K-means Singular Value Decomposition Research Articles

A Hierarchical Fault Diagnosis Model for Planetary Gearbox with Shift-invariant Dictionary and OMPAN

Abstract Planetary gearbox has been widely applied in the mechanical transmission system, and the failure types of planetary gearbox are more and more diversified. The conventional fault diagnosis methods focus on identifying the faults in the fault library, but ignored the faults outside the fault library. However, it is impossible to build a fault library for all failure types. Targeting the problem of identifying the faults outside the fault library, a hierarchical fault diagnosis method for planetary gearbox with shift-invariant dictionary and orthogonal matching pursuit with adaptive noise (OMPAN) is proposed in this paper. By k-means singular value decomposition (K-SVD) dictionary learning method and shift-invariant strategy, a shift-invariant dictionary is constructed so that the normal modulation components of signals can be completed decomposed. OMPAN algorithm is proposed, which uses the white Gaussian noise to improve the solution method of the orthogonal matching pursuit (OMP) algorithm so that it can separate the modulation components in the signal more accurately. The fault feature extraction is developed via shift-invariant dictionary and OMPAN. A hierarchical classifier is proposed with 3 sub-classifiers so that both the faults in the fault library and the faults outside the fault library are identified. The effectiveness of the proposed hierarchical fault diagnosis method is validated by a planetary gearbox experiment. Result show that the proposed shift-invariant dictionary and OMPAN method has achieved a superior performance in highlighting fault features compared with other two sparse decomposition methods. The proposed hierarchical fault diagnosis approach has achieved a good performance both in classification of the faults in the fault library and identification of the faults outside the fault library.

Non-Local SAR Image Despeckling Based on Sparse Representation

Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms.

CORDIC KSVD based Online Dictionary Learning for Speech Enhancement on ASIC/FPGA Platforms

Background: The enhancement of real-world speech signals is still a challenging task to eliminate noises, namely reverberation, background, street, and babble noises. Recently learned methods like dictionary learning have become increasingly popular and showed promising results in speech enhancement. The K-means Singular Value Decomposition (KSVD) algorithm is best suited for dictionary learning among many sparse representation algorithms. Moreover, the orthogonal matching pursuit (OMP) based algorithm used for signal recovery is given. The orthogonal matching pursuit (OMP) based algorithm for signal recovery gives the best enhancement results. On the other hand, FPGAs and ASICs are widely used to accelerate speech enhancement applications. FPGAs are commonly used in healthcare and consumer applications, where speech enhancement plays a crucial role. Methods: This paper proposes a modified KSVD algorithm that can easily be implemented onto hardware platforms like FPGAs and ASICS. Instead of using the double-precision arithmetic for the singular value decomposition part of the KSVD algorithm, we proposed to use CORDIC (Coordinate Rotation Digital Computer) based QR decomposition and QR-based singular value decomposition in dictionary learning. Results: The proposed KSVD algorithm is optimal with the CORDIC algorithm that can reduce by 7-8 times the processing time. Conclusion: The finding indicates that the proposed work is best suited to FPGA or ASIC platforms.

Deep Kernel Dictionary Learning for detection of wave breaking features in Atmospheric Gravity Waves

Atmospheric Gravity Waves play a significant role in the Middle Atmosphere Dynamics and breaking of Gravity Waves leads to turbulence. To confirm the breaking of Atmospheric Gravity Waves, we require Wind Velocity profiles with high accuracy and the problem in the existing scenario is the Wind velocity measuring instruments are not accurate at the altitude of interest and are very sparse. We came up with a solution by taking advantage of Dictionary Learning and Deep Learning approach for the detection of the Wave Breaking events from atmospheric temperature perturbations instead of looking for wind velocity profiles.In the present work, we discuss incorporating Kernels into Deep Dictionary Learning. The Deep Dictionary Learning algorithm, introduced recently, shows an improvement in feature detection in comparison to k-means Singular Value Decomposition (KSVD) Dictionary Learning, Convolution Neural Network, and LSTM Auto-Encoders. Incorporating Kernels into Deep Dictionary Learning increases the effectiveness of the detection of the Wave Breaking events. The potential of the proposed method is demonstrated with a case study on the detection of atmospheric Gravity Wave Breaking event leading to turbulence in the atmosphere using satellite data (Aura-Microwave Limb Sounder) and is validated using data available from the ground-based instruments.

Ensemble empirical mode decomposition and stacking model for filtering borehole distributed acoustic sensing records

We have evaluated the ensemble empirical mode decomposition (EEMD) and stacking model for borehole seismic-data denoising. The borehole records collected by distributed acoustic sensing (DAS) technology have multitype noise contamination, and it is difficult to attenuate these noises while recovering the seismic waves well. We first perform EEMD on the seismic data to obtain the signal-to-noise modal components, then extract the time and frequency information of the decomposed modes using six feature factors, and finally introduce an ensemble learning method to classify the acquired modal features effectively. Stacking is the ensemble learning technique we used in our study. This technique integrates several diverse basic ensemble models using the meta-learning strategy and constructs a highly integrated framework with superior performance and good generalization. In addition, the basic ensemble models consist of many decision tree classifiers following two different ideas of parallelization and serialization. The feature extraction process provides sufficient DAS feature data for the training process of the framework. Synthetic and real experimental results demonstrate that the stacking integration framework effectively separates the signal-to-noise modal features of the borehole DAS records. Furthermore, the EEMD-stacking method performs better than wavelet transform, intrinsic time-scale decomposition, robust principal component analysis, k-means singular value decomposition, and median filtering on the denoising task.

Non-negative constrained dictionary learning for compressed sensing of ECG signals

Objective. Overcomplete dictionaries are widely used in compressed sensing (CS) to improve the quality of signal reconstruction. However, dictionary learning under the -norm or -norm constraint inevitably produces dictionary atoms that are negatively correlated with the original signal; meanwhile, when we use a sparse linear combination of dictionary atoms to represent a signal, it is suboptimal for the dictionary atoms to “cancel each other out” by addition and subtraction to approximate the sample. In this paper, we propose a non-negative constrained dictionary learning (NCDL) algorithm to improve the reconstruction performance of CS with electrocardiogram (ECG) signals. Approach. Non-NCDL was divided into an encoding stage and a dictionary learning stage. In the encoding stage, non-negative constraints were imposed on the encoding coefficients and obtained the sparse solution using the alternating direction method of multipliers. At the same time, a penalty term was integrated into the objective function in order to remove small coding coefficients and achieve the effect of sparse coding. In the dictionary learning stage, the block coordinate descent algorithm was utilized to update the dictionary with a view to obtaining an overcomplete dictionary. Results. The performance of the proposed NCDL algorithm was evaluated using the standard MIT-BIH database. Quantitative performance metrics, such as percent root mean square difference (PRD1) and root mean square error, were compared with existing CS approaches to quantify the efficacy of the proposed scheme. For a PRD1 value of 9%, the compression ratio (CR) of the NCDL approach was around 2.78. When CR ranged from 1.05 to 2.78, the proposed NCDL approach outperformed the method of optimal direction, k-means singular value decomposition, and online dictionary learning approaches in ECG signal reconstruction based on CS. Significance. This promising preliminary result demonstrates the capability and feasibility of the proposed bioimpedance method and may open up a new direction for this application. The non-NCDL method proposed in this paper can be used to obtain a sparse basis and improve the performance of CS reconstruction.

Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method

Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method.

Mechanical Compound Fault Analysis Method Based on Shift Invariant Dictionary Learning and Improved FastICA Algorithm

For mechanical compound fault, it is of great significance to employ the vibration signal of a single-channel compound fault to analyze and realize the separation of multiple fault sources, which is essentially the problem of single-channel blind source separation. Shift invariant K-means singular value decomposition (shift invariant K-SVD) dictionary learning is suitable to extract the periodic and repeated fault features of a rotating machinery fault, hence in this article a single-channel compound fault analysis method is put forward which combines shift invariant K-SVD with improved fast independent component analysis (improved FastICA) algorithm. Firstly, based on single-channel compound fault signal, the shift invariant K-SVD algorithm can be used for learning multiple latent components that can be constructed as a virtual multi-channel signal. Then the improved FastICA algorithm is utilized to realize the separation of multiple fault source signals. With regard to the FastICA algorithm, the third-order convergence Newton iteration method is adopted to improve convergence speed. Moreover, in order to address the problem that FastICA is very sensitive to initialization, a steepest descent method can be applied. The experimental analysis of the compound fault of rolling bearing verifies that the presented method is effective to separate multiple fault source signals and the improved FastICA algorithm can increase convergence rate and overcome the problem of sensitivity to initialization.

A novel denoising algorithm for medical images based on the non‐convex non‐local similar adaptive regularization

Sparse representation is a powerful statistical image modelling technique and has been successfully applied to image denoising. For a given patch, a non-convex non-local similarity adaptive method is adopted for sparse representation of images. First, it uses the autoregressive model to perform dictionary learning from sample patch datasets. Second, the sparse representation of an image introduces non-convex non-local self-similarity as the regularization term. In order to make better use of the sparse regularization method for image denoising, the parameters used in this study are estimated using adaptive methods. This model is more efficient and accurate, Compared with K-means singular value decomposition (KSVD) algorithm, a generalized K-means clustering method, total variation of population sparsity (GSTV) algorithm, adaptive sparse domain selection (ASDS) algorithm, forward denoising convolutional neural network (DnCNNs), a fast and flexible Convolutional Neural Network image denoising method (FFNNet) and operator-splitting algorithm to minimize the Euler elastica functional (OSEEF). Image noise-reduction experiments confirmed that using the adaptive regularization method, the results in peak signal to noise ratio (PSNR) and visual opinion are better than other algorithms.

Karhunen Loeve Transform with adaptive dictionary learning for coherent and random noise attenuation in seismic data

Seismic reflection data are used by geologists to identify the best site for oil and gas explorations. The raw seismic image cannot be directly used for explorations because the noises may hinder the primary reflections and result in misinterpretations. In the proposed research work, the denoising method with adaptive dictionary learning is attempted to attenuate the coherent and random noises which affect the seismic reflections. In this method, Karhunen Loeve Transform (KLT) is combined with K-means Singular Value Decomposition (KSVD) to improve the Signal to Noise Ratio (SNR) of the seismic data. The combination of fixed transform using KLT and learning-based dictionary using KSVD remove the redundant data while retaining the necessary data for further interpretations. KLT is applied on the whole seismic image to decorrelate the coefficients and retain the primary reflections. The horizontal events in the seismic image are preserved as they represent the eigen images with large energy. The KSVD is applied to the KLT resultant data, which denoises the patch of data while simultaneously updating the dictionary. The noise is eliminated using local sparsity of the image where mutually overlapping small image blocks are learned to yield the self-adaptive redundant dictionary. It is then used to obtain the sparse representation of the image blocks by eliminating noise. The algorithm is tested for both synthetic and field seismic images, and the results indicate a better reduction of random and coherent noise with the acceptable execution time compared to other denoising algorithms used. The KLT combined with KSVD method decorrelate the seismic data from noise by preserving the primary reflections and discarding the redundant noise.

Detection of Defects in Additively Manufactured Stainless Steel 316L with Compact Infrared Camera and Machine Learning Algorithms

Additive manufacturing (AM) is an emerging method for cost-efficient fabrication of nuclear reactor parts. AM of metallic structures for nuclear energy applications is currently based on the laser powder bed fusion process, which can introduce internal material flaws, such as pores and anisotropy. Integrity of AM structures needs to be evaluated nondestructively because material flaws could lead to premature failures due to exposure to high temperature, radiation and corrosive environments in a nuclear reactor. Thermal tomography (TT) provides a capability for non-destructive evaluation of sub-surface defects in arbitrary size structures. We investigate TT of AM stainless steel 316L specimens with imprinted internal porosity defects using a relatively low-cost, small form factor infrared camera based on an uncooled micro-bolometer detector. Sparse coding-related K-means singular value decomposition machine learning, image processing algorithms are developed to improve the quality of TT images through removal of additive white Gaussian noise without blurring the images.

Sparse representation of Brillouin spectrum using dictionary learning.

Distributed optical fiber Brillouin sensors can monitor the temperature and strain along a fiber by estimating the Brillouin frequency shift (BFS) according to the measured Brillouin spectrum. The system performance is highly dependent on the algorithm of BFS extraction. The well-established Lorentz curve fitting (LCF) method is generally employed because the Brillouin spectrum theoretically satisfies a Lorentz shape. Recently, machine-learning methods have been proposed for more effective BFS extraction, but they have some drawbacks and limitations. The machine-learning algorithms require a large amount of data and high computing power to find suitable extraction methods. However, with prior knowledge, Brillouin spectrum can be treated as a regular signal that requires only three degrees of freedom to define. The unique sparsity characteristics of Brillouin spectrum have not been well studied or exploited. In this paper, we propose a sparse representation method for Brillouin spectrum that extracts three sparse features of the Brillouin spectrum through the dictionary-learning algorithm (K-means singular value decomposition). The correlation between the sparse coefficient and the BFS is experimentally calibrated and verified. The accuracy of the proposed algorithm is comparable to that of LCF, and its processing is six times faster. This sparse representation method for Brillouin spectra is promising as an alternative universal BFS extraction method for distributed Brillouin sensors.

A new K-means singular value decomposition method based on self-adaptive matching pursuit and its application in fault diagnosis of rolling bearing weak fault

Sparse decomposition has excellent adaptability and high flexibility in describing arbitrary complex signals based on redundant and over-complete dictionary, thus having the advantage of being free from the limitations of traditional signal processing methods such as wavelet and fast Fourier transform being imposed by orthogonal basis. Sparse decomposition provides an effective approach for feature extraction of intricate vibration signals collected from rotating machinery. Self-learning over-complete dictionary and pre-defined over-complete dictionary are the two dictionary construction modes of sparse decomposition. Normally, the former mode owns the virtues of much more adaptive and flexible than the latter one, and several kinds of classical self-learning over-complete dictionary methods have been arising in recent years. K-means singular value decomposition is a classical self-learning over-complete dictionary method and has been used in image processing, speech processing, and vibration signal processing. However, K-means singular value decomposition has relative low reconstruction accuracy and poor stability to enhance the desired features. To overcome the above-mentioned shortcomings of K-means singular value decomposition, a new K-means singular value decomposition sparse representation method based on traditional K-means singular value decomposition method was proposed in this article, which uses the sparse adaptive matching pursuit algorithm and an iterative method based on the minimum similarity of atomic structure. The effectiveness and advantage of the proposed method were verified through simulation and experiment.

Visual saliency object detection using sparse learning

In many applications in order to recognise the relationship between user and computer, the position at which the user looks should be detected. To this end, a salient object should be extracted that is attracted to the attention of the viewer. In this study, a new method is proposed to extract the object saliency map, which is based on learning automata and sparse algorithms. In the proposed method, after decomposition of an image to its superpixels, eight features (namely three features in red–green–blue colour space, coalition, central bias, rotation feature, brightness, and colour difference) are extracted. Then the extracted features are normalised to zero mean and unit variance. In this study, K-means singular-value decomposition is used to integrate the extracted features. The performance of the proposed method is compared with that of 20 other methods by applying four new databases, including MSRA-100, ECSSD, MSRA-10K, and Pascal-S. The obtained results show that the proposed method has a better performance compared to the other methods with regard to the prediction of the salient object.

An Enhanced K-Means Clustering Based on K- SVD_DWT Algorithm for Image Segmentation

The complete nature of the image geared by various processes but image segmentation plays vital role. For object illustration, image analysis, visualization and image processing task the image is segmented into useful information by image segmentation. The image is segmented with respect to the opted scenario by image segmentation. The image measurements like texture, color and depth are considered by the segmentation. The plant disease can be spotted and classified in the field of agriculture and image segmentation is essential in image processing. Based on the morphological characteristics of plants, the diseases can be classified. Image segmentation is of importance within the field of image process. This work focuses on K-means Singular Value Decomposition (K-SVD) and Discrete Wavelet Transform (DWT) that is associated with Kmeans clustering for effectual image segmentation of leaf. Image segmentation is the basic pre-processing task to segregate the leaves in several image process applications. The most challenge in analyzing the plant images are locating and segmenting plants. Image segmentation accustomed to discover the objects and limits such as lines, curves, etc. in images. K-means cluster algorithmic program is wide employed in image segmentation to its machine simplicity. However, the clump results obtained from K-means heavily rely on the initial parameters. Mostly, these initial parameters are elite through hit and trial rule that ends up in inconsistency within the image segmentation results. In this work, an improved K-means cluster algorithmic program is projected for image segmentation, using a histogram based mostly initial parameter estimation procedure. To boot, the projected algorithmic program needs less user interaction to work out K-means data format parameters. Some experiments are conducted supported numerous gray images to check the projected approach. The experiment results show that the projected approach will improve the K-means based mostly image segmentation results

Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning

A wheelset bearing is a crucial energy transmission element in high-speed trains. Any parts of the wheelset bearing that have faults may endanger the safety of the railway service. Therefore, it is important to monitor the running condition of a wheelset bearing. The multifault on a wheelset bearing is very common, and these impulsive components generated by different types of faults may interact with each other, which increases the difficulty of entirely identifying those faults. To solve the multifault problem, this paper proposed a hierarchical shift-invariant K-means singular value decomposition (H-SI-K-SVD) to hierarchically separate those multifault impulsive components based on their fault power levels. Each of the separated impulse signals contains only one fault impulse, and the fault information could be highlighted both in time domain and frequency domain. In addition, the sparsity of envelope spectrum (SES) is introduced as an indicator to adaptively tune a key parameter in this method. The effectiveness of the proposed method is verified by both simulation and experimental signals. Compared with ensemble empirical model decomposition (EEMD), the proposed method exhibits better performance in separating the multifault impulsive components and detecting the faults of a wheelset bearing.

A Two-Stage Household Electricity Demand Estimation Approach Based on Edge Deep Sparse Coding

The widespread popularity of smart meters enables the collection of an immense amount of fine-grained data, thereby realizing a two-way information flow between the grid and the customer, along with personalized interaction services, such as precise demand response. These services basically rely on the accurate estimation of electricity demand, and the key challenge lies in the high volatility and uncertainty of load profiles and the tremendous communication pressure on the data link or computing center. This study proposed a novel two-stage approach for estimating household electricity demand based on edge deep sparse coding. In the first sparse coding stage, the status of electrical devices was introduced into the deep non-negative k-means-singular value decomposition (K-SVD) sparse algorithm to estimate the behavior of customers. The patterns extracted in the first stage were used to train the long short-term memory (LSTM) network and forecast household electricity demand in the subsequent 30 min. The developed method was implemented on the Python platform and tested on AMPds dataset. The proposed method outperformed the multi-layer perception (MLP) by 51.26%, the autoregressive integrated moving average model (ARIMA) by 36.62%, and LSTM with shallow K-SVD by 16.4% in terms of mean absolute percent error (MAPE). In the field of mean absolute error and root mean squared error, the improvement was 53.95% and 36.73% compared with MLP, 28.47% and 23.36% compared with ARIMA, 11.38% and 18.16% compared with LSTM with shallow K-SVD. The results of the experiments demonstrated that the proposed method can provide considerable and stable improvement in household electricity demand estimation.

Multi-focus image fusion based on joint sparse representation and optimum theory

Image fusion can be regarded as how to integrate complementary information in images with different focal region together. However, except for complementary information, there is also redundant information in source images. To obtain a better fused image, an efficient image fusion method based on sparse representation and optimal solution was proposed in this paper. Firstly, we obtained adaptive dictionaries based on source images themselves by K-means singular value decomposition. Then, we combined a fixed dictionary with adaptive dictionaries to obtain the joint dictionary. By sparse coding source images with the final joint dictionary, complementary and redundant components could be apart. Next, the optimum theory was employed to fuse complementary components and an optimal solution could be obtained by orthogonal matching pursuit. A fused image was constructed by sparse representation at last. Experimental results showed the proposed method had better visual effects and objective valuable index values.

Impulsive component extraction using shift-invariant dictionary learning and its application to gear-box bearing early fault diagnosis

The impulsive components induced by bearing faults are key features for assessing gear-box bearing faults. However, because of heavy background noise and the interferences of other vibrations, it is difficult to extract these impulsive components caused by faults, particularly early faults, from the measured vibration signals. To capture the high-level structure of impulsive components embedded in measured vibration signals, a dictionary learning method called shift-invariant K-means singular value decomposition (SI-K-SVD) dictionary learning is used to detect the early faults of gear-box bearings. Although SI-K-SVD is more flexible and adaptable than existing methods, the improper selection of two SI-K-SVD-related parameters, namely, the number of iterations and the pattern lengths, has an adverse influence on fault detection performance. Therefore, the sparsity of the envelope spectrum (SES) and the kurtosis of the envelope spectrum (KES) are used to select these two key parameters, respectively. SI-K-SVD with the two selected optimal parameter values, referred to as optimal parameter SI-K-SVD (OP-SI-K-SVD), is proposed to detect gear-box bearing faults. The proposed method is verified by both simulations and an experiment. Compared to the state-of-the-art methods, namely, empirical model decomposition, wavelet transform and K-SVD, OP-SI-K-SVD has better performance in diagnosing the early faults of a gear-box bearing.

Identification Method for Voltage Sags Based on K-means-Singular Value Decomposition and Least Squares Support Vector Machine

Voltage sag is one of the most serious problems in power quality. The occurrence of voltage sag will lead to a huge loss in the social economy and have a serious effect on people’s daily life. The identification of sag types is the basis for solving the problem and ensuring the safe grid operation. Therefore, with the measured data uploaded by the sag monitoring system, this paper proposes a sag type identification algorithm based on K-means-Singular Value Decomposition (K-SVD) and Least Squares Support Vector Machine (LS-SVM). Firstly; each phase of the sag sample RMS data is sparsely coded by the K-SVD algorithm and the sparse coding information of each phase data is used as the feature matrix of the sag sample. Then the LS-SVM classifier is used to identify the sag type. This method not only works without any dependence on the sag data feature extraction by artificial ways, but can also judge the short-circuit fault phase, providing more effective information for the repair of grid faults. Finally, based on a comparison with existing methods, the accuracy advantages of the proposed algorithm with be presented.