 more
TL;DR: In this paper, a new approach to machine health monitoring based on the Approximate Entropy (ApEn) is presented, which is a statistical measure that quantifies the regularity of a time series, such as vibration signals measured from an electrical motor or a rolling bearing.
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A summary of fault modelling and predictive health monitoring of rolling element bearings
Idriss El-Thalji,Erkki Jantunen +1 more
TL;DR: In this article, the authors provide a critical review of the predictive health monitoring methods of the entire defect evolution process i.e. wear evolution over the whole lifetime and suggest enhancements for rolling element bearing monitoring.
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Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension
TL;DR: The capacity dimension, information dimension and correlation dimension are applied to classify various fault types and evaluate various fault conditions of rolling element bearing, and the classification performance of each fractal dimension and their combinations are evaluated by using SVMs.
Journal ArticleDOI
Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell
TL;DR: A novel condition-based monitoring system consisting of six modules: sensing, signal processing, feature extraction, classification, high-level fusion and decision making module, and a relationship between bearing condition and sensor performance has been found.