Life Feature Extraction Based on Hilbert Marginal Spectrum Entropy for ADT Vibration

In the paper, a fault feature extraction method for rotor system is proposed based on Hilbert marginal spectrum. Compared with the spectrum analysis method via Fourier transformation, it is more effective for the rotating machinery vibrating signal analysis. Extracting the rotor system fault feature frequency from Hilbert marginal spectrum can not only enhance the frequency resolution, but also remove other unrelated frequency component, so as to make the spectrum peak of the fault feature frequency more obviously, and the analysis diagnosis results more accurately. This method result is applied to the fault feature extraction and diagnosis of the rotor system, and the analysis results of the experiment signal verify the validity of this method.

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A Feature Extraction Method for the Wear of Milling Tools Based on the Hilbert Marginal Spectrum

Machining Science and Technology ◽

10.1080/10910344.2019.1636263 ◽

2019 ◽

Vol 23 (6) ◽

pp. 847-868 ◽

Cited By ~ 2

Author(s):

Xu Chuangwen ◽

Chai Yuzhen ◽

Li Huaiyuan ◽

Shi Zhicheng ◽

Zhang Ling ◽

...

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Feature Extraction Method ◽

Marginal Spectrum ◽

Milling Tools

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A Novel Feature Extraction Method for the Condition Monitoring of Bearings

Energies ◽

10.3390/en14082322 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2322

Author(s):

Abdenour Soualhi ◽

Bilal El Yousfi ◽

Hubert Razik ◽

Tianzhen Wang

Keyword(s):

Feature Extraction ◽

Extraction Method ◽

Health Indicator ◽

Intrinsic Mode Functions ◽

Vibration Signals ◽

Feature Extraction Method ◽

Mode Decomposition ◽

Marginal Spectrum ◽

Spectrum Density ◽

The Hilbert Transform

This paper presents an innovative approach to the extraction of an indicator for the monitoring of bearing degradation. This approach is based on the principles of the empirical mode decomposition (EMD) and the Hilbert transform (HT). The proposed approach extracts the temporal components of oscillating vibration signals called intrinsic mode functions (IMFs). These components are classified locally from the highest frequencies to the lowest frequencies. By selecting the appropriate components, it is possible to construct a bank of self-adaptive and automatic filters. Combined with the HT, the EMD allows an estimate of the instantaneous frequency of each IMF. A health indicator called the Hilbert marginal spectrum density is then extracted in order to detect and diagnose the degradation of bearings. This approach was validated on two test benches with variable speeds and loads. The obtained results demonstrated the effectiveness of this approach for the monitoring of ball and roller bearings.

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An Improved Quantitative Image Analyser

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100078663 ◽

1977 ◽

Vol 35 ◽

pp. 226-227

Author(s):

J.P. Fallon ◽

P.J. Gregory ◽

C.J. Taylor

Keyword(s):

Feature Extraction ◽

Quantitative Analysis ◽

Frequency Content ◽

General Sense ◽

Physical Measurements ◽

Quantitative Image ◽

Image Analyser ◽

Automatic Methods ◽

Quantitative Results

Quantitative image analysis systems have been used for several years in research and quality control applications in various fields including metallurgy and medicine. The technique has been applied as an extension of subjective microscopy to problems requiring quantitative results and which are amenable to automatic methods of interpretation.Feature extraction. In the most general sense, a feature can be defined as a portion of the image which differs in some consistent way from the background. A feature may be characterized by the density difference between itself and the background, by an edge gradient, or by the spatial frequency content (texture) within its boundaries. The task of feature extraction includes recognition of features and encoding of the associated information for quantitative analysis.Quantitative Analysis. Quantitative analysis is the determination of one or more physical measurements of each feature. These measurements may be straightforward ones such as area, length, or perimeter, or more complex stereological measurements such as convex perimeter or Feret's diameter.

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