scholarly journals A Novel Signal Separation Method Based on Improved Sparse Non-Negative Matrix Factorization

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 445
Author(s):  
Huaqing Wang ◽  
Mengyang Wang ◽  
Junlin Li ◽  
Liuyang Song ◽  
Yansong Hao
Keyword(s):  
Frequency Distribution ◽  
Matrix Factorization ◽  
Vibration Signal ◽  
Separation Method ◽  
Signal Separation ◽  
Reference Vector ◽  
Time Frequency ◽  
Fault Features ◽  
Time Frequency Distribution ◽  
Non Negative Matrix Factorization

In order to separate and extract compound fault features of a vibration signal from a single channel, a novel signal separation method is proposed based on improved sparse non-negative matrix factorization (SNMF). In view of the traditional SNMF failure to perform well in the underdetermined blind source separation, a constraint reference vector is introduced in the SNMF algorithm, which can be generated by the pulse method. The square wave sequences are constructed as the constraint reference vector. The output separated signal is constrained by the vector, and the vector will update according to the feedback of the separated signal. The redundancy of the mixture signal will be reduced during the constantly updating of the vector. The time–frequency distribution is firstly applied to capture the local fault features of the vibration signal. Then the high dimension feature matrix of time–frequency distribution is factorized to select local fault features with the improved SNMF method. Meanwhile, the compound fault features can be separated and extracted automatically by using the sparse property of the improved SNMF method. Finally, envelope analysis is used to identify the feature of the output separated signal and realize compound faults diagnosis. The simulation and test results show that the proposed method can effectively solve the separation of compound faults for rotating machinery, which can reduce the dimension and improve the efficiency of algorithm. It is also confirmed that the feature extraction and separation capability of proposed method is superior to the traditional SNMF algorithm.

scholarly journals Feature Extraction Using Sparse Kernel Non-Negative Matrix Factorization for Rolling Element Bearing Diagnosis

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3680
Author(s):  
Lin Liang ◽  
Xingyun Ding ◽  
Fei Liu ◽  
Yuanming Chen ◽  
Haobin Wen
Keyword(s):  
Feature Extraction ◽  
Fault Detection ◽  
Matrix Factorization ◽  
Vibration Signal ◽  
Frequency Bands ◽  
Time Frequency ◽  
Bearing Fault ◽  
Fault Features ◽  
Sparse Kernel ◽  
Non Negative Matrix Factorization

For early fault detection of a bearing, the localized defect generally brings a complex vibration signal, so it is difficult to detect the periodic transient characteristics from the signal spectrum using conventional bearing fault diagnosis methods. Therefore, many matrix analysis technologies, such as singular value decomposition (SVD) and reweighted SVD (RSVD), were proposed recently to solve this problem. However, such technologies also face failure in bearing fault detection due to the poor interpretability of the obtained eigenvector. Non-negative Matrix Factorization (NMF), as a part-based representation algorithm, can extract low-rank basis spaces with natural sparsity from the time–frequency representation. It performs excellent interpretability of the factor matrices due to its non-negative constraints. By this virtue, NMF can extract the fault feature by separating the frequency bands of resonance regions from the amplitude spectrogram automatically. In this paper, a new feature extraction method based on sparse kernel NMF (KNMF) was proposed to extract the fault features from the amplitude spectrogram in greater depth. By decomposing the amplitude spectrogram using the kernel-based NMF model with L1 regularization, sparser spectral bases can be obtained. Using KNMF with the linear kernel function, the time–frequency distribution of the vibration signal can be decomposed into a subspace with different frequency bands. Thus, we can extract the fault features, a series of periodic impulses, from the decomposed subspace according to the sparse frequency bands in the spectral bases. As a result, the proposed method shows a very high performance in extracting fault features, which is verified by experimental investigations and benchmarked by the Fast Kurtogram, SVD and NMF-based methods.

Comparison of Methods for Time-Frequency Analysis of Oil Whip Vibration Signal

2011 ◽  
Vol 211-212 ◽  
pp. 983-987
Author(s):  
Ling Xiang ◽  
Hao Sun
Keyword(s):  
Frequency Distribution ◽  
Frequency Analysis ◽  
Vibration Signal ◽  
Time Frequency Analysis ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Oil Whip ◽  
Redundant Component ◽  
Non Stationary Signal ◽  
Time Frequency Distribution

The signal analysis is important in extracting fault characteristics in fault diagnosis of machinery. To deal with non-stationary signal, time-frequency analysis techniques are widely used. The experiment data of oil whip vibration fault signal were analyzed by different methods, such as short time Fourier transform (STFT), Wigner-Ville distribution (WVD), Wavelet transform (WT) and Hilbert-Huang Transform (HHT). Compared with these methods, it is demonstrated that the time-frequency resolutions of STFT and WVD were inconsistent, which were easy to cross or make signal lower. WT had distinct time-frequency distribution, but it brought redundant component. HHT time-frequency analysis can detect components of low energy, and displayed true and distinct time-frequency distribution. Therefore, it is a very effective tool to diagnose the faults of rotating machinery.

Signal separation and correction with multiple Doppler acoustic sources for wayside fault diagnosis of train bearings

2016 ◽  
Vol 231 (14) ◽  
pp. 2664-2680
Author(s):  
Shangbin Zhang ◽  
Qingbo He ◽  
Haibin Zhang ◽  
Kesai Ouyang ◽  
Fanrang Kong
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Frequency Distribution ◽  
Signal Separation ◽  
Frequency Distributions ◽  
Time Frequency ◽  
S Transform ◽  
Generalized S Transform ◽  
Original Time ◽  
Time Frequency Distribution

The extraction of single train signal is necessary in wayside fault diagnosis because the acoustic signal acquired by a microphone is composed of multiple train bearing signals and noises. However, the Doppler distortion in the signal acquired by a microphone effectively hinders the signal separation and fault diagnosis. To address this issue, we propose a novel method based on the generalized S-transform, morphological filtering, and time–frequency amplitude matching-based resampling time series for multiple-Doppler-acoustic-source signal separation and correction. First, the original time–frequency distribution is constructed by applying generalized S-transform to the raw signal acquired by a microphone. Based on a morphological filter, several time–frequency distributions corresponding to different single source Doppler fault signals are extracted from the original time–frequency distribution. Subsequently, the time–frequency distributions are reverted to time signals by inverse generalized S-transform. Then, a resampling time series is built by time–frequency amplitude matching to obtain the correct signals without Doppler distortion. Finally, the bearing fault is diagnosed by the envelope spectrum of the correction signal. The effectiveness of this method is verified by simulated and practical signals.

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