Application of Time-Frequency Analysis & Blind Source Separation to Diagnosis of Faults with Generator Rotor System

One method for diagnosis of faults with generator rotor is contrived by combining local wave method and blind source separation. Time-frequency image varies with local wave of different fault signals, and this feature is applied to identify different faults. In order to realize automatic classification of faults, blind source separation is employed for separation of independent components in time-frequency image of local wave of different fault signals, so as to derive projection coefficients for a set of source images. On the basis of this, automatic classification of faults is realized with probability nerve network. Taking fault signal of rotor as an example, this method is investigated, and the validity is proved by experimental results.

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Blind Source Separation for Under-Determined Mixtures Based on Time-Frequency Analysis

Key Engineering Materials ◽

10.4028/www.scientific.net/kem.693.1350 ◽

2016 ◽

Vol 693 ◽

pp. 1350-1356 ◽

Cited By ~ 1

Author(s):

Hong Kun Li ◽

Hong Yi Liu ◽

Chang Bo He

Keyword(s):

Blind Source Separation ◽

Frequency Analysis ◽

Source Separation ◽

Vibration Signal ◽

Time Frequency Analysis ◽

Time Frequency ◽

Mode Decomposition ◽

Diagnosis And Classification ◽

Source Signals

Blind source separation (BSS) is an effective method for the fault diagnosis and classification of mixture signals with multiple vibration sources. The traditional BSS algorithm is applicable to the number of observed signals is no less to the source signals. But BSS performance is limit for the under-determined condition that the number of observed signals is less than source signals. In this research, we provide an under-determined BSS method based on the advantage of time-frequency analysis and empirical mode decomposition (EMD). It is suitable for weak feature extraction and pattern recognition. Firstly, vibration signal is decomposed by using EMD. The number of source signals are estimated and the optimal observed signals are selected according to the EMD. Then, the vibration signal and the optimal observed signals are used to construct the multi-channel observed signals. In the end, BSS based on time-frequency analysis are used to the constructed signals. Gearbox signals are used to verify the effectiveness of this method.

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Linear Multichannel Blind Source Separation based on Time-Frequency Mask Obtained by Harmonic/Percussive Sound Separation

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) ◽

10.1109/icassp39728.2021.9413494 ◽

2021 ◽

Author(s):

Soichiro Oyabu ◽

Daichi Kitamura ◽

Kohei Yatabe

Keyword(s):

Blind Source Separation ◽

Source Separation ◽

Time Frequency ◽

Sound Separation

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An Underdetermined Blind Source Separation Method with Application to Modal Identification

Shock and Vibration ◽

10.1155/2019/1637163 ◽

2019 ◽

Vol 2019 ◽

pp. 1-15 ◽

Cited By ~ 1

Author(s):

Gang Yu

Keyword(s):

Blind Source Separation ◽

Source Separation ◽

Modal Identification ◽

Modal Parameters ◽

Underdetermined Blind Source Separation ◽

Structural Dynamic ◽

Time Frequency ◽

Modal Response ◽

Experimental Validations ◽

Straight Lines

In structural dynamic analysis, the blind source separation (BSS) technique has been accepted as one of the most effective ways for modal identification, in which how to extract the modal parameters using very limited sensors is a highly challenging task in this field. In this paper, we first review the drawbacks of the conventional BSS methods and then propose a novel underdetermined BSS method for addressing the modal identification with limited sensors. The proposed method is established on the clustering features of time-frequency (TF) transform of modal response signals. This study finds that the TF energy belonging to different monotone modals can cluster into distinct straight lines. Meanwhile, we provide the detailed theorem to explain the clustering features. Moreover, the TF coefficients of each modal are employed to reconstruct all monotone signals, which can benefit to individually identify the modal parameters. In experimental validations, two experimental validations demonstrate the effectiveness of the proposed method.

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