Journal ArticleDOI
Early detection of gear failure by vibration analysis--ii. interpretation of the time-frequency distribution using image processing techniques
Weiji Wang,P.D. McFadden +1 more
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TLDR
In this paper, a system is described which uses image processing techniques to assist in the automatic interpretation of gear vibration signatures for early failure detection and fault diagnosis, where the vibration signature of individual gear in a gearbox is extracted by the time domain synchronous averaging technique from the total vibration signal measured on the gearbox casing.About:
This article is published in Mechanical Systems and Signal Processing.The article was published on 1993-05-01. It has received 91 citations till now. The article focuses on the topics: Image processing & Feature extraction.read more
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Journal ArticleDOI
A review on machinery diagnostics and prognostics implementing condition-based maintenance
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
Journal ArticleDOI
Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples
TL;DR: A systematic review of over 20 major time-frequency analysis methods reported in more than 100 representative articles published since 1990 can be found in this article, where their fundamental principles, advantages and disadvantages, and applications to fault diagnosis of machinery have been examined.
Journal ArticleDOI
A review of vibration-based techniques for helicopter transmission diagnostics
Paul D. Samuel,Darryll J. Pines +1 more
TL;DR: Emerging research trends suggest that improvements in signal processing, sensor development and individual-tooth mesh waveform modelling could improve the performance of current and future helicopter transmission diagnostics.
Journal ArticleDOI
The combined use of vibration, acoustic emission and oil debris on-line monitoring towards a more effective condition monitoring of rotating machinery
TL;DR: In this article, three on-line monitoring techniques are implemented in the tests and data fusion is accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix.
Journal ArticleDOI
Fault diagnosis of spur bevel gear box using artificial neural network (ANN), and proximal support vector machine (PSVM)
TL;DR: The statistical feature vectors from Morlet wavelet coefficients are classified using J48 algorithm and the predominant features were fed as input for training and testing ANN and PSVM and their relative efficiency in classifying the faults in the bevel gear box was compared.
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Application of wavelets to gearbox vibration signals for fault detection
Weiji Wang,P.D. McFadden +1 more