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Vibration signals are used to monitor the running state of mechanical equipment, but always suffer from a lot of noise in the acquisition process. In order to eliminate noise interference as much as possible, a multilevel residual convolution autoencoder network based noise learning method (LN-MRSCAE) is proposed in this paper. The LN-MRSCAE consists of a deep convolutional autoencoder network and multilevel residual, in which the learning noise module encodes and decodes the noise components, combining with a residual module to obtain the denoised signal. The multilevel residual structure is designed to address the gradient disappearance in network learning and improve the training speed. In specific, the noise learning replaces the traditional clean signal learning. Instead of learning the characteristics of the original clean signal, just learn the characteristics of the noise signal. The effectiveness of LN-MRSCAE network denoising is verified by classic analog signal and actual bearing vibration signals with different faults. The results show that the LN-MRSCAE model is superior to the latest models, and the noise component of the signal is obviously suppressed. [ABSTRACT FROM AUTHOR]

The failure features of rolling bearings are often weak due to the influence of strong background noise. In addition, the vibration signals of faulty rolling bearing often show nonlinear and non-stationary characteristics, and the conventional time-frequency method is no longer suitable for extracting effective fault features. In order to extract the early weak fault characteristics of rolling bearing accurately, a weak fault feature extraction method for rolling bearing by combining adaptive chirp mode decomposition (ACMD) based on sparsity index regrouping scheme with time-delayed feedback stochastic resonance (TDSR) is proposed in the paper. The proposed method comprehensively utilizes the adaptive decomposition characteristics of ACMD for multi-component non-stationary signals and the enhancement effect of TDFSR on low-frequency signals in the fast Fourier transform (FFT) result. Firstly, ACMD is used to decompose the early weak fault signal of rolling bearing into a series of mode signals, then the proposed signal regrouping scheme based on sparsity index is utilized to regroup the obtained series of modes. Secondly, the optimal reconstructed component containing the main fault information is input into the calculation model of TDSR. Finally, FFT is performed on the output signal of TDSR to extract the fault characteristics effectively. [ABSTRACT FROM AUTHOR]

To solve the difficulty in weak fault detection of rolling element bearing (REB), a fusion method by combining robust empirical mode decomposition (REMD) with adaptive maximum second-order cyclostationarity blind deconvolution (AMCBD) is proposed in the paper. The advantage of REMD in determining the optimal iteration number of a sifting process and the advantage of AMCBD in setting the key parameter (targeted cyclic frequency or fault period) appropriately are utilized comprehensively by the proposed method. Firstly, in view of the multi-component and modulation characteristic of the vibration signal of REB, REMD is used to extract the useful component from the multi-component and modulated signal. Then, AMCBD is used to process the selected useful component to further highlight the cyclostationary and impulse characteristics of the vibration signal of faulty REB. Compared with traditional maximum second-order cyclostationarity blind deconvolution (MCBD) method, AMCBD has the advantage of no needing prior knowledge of the faulty REB such as the targeted cyclic frequency or fault period. At last, envelope spectral (ES) is applied on the signal handled by AMCBD and satisfactory fault extraction feature result is obtained. Effectiveness of the proposed method is verified through simulated, experimental, and engineering signals, and its superiority is also presented through comparison study. [ABSTRACT FROM AUTHOR]

As the most commonly used support component in engineering, rolling element bearing is also the most prone-to-failure part. The vibration signal of faulty bearing will take on repetitive impact and modulation characteristics, and the two features are often difficult to be extracted by conventional fault feature extraction methods such as envelope spectral. The main reasons are due to the influence of strong background noise, the signal attenuation of the acquisition path, and the early weak failure characteristics. To solve the above problem, a weak fault feature extraction method of rolling element bearing by combing improved minimum entropy de-convolution with enhanced envelope spectral is proposed in the paper. The enhancement effect of improved minimum entropy de-convolution on impact features and the satisfactory extraction effect of EES on repetitive impact and modulation features are utilized comprehensively by the proposed method. Firstly, improved minimum entropy de-convolution is used to filter the vibration signal of faulty bearing to enhance the impact characteristics, and the influence of signal acquisition path on the attenuation of the signal characteristics is also weakened at the same time. Then, enhanced envelope spectral is performed on the filtered signal, and the repetitive impact and modulation characteristics of vibration signal are extracted synchronously. In order to solve the shortcomings of the current commonly used de-convolution methods, an improved minimum entropy de-convolution method based on D-norm is proposed, which can solve the interference caused by random impulsive signals effectively. In addition, compared with the conventional method such as envelope spectral, the enhanced envelope spectral method could extract the repetitive impact and modulation characteristics of the faulty signal simultaneously much more effectively. Effectiveness and superiority of the proposed method are verified through simulation, experiment, and engineering application. [ABSTRACT FROM AUTHOR]

Vibration signals of rolling element bearing's early weak fault are often submerged by some interference components. To extract early weak fault features accurately, a weak fault feature enhancement method of rolling element bearing based on resonance sparse decompositionand multi-objective information frequency band selection is proposed. This method makes full use of resonance sparse decomposition in filtering the interferences and multi-objective information frequency band selection in enhancing impulsive and cyclostationary features of rolling element bearing's early weak fault simultaneously. First, resonance sparse decomposition is used as the preprocessing program of multi-objective information frequency band selection to filter the interferences (such as rotating frequency with its harmonics) of rolling element bearing's early weak fault characteristic components. Then, the filtered components containing main fault information are analyzed by MIFBS to establish the best band-pass filter. Finally, the envelope spectrum analysis is applied on the filtered vibration signal, and the fault characteristic frequency with its harmonics is extracted. To achieve the optimal output parameters of multi-objective information frequency band selection, fusion indexes based on time- and frequency-domain estimators are proposed and used to balance the enhancement of impulsive and cyclostationary characteristics of rolling element bearing's early weak fault signals. Compared with most of the existing methods mainly based on single time-domain estimators or frequency-domain estimators to improve performance spectral kurtosis, the proposed fusion indexes overcome their defects and could enhance the impulsive and cyclostationary features of rolling element bearing's early weak fault simultaneously. Effectiveness of the proposed method is verified through simulation and experiment. Besides, its advantage over the other related methods is also presented. [ABSTRACT FROM AUTHOR]

The vibration signal containing the fault feature is buried by the strong background noise usually (the noise is referring to the other signals not containing the fault feature in the general sense) when a fault arises in rotating machinery, so the collected vibration signal needs to be processed normally in order to get a correct diagnosis result. Sparse representation theory is a relatively new signal processing method and matching pursuit (MP) is the classical sparse representation method. However, in the traditional MP method only one kind of atom dictionary is constructed to match the fault feature hidden in the vibration signal, which will cause an unsatisfactory analysis result usually when several different kinds of components arise in the vibration signal synchronously. This paper proposes a novel fault feature strengthening method basing on MP to solve the above problem, and the proposed method constructs several different kinds of atom dictionary based on a multi feature pattern set to match the multi-components buried in the collected vibration signal of rotating machinery. Firstly, the effectiveness and advantages of the proposed method are verified by kinds of typical simulation signals of rotating machinery. Then the proposed method is extended to preprocess the vibration signals of bearing and gearbox, and the corresponding analysis results prove that the proposed method has obviously a better fault feature extraction effect on the vibration signal of rotating machinery than the traditional MP method, and the proposed method lays the foundation for further correct fault analysis. [ABSTRACT FROM AUTHOR]

The bispectrum of rolling element bearing compound faults contains abundant fault characteristic information, and how to extract the fault feature effectively is a key problem. The fault diagnosis method of rolling element bearing compound faults based on Sparse No-Negative Matrix Factorization (SNMF)-Support Vector Data Description (SVDD) is proposed in the paper. The figure handling method SNMF is used firstly in fault feature extraction of the bispectrums of rolling element bearing different kinds of compound faults and the sparse coefficient matrices of the bispectrums are obtained. The sparse coefficient matrices are used as training and test input vectors of SVDD. At last, the three kinds of rolling element bearing compound faults (inner race outer race compound faults, outer race rolling element compound faults and inner race outer race rolling element compound faults) are classified correctly. In order to verify the advantages of the proposed method, the diagnosis results of the same three kinds of rolling element bearing compound faults based on No-Negative Matrix Factorization (NMF)-SVDD is used as comparison. The proposed method provides a new idea for fault diagnosis of rolling element bearing compound faults. [ABSTRACT FROM AUTHOR]

In order to realize equipment’s near-zero downtime and maximum productivity a rolling bearing performance degradation assessment is proposed, which is one of the most important techniques. The traditional feature extraction methods based on stationary and Gauss characteristics were unfit to handle non-stationary and non-Gauss signals when the rolling bearing performance begins to degrade. The higher-order statistics (HOS) are fit for handling the non-stationary and non-Gauss signals. Bispectrum not only possesses all the properties of HOS but also has a higher computational efficiency because of a lower order compared with other types of HOS. The support vector data description (SVDD) is a single value classification method which can overcome the problem of a lack of samples in the degradation process of the rolling bearing. A method is proposed in the paper which is based on the bispectrum and SVDD. Firstly, the rolling bearing data of normal state is collected and handled by bispectrum. Based on feature vectors extracted from normal data an SVDD model fitting a tight hypersphere around them is trained. The general distance of test data handled by the bispectrum to this hypersphere is used as the degradation index. In the end, through simulation and a rolling bearing’s accelerated life test, the effectiveness and feasibility of the proposed method is verified. [ABSTRACT FROM PUBLISHER]

The rolling bearing fault signal under strong background noise is very weak because of environmental noise impaction and the attenuation of signal. The feature extraction of rolling bearings’ weak fault is very important in avoiding serious disaster, but it is also very difficult. Sparse decomposition has been used in the fault feature extraction of rolling bearings. But its performance is very poor when the background noise is very strong. This text combines the minimum entropy de-convolution (MED) and sparse decomposition to extract the feature of a rolling bearing’s weak fault. Firstly, the rolling bearing weak fault signal with strong background noise is de-noised using the MED method, and subsequently the de-noised signal is handled by sparse decomposition. Finally, the fault feature extraction method-envelope demodulation is carried on the last given signal and better results are obtained. In conclusion, through simulation and experiment the effectiveness and the feasibility of the proposed method are verified. [ABSTRACT FROM PUBLISHER]