Shift-invariant Sparse Coding Research Articles

Three dictionary learning algorithms and their applications for marine controlled source electromagnetic data denoising

Marine controlled source electromagnetic (MCSEM) is profoundly used for undersea resources exploration. The effective signal is easily contaminated by kinds of noise when the transmitter-receiver offset is large. Suppressing the noise influence is vital to improve data quality and further interpretation accuracy. Denoising becomes a research focus with the widespread application of the MCSEM technique. Many denoising approaches are proposed by different researchers. However, most of them only target a single type of noise, which severely limits the application of these approaches. The fast-developing dictionary learning technique paves a new way for MCSEM data denoising. Currently, typical dictionary learning algorithms include k-means singular value decomposition (K-SVD), data-driven tight frame (DDTF), shift-invariant sparse coding (SISC) and so on. These three algorithms are different in principles and arithmetic processes. Their applications for MCSEM data denoising are explored for the first time in this article. Besides, a comparative analysis of these three noise reduction methods is carried out. The comparison proves the effectiveness and superiority of the K-SVD, followed by the DDTF method. Besides, all these denoising methods are applied to the field data. The results further corroborates the above conclusions.

Noise Attenuation for CSEM Data via Deep Residual Denoising Convolutional Neural Network and Shift-Invariant Sparse Coding

To overcome the interference of noise on the exploration effectiveness of the controlled-source electromagnetic method (CSEM), we improved the deep learning algorithm by combining the denoising convolutional neural network (DnCNN) with the residual network (ResNet), and propose a method based on the residual denoising convolutional neural network (ResDnCNN) and shift-invariant sparse coding (SISC) for denoising CSEM data. Firstly, a sample library was constructed by adding simulated noises of different types and amplitudes to high-quality CSEM data collected. Then, the sample library was used for model training in the ResDnCNN, resulting in a network model specifically designed for denoising CSEM data. Subsequently, the trained model was employed to denoise the measured data, generating preliminary denoised data. Finally, the preliminary denoised data was processed using SISC to obtain the final denoised high-quality data. Comparative experiments with the ResNet, DnCNN, U-Net, and long short-term memory (LSTM) networks demonstrated the significant advantages of our proposed method. It effectively removed strong noise such as Gaussian, impulse, and square wave, resulting in an improvement of the signal-to-noise ratio by nearly 20 dB. Testing on CSEM data from Sichuan Province, China, showed that the apparent resistivity curves plotted using our method were smoother and more credible.

Feature Extraction of Bearing Weak Fault Based on Sparse Coding Theory and Adaptive EWT

In industry, early fault signals of rolling bearings are submerged in strong background noise, causing a low signal-to-noise ratio (SNR) and difficult diagnosis. This paper proposes a fault feature extraction method based on an optimized Laplacian wavelet dictionary (LWD) and the feature symbol search (FSS) algorithm to extract early fault characteristic frequencies of bearings under low SNR. As the morphological parameters of the Laplace wavelet dictionary and sparse coefficients are not easy to obtain, this method uses the adaptive empirical wavelet transform (AEWT) to determine the morphological parameters of the Laplace wavelet. Firstly, AEWT is applied to obtain the different frequency components, and the combination index is utilized for optimal component selection. Then, the morphological parameters of LWD are determined by AEWT processing, by which the overcomplete dictionary that best matches the signal can be obtained. Finally, the optimal sparse representation of the component signal in the dictionary is calculated by FSS, which helps to achieve sparse denoising and enhance the impact features. The effectiveness of the method is verified by simulation. The effectiveness and advantages of LWDFSS-AEWT are verified by experiment in comparison with methods such as fast spectral kurtosis (FSK), correlation filtering (CF), shift-invariant sparse coding (SISC), base pursuit denoising (BPDN) and wavelet packet transform Kurtogram (WPT Kurtogram).

Dictionary learning and shift-invariant sparse coding denoising for controlled-source electromagnetic data combined with complementary ensemble empirical mode decomposition

Controlled-source electromagnetic (CSEM) data recorded in industrialized areas are inevitably contaminated by strong cultural noise. Traditional noise attenuation methods are often ineffective for intricate aperiodic noise. To address the abovementioned problem, we have developed a novel noise isolation method based on the fast Fourier transform, complementary ensemble empirical mode decomposition (CEEMD), and shift-invariant sparse coding (SISC, an unsupervised machine-learning algorithm under a data-driven framework). First, large powerline noise is accurately subtracted in the frequency domain. Then, the CEEMD-based algorithm is used to correct the large baseline drift. Finally, taking advantage of the sparsity of periodic signals, SISC is applied to autonomously learn a feature atom (the useful signal with a length of one period) from the detrended signal and recover the CSEM signal with high accuracy. We determine the performance of the SISC by comparing it with three other promising signal processing methods, such as the mathematic morphology filtering, soft-threshold wavelet filtering, and K-singular-value decomposition (another dictionary learning method) sparse decomposition. Experimental results illustrate that SISC provides the best performance. Robustness test results indicate that SISC can increase the signal-to-noise ratio of noisy signal from 0 to more than 15 dB. Case studies of synthetic and real data collected in the Chinese provinces of Sichuan and Yunnan indicate that our method is capable of effectively recovering the useful signal from the observed data contaminated with different kinds of strong ambient noise. The curves of U/I and apparent resistivity after applying our method improved greatly. Moreover, our method performs better than the robust estimation method based on correlation analysis.

Improved shift-invariant sparse coding for noise attenuation of magnetotelluric data

Magnetotelluric (MT) method is widely used for revealing deep electrical structure. However, natural MT signals are susceptible to cultural noises. In particular, the existing data-processing methods usually fail to work when MT data are contaminated by persistent or coherent noises. To improve the quality of MT data collected with strong ambient noises, we propose a novel time-series editing method based on the improved shift-invariant sparse coding (ISISC), a data-driven machine learning algorithm. First, a redundant dictionary is learned autonomously from the raw MT data. Second, cultural noises are reconstructed using the learned dictionary and the orthogonal matching pursuit (OMP) algorithm. Finally, the de-noised MT data are obtained by subtracting the reconstructed cultural noises from the raw MT data. The synthetic data, field experimental data and measured data are tested to verify the effectiveness of the newly proposed method. The results show that our new scheme can effectively remove strong cultural noises and has better adaptability and efficiency than the predefined dictionary-based methods. The method can be used as an alternative when a remote reference station is not available.

Vegetation High-Impedance Faults’ High-Frequency Signatures via Sparse Coding

High-impedance faults (HIFs) behavior in power distribution systems depends on multiple factors, making it a challenging disturbance to model. Factors, such as network characteristics and impedance surface, can change the phenomena so intensely that insights about their behavior may not translate well between faults with different parameters. Signal processing techniques can help reveal patterns from specific types of fault, given the availability of sampled data from real faults. The methodology described in this article uses the shift-invariant sparse coding technique on a data set of staged vegetation HIFs to address this hypothesis. The technique facilitates the uncoupling of shifted and convoluted patterns present in the recorded fault signals, while a methodology to correlate them with fault occurrences is proposed. The investigation of underdiscussed high-frequency fault signals from a specific type of fault (small current vegetation HIFs) distinguishes this article from related works. The methodology to attest the found patterns as fault signatures and their analysis while using a particular high-frequency sampling method are key novel aspects presented. Nonetheless, the evidence of consistent behavior in real vegetation HIFs at higher frequencies that could assist their detection is the main contribution of this article. These results can enhance phenomena awareness and support future methodologies dealing with such disturbances.

Feature extraction of the weak periodic signal of rolling element bearing’ early fault based on shift invariant sparse coding

When fault such as pit failure arises in the rolling element bearing the vibration signal of which will take on periodic characteristics, and the abrupt failure of rotating machinery can be avoided effectively if the weak periodic characteristics of the early fault stage is extracted timely. However, the periodic characteristics of bearing’ early weak fault is hard to be extracted usually and the reasons can be boiled to as following: Firstly, the weak periodic signal of rolling element bearing’ early fault stage is buried by the strong background noise. Secondly, the weak fault cannot show the complete shock attenuation impulsive characteristic due to its weak energy, so the traditional wavelet transform would not work effectively if a proper wavelet basis function fitting for analyzing the impulsive characteristics is not selected. To solve the above two problems, a feature extraction method of the weak periodic signal of rolling element bearing’ early fault based on Shift Invariant Sparse Coding (SISC) originating from sparse representation is proposed in the paper. To capture the underlying structure of machinery fault signal, SICS provides an effective basis functions learning scheme by solving the flowing two convex optimization problems iteratively: 1) L1-regularized least squares problem. 2) L2-constrained least squares problem. The fault feature can be probably contained and extracted if optimal latent component is filtered among these basis functions. The feasibility and effectiveness of the proposed method are verified through the corresponding simulation and experiment.

Wind turbine planetary gearbox feature extraction and fault diagnosis using a deep-learning-based approach

Planetary gearboxes are widely used in the drivetrain of wind turbines. Planetary gearbox fault diagnosis is very important for reducing the downtime and maintenance cost and improving the safety, reliability, and life span of the wind turbines. The wind energy industry is currently using condition monitoring systems to collect massive real-time data and conventional vibratory analysis as a standard method for planetary gearbox condition monitoring. As an attractive option to process big data for fault diagnosis, deep learning can automatically learn features that otherwise require much skill, time, and experience. This article presents a new deep-learning-based method for wind turbine planetary gearbox fault diagnosis developed by a large memory storage and retrieval neural network with dictionary learning. The developed approach can automatically extract self-learned fault features from raw vibration monitoring data and perform planetary gearbox fault diagnosis without supervised fine-tuning process. From the raw vibration monitoring data, a dictionary is first learned by a large memory storage and retrieval with dictionary learning network. Based on the learned dictionary, a sparse representation of the raw vibration signals is generated by shift-invariant sparse coding and input to a large memory storage and retrieval network classifier to obtain fault diagnosis results. The structure of the large memory storage and retrieval with dictionary learning is determined by optimal selection of the sliding box size to generate sub-patterns from the vibration data. The effectiveness of the presented method is tested and validated with a set of seeded fault vibration data collected at a planetary gearbox test rig in laboratory. The validation results have shown a promising planetary gearbox fault diagnosis performance with the presented method.

Fault detection of a wheelset bearing in a high-speed train using the shock-response convolutional sparse-coding technique

The wheelset bearing is an essential mechanical component of a high-speed train, and its fault detection is of great importance for ensuring the safety of high-speed train service. However, the strong nonlinear and non-stationary modulation characteristics of the impulses induced by bearing faults, noises and interferences make fault detection of the wheelset bearing difficult and complex. The impulses induced by bearing faults have convolutional sparse characteristics, and can be described as the convolution of shock responses and time location coefficients. This paper presents a new fault detection method based on the shock-response convolutional sparse-coding technique (SRCSCT) to resolve the two inverse problems related to convolution characteristics. One of the problems is that shock responses are learned directly from vibration signals via the shift-invariant dictionary learning (SIDL) of SRCSCT; the other is that the time location coefficients are inferred from vibration signals via shift-invariant sparse coding (SISC) of SRCSCT. Shock responses can reflect the high-level structures of impulses embedded in vibration signals. Time location coefficients can be used to determine the time of impulse occurrence. The convolutions of learned shock responses and inferred corresponding time location coefficients are used to extract impulses submerged in vibration signals and are convenient for post-processing methods, such as Hilbert envelope analysis. Both the simulated signals and real vibration signals collected from a wheelset bearing test bench are used to verify the proposed method. The results show that SRCSCT not only effectively detects faults of the wheelset bearing but also accurately characterizes the fault dynamic behaviours of the bearing defects.

Adaptive machinery fault diagnosis based on improved shift-invariant sparse coding

In machinery fault diagnosis, it is common that one kind of fault may correspond to several conditions, these conditions may contain different loads, different speeds and so on. When using conventional intelligent machinery fault diagnosis methods on diagnosing this kind of faults, if only one condition among all of these conditions was trained, when using this trained classifier for diagnosing fault which containing all conditions, it would obtain a classification result with higher error, it is the problem of robustness; but if we train all these data in each condition, the robustness can be improved a lot, but the time would be wasted. In order to balance these two aspects of fault diagnosis which seem contradict with each other, someone proposed a new method which based on shift-invariant sparse coding (SISC) method, this method can learn features from each condition of the same fault, and these features are adaptive to other conditions, which solve the first problem, but time efficiency of this algorithm is lower, in this paper, by improving the efficiency of shift-invariant sparse coding, we can reduce a lot of time on learning features. Through the experiment testing, it showed that this new method proposed in this paper produced better performance than SISC algorithm.

Blind source separation of rolling element bearing’ single channel compound fault based on Shift Invariant Sparse Coding

The mechanical vibration source signal collected by sensor often includes a variety of internal vibration source of contributions such as gears, bearings, shaft and so on. It is often hoped to achieve effective separation of the source signal in order to obtain better fault diagnosis result. Blind source separation of the failure signal of rolling element bearing is a challenging task due to the above reasons, especially in the case of single channel compound fault. A method of blind source separation of rolling element bearing’s single channel compound fault based on Shift-Invariant Sparse Coding (SISC) is proposed in the paper. The waveform characteristic of different fault signal has some difference in the structure even that the same impulse characteristics of signals are produced by different parts, and the difference can be captured by the SISC method with the following reasons: Firstly, a set of basis functions is trained and obtained by SISC feature self-study method (The number of the basis functions is big necessarily). Then the potential components are constructed using the corresponding obtained basis functions. At last, the clustering operation is carried out using the structural similarity of the potential components, and the clustering signals represent the different vibration source signals. Apply the traditional vibration signal handling method such as envelope demodulation to the obtained clustering signals respectively and better fault diagnosis results are obtained at last.

Sparse representation based latent components analysis for machinery weak fault detection

Weak machinery fault detection is a difficult task because of two main reasons (1) At the early stage of fault development, signature of fault related component performs incompletely and is quite different from that at the apparent failure stage. In most instances, it seems almost identical with the normal operating state. (2) The fault feature is always submerged and distorted by relatively strong background noise and macro-structural vibrations even if the fault component already performs completely, especially when the structure of fault components and interference are close. To solve these problems, we should penetrate into the underlying structure of the signal. Sparse representation provides a class of algorithms for finding succinct representations of signal that capture higher-level features in the data. With the purpose of extracting incomplete or seriously overwhelmed fault components, a sparse representation based latent components decomposition method is proposed in this paper. As a special case of sparse representation, shift-invariant sparse coding algorithm provides an effective basis functions learning scheme for capturing the underlying structure of machinery fault signal by iteratively solving two convex optimization problems: an L1-regularized least squares problem and an L2-constrained least squares problem. Among these basis functions, fault feature can be probably contained and extracted if optimal latent component is filtered. The proposed scheme is applied to analyze vibration signals of both rolling bearings and gears. Experiment of accelerated lifetime test of bearings validates the proposed method׳s ability of detecting early fault. Besides, experiments of fault bearings and gears with heavy noise and interference show the approach can effectively distinguish subtle differences between defect and interference. All the experimental data are analyzed by wavelet shrinkage and basis pursuit de-noising (BPDN) method for comparison.

Adaptive feature extraction using sparse coding for machinery fault diagnosis

In the signal processing domain, there has been growing interest in sparse coding with a learned dictionary instead of a predefined one, which is advocated as an effective mathematical description for the underlying principle of mammalian sensory systems in processing information. In this paper, sparse coding is introduced as a feature extraction technique for machinery fault diagnosis and an adaptive feature extraction scheme is proposed based on it. The two core problems of sparse coding, i.e., dictionary learning and coefficients solving, are discussed in detail. A natural extension of sparse coding, shift-invariant sparse coding, is also introduced. Then, the vibration signals of rolling element bearings are taken as the target signals to verify the proposed scheme, and shift-invariant sparse coding is used for vibration analysis. With the purpose of diagnosing the different fault conditions of bearings, features are extracted following the proposed scheme: basis functions are separately learned from each class of vibration signals trying to capture the defective impulses; a redundant dictionary is built by merging all the learned basis functions; based on the redundant dictionary, the diagnostic information is made explicit in the solved sparse representations of vibration signals; sparse features are formulated in terms of activations of atoms. The multiclass linear discriminant analysis (LDA) classifier is used to test the discriminability of the extracted sparse features and the adaptability of the learned atoms. The experiments show that sparse coding is an effective feature extraction technique for machinery fault diagnosis.

Monte Carlo Methods for Adaptive Sparse Approximations of Time-Series

This paper deals with adaptive sparse approximations of time-series. The work is based on a Bayesian specification of the shift-invariant sparse coding model. To learn approximations for a particular class of signals, two different learning strategies are discussed. The first method uses a gradient optimization technique commonly employed in sparse coding problems. The other method is novel in this context and is based on a sampling estimate. To approximate the gradient in the first approach we compare two Monte Carlo estimation techniques, Gibbs sampling and a novel importance sampling method. The second approach is based on a direct sample estimate and uses an extension of the Gibbs sampler used with the first approach. Both approaches allow the specification of different prior distributions and we here introduce a novel mixture prior based on a modified Rayleigh distribution. Experiments demonstrate that all Gibbs sampler based methods show comparable performance. The importance sampler was found to work nearly as well as the Gibbs sampler on smaller problems in terms of estimating the model parameters, however, the method performed substantially worse on estimating the sparse coefficients. For large problems we found that the combination of a subset selection heuristic with the Gibbs sampling approaches can outperform previous suggested methods. In addition, the methods studied here are flexible and allow the incorporation of additional prior knowledge, such as the nonnegativity of the approximation coefficients, which was found to offer additional benefits where applicable.