Noise reduction method for vibration signals 2D time-frequency distribution using anisotropic diffusion equation

2014 ◽  
Vol 38 (4) ◽  
pp. 609-616
Author(s):  
Aijun Yin ◽  
Lei Zhao ◽  
Zhengyi Yang ◽  
Benqian Chen
Keyword(s):  
Diffusion Equation ◽  
Noise Reduction ◽  
Frequency Distribution ◽  
Anisotropic Diffusion ◽  
Reduction Method ◽  
Vibration Signals ◽  
Time Frequency ◽  
Anisotropic Diffusion Equation ◽  
Time Frequency Distribution

An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition

2015 ◽  
Vol 230 (11) ◽  
pp. 1830-1845 ◽  
Author(s):  
Yi Wang ◽  
Dan Liu ◽  
Guanghua Xu ◽  
Kuosheng Jiang
Keyword(s):  
Dimensionality Reduction ◽  
Frequency Distribution ◽  
Frequency Band ◽  
Reduction Method ◽  
Singular Value ◽  
Signal Extraction ◽  
Time Frequency ◽  
Value Decomposition ◽  
Dimensionality Reduction Method ◽  
Time Frequency Distribution

The fast kurtogram, a faint signal extraction method, has been regarded as an effective approach to detect and characterize faint transient features in vibration signals. However, the fast kurtogram, a band-pass filtering method, which extracts transient signals by optimal frequency band selection and leaves the noise in the selected frequency band unprocessed. Therefore, to overcome the shortcoming of the fast kurtogram method, a method which can wipe off the noise in the whole frequency band is necessary. This paper proposes a novel faint signal extraction method by time–frequency distribution image dimensionality reduction. Since time–frequency distribution image can reveal intrinsic feature of nonstationary signals and can make the weak impulses feature prominent, and besides, the transient impulse feature and the noise component lie in different dimensions, so using the dimensionality reduction method based on singular value decomposition to suppress the background noise in the raw time–frequency distribution image is motivated. A bearing outer race fault signal obtained from a test-to-failure experiment and a bearing inner race fault signal obtained from an experimental motor are employed to demonstrate the enhanced performance of the proposed method in faint signal extraction. The results indicate that the proposed method outperforms the fast kurtogram method and is effective in faint signal extraction.

Design of a Time-Frequency Distribution for Vibration Monitoring under Corrosions in the Pipe

2006 ◽  
Vol 321-323 ◽  
pp. 1257-1261
Author(s):  
Gi Young Park ◽  
C.K. Lee ◽  
Jung Taek Kim ◽  
K.C. Kwon ◽  
Sang J. Lee
Keyword(s):  
Frequency Distribution ◽  
Vibration Monitoring ◽  
Vibration Signals ◽  
Plant Operation ◽  
Time Frequency ◽  
Analysis Techniques ◽  
Cross Terms ◽  
Conventional Methods ◽  
Time Frequency Distribution

To monitor the wear and degradation on a pipe by corrosion during a plant operation, the vibration signals were measured by an accelerometer and analyzed by several analysis techniques. From the conventional methods, it was difficult to identify the wear and degradation on the pipe. And hence, the time-frequency distribution (TFD) and the adaptive cone-kernel distribution (ACKD) devised for reducing the interfering cross-terms are applied to the acquired data. They can provide the distinguishing peak patterns between the normal and corrosion signals.

A Time-Frequency Distribution for Analysis of Signals with Transient Components and Its Application to Vibration Analysis

1999 ◽  
Vol 121 (3) ◽  
pp. 328-333 ◽  
Author(s):  
G. T. Zheng ◽  
P. D. McFadden
Keyword(s):  
Frequency Distribution ◽  
Wigner Distribution ◽  
Vibration Signals ◽  
Frequency Distributions ◽  
Time Frequency ◽  
Frequency Domains ◽  
Harmonic Components ◽  
Cohen Class ◽  
New Distribution ◽  
Time Frequency Distribution

Bilinear time-frequency distributions, which provide simultaneous high resolution in both time and frequency domains, offer advantages for the analysis of vibration signals where the harmonic components and sidebands may be closely spaced. However, the Choi-Williams exponential distribution is found to be unsuitable, and aliasing produced by distributions of the Cohen class also causes problems. An aliasfree exponential time-frequency distribution is introduced, which combines features of distributions of the Cohen class and the generalized Wigner distribution. The new distribution is shown to be well suited to the analysis of signals with transient components.

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