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

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.

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 445
Author(s):  
Huaqing Wang ◽  
Mengyang Wang ◽  
Junlin Li ◽  
Liuyang Song ◽  
Yansong Hao

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.


2007 ◽  
Vol 3 (1) ◽  
pp. 7-16
Author(s):  
Khalid Al-Raheem ◽  
Asok Roy ◽  
K. Ramachandran ◽  
David Harrison ◽  
Steven Grainger

The Exploitation of Wavelet De-Noising To Detect Bearing Faults Failure diagnosis is an important component of the Condition Based Maintenance (CBM) activities for most engineering systems. Rolling element bearings are the most common cause of rotating machinery failure. The existence of the amplitude modulation and noises in the faulty bearing vibration signal present challenges to effective fault detection method. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, we proposed new approach for bearing fault detection based on the autocorrelation of wavelet de-noised vibration signal through a wavelet base function derived from the bearing impulse response. To improve the fault detection process the wavelet parameters (damping factor and center frequency) are optimized using maximization kurtosis criteria to produce wavelet base function with high similarity with the impulses generated by bearing defects, that leads to increase the magnitude of the wavelet coefficients related to the fault impulses and enhance the fault detection process. The results show the effectiveness of the proposed technique to reveal the bearing fault impulses and its periodicity for both simulated and real rolling bearing vibration signals.


Author(s):  
Yaguo Lei ◽  
Jing Lin ◽  
Dong Han ◽  
Zhengjia He

Rolling element bearings are widely used in modern machinery and play an important role in industrial applications. Tough environments under which they work make them subject to failure. The classical strategy is to collect bearing vibration signals and denoise the signals to detect fault features by using signal processing techniques. Although the noise is reduced with this strategy, the fault features may be weakened or even destroyed as well. Different from the classical denoising techniques, stochastic resonance is able to extract weak features embedded in heavy noise by utilizing noise instead of eliminating noise. The single stochastic resonance, however, fails to extract the fault features when the signal-to-noise ratio of the bearing vibration signals is very low. To address this problem, this paper investigates the enhancement methods of stochastic resonance and develops a cascaded stochastic resonance-based weak feature extraction method for bearing fault detection. Two sets of vibration signals collected respectively from an experimental bearing and a bearing inside a train wheel pair are utilized to demonstrate the proposed method. The results show that the method is superior to the other enhancement methods in extracting weak features of bearing faults.


2013 ◽  
Vol 694-697 ◽  
pp. 1377-1381
Author(s):  
Xing Chun Wei ◽  
Yu Lin Tang ◽  
Tao Chen

Aiming at rolling bearing fault signal of the non stationary feature, Apply a new method to the rolling bearing vibration signal of feature extraction, which is combined the Empirical Mode Decomposition (EMD) and the Choi-Williams distribution. Firstly, original signals were decomposed into a series of intrinsic mode functions (IMF) of different scales. To the decomposed each IMF component for Choi-Williams time-frequency analysis, Then take the linear superposition, finally obtained the rolling bearing vibration signal of Choi-Williams distribution. After the analyses of the rolling bearing inner ring, outer ring and rolling element fault signal ,the results show that this method can effectively suppress the frequency aliasing and interference caused by cross terms. And be able to accurately extract the fault frequency of the bearing inner ring, outer ring and rolling element, lay the foundation for the subsequent rolling bearing state recognition.


Author(s):  
Huan Huang ◽  
Natalie Baddour ◽  
Ming Liang

The kurtogram is a spectral analysis tool used to detect non-stationarities in a signal. It can be effectively used to determine the optimal filter for bearing fault feature extraction from a blurred vibration signal, since the transients of the bearing fault-induced signal can be regarded as non-stationary. However, the effectiveness of the kurtogram is diminished when the signal is collected from a bearing operating under time-varying speed conditions. There is a need to improve the performance of the kurtogram under time-varying speed conditions. In this paper, a short-time kurtogram method is proposed for bearing fault feature extraction under time-varying speed conditions. The performance of the short-time kurtogram is examined with experimental data. The results demonstrate that the short-time kurtogram can effectively be used to extract bearing fault features under time-varying speed conditions.


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