Fault Diagnosis of Rotating Equipment Bearing Based on EEMD and Improved Sparse Representation Algorithm

Aiming at the problem that the vibration signals of rolling bearings working in a harsh environment are mixed with many harmonic components and noise signals, while the traditional sparse representation algorithm takes a long time to calculate and has a limited accuracy, a bearing fault feature extraction method based on the ensemble empirical mode decomposition (EEMD) algorithm and improved sparse representation is proposed. Firstly, an improved orthogonal matching pursuit (adapOMP) algorithm is used to separate the harmonic components in the signal to obtain the filtered signal. The processed signal is decomposed by EEMD, and the signal with a kurtosis greater than three is reconstructed. Then, Hankel matrix transformation is carried out to construct the learning dictionary. The K-singular value decomposition (K-SVD) algorithm using the improved termination criterion makes the algorithm have a certain adaptability, and the reconstructed signal is constructed by processing the EEMD results. Through the comparative analysis of the three methods under strong noise, although the K-SVD algorithm can produce good results after being processed by the adapOMP algorithm, the effect of the algorithm is not obvious in the low-frequency range. The method proposed in this paper can effectively extract the impact component from the signal. This will have a positive effect on the extraction of rotating machinery impact features in complex noise environments

Sparse decomposition based on ADMM dictionary learning for fault feature extraction of rolling element bearing

Sparse decomposition is a novel method for the fault diagnosis of rolling element bearing, whether the construction of dictionary model is good or not will directly affect the results of sparse decomposition. In order to effectively extract the fault characteristics of rolling element bearing, a sparse decomposition method based on the over-complete dictionary learning of alternating direction method of multipliers (ADMM) is presented in this paper. In the process of dictionary learning, ADMM is used to update the atoms of the dictionary. Compared with the K-SVD dictionary learning and non-learning dictionary method, the learned ADMM dictionary has a better structure and faster speed in the sparse decomposition. The ADMM dictionary learning method combined with the orthogonal matching pursuit (OMP) is used to implement the sparse decomposition of the vibration signal. The envelope spectrum technique is used to analyze the results of the sparse decomposition for the fault feature extraction of the rolling element bearing. The experimental results show that the ADMM dictionary learning method can updates the dictionary atoms to better fit the original signal data than K-SVD dictionary learning, the high frequency noise in the vibration signal of the rolling bearing can be effectively suppressed, and the fault characteristic frequency can be highlighted, which is very favorable for the fault diagnosis of the rolling element bearing

Detection and classification of non-stationary signals using sparse representations in adaptive dictionaries

Automatic classification of non-stationary radio frequency (RF) signals is of particular interest in persistent surveillance and remote sensing applications. Such signals are often acquired in noisy, cluttered environments, and may be characterized by complex or unknown analytical models, making feature extraction and classification difficult. This thesis proposes an adaptive classification approach for poorly characterized targets and backgrounds based on sparse representations in non-analytical dictionaries learned from data. Conventional analytical orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of non-stationary signals, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They generally do not lead to sparse decompositions (i.e., with very few non-zero coefficients), and use in classification requires separate feature selection algorithms. Pursuit-type decompositions in analytical overcomplete (non-orthogonal) dictionaries yield sparse representations, by design, and work well for signals that are similar to the dictionary elements. The pursuit search, however, has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. One such overcomplete analytical dictionary method is also analyzed in this thesis for comparative purposes. The main thrust of the thesis is learning discriminative RF dictionaries directly from data, without relying on analytical constraints or additional knowledge about the signal characteristics. A pursuit search is used over the learned dictionaries to generate sparse classification features in order to identify time windows that contain a target pulse. Two state-of-the-art dictionary learning methods are compared, the K-SVD algorithm and Hebbian learning, in terms of their classification performance as a function of dictionary training parameters. Additionally, a novel hybrid dictionary algorithm is introduced, demonstrating better performance and higher robustness to noise. The issue of dictionary dimensionality is explored and this thesis demonstrates that undercomplete learned dictionaries are suitable for non-stationary RF classification. Results on simulated data sets with varying background clutter and noise levels are presented. Lastly, unsupervised classification with undercomplete learned dictionaries is also demonstrated in satellite imagery analysis

Sparse and Nonnegative Factorizations For Music Understanding

In this dissertation, we propose methods for sparse and nonnegative factorization that are specifically suited for analyzing musical signals. First, we discuss two constraints that aid factorization of musical signals: harmonic and co-occurrence constraints. We propose a novel dictionary learning method that imposes harmonic constraints upon the atoms of the learned dictionary while allowing the dictionary size to grow appropriately during the learning procedure. When there is significant spectral-temporal overlap among the musical sources, our method outperforms popular existing matrix factorization methods as measured by the recall and precision of learned dictionary atoms. We also propose co-occurrence constraints -- three simple and convenient multiplicative update rules for nonnegative matrix factorization (NMF) that enforce dependence among atoms. Using examples in music transcription, we demonstrate the ability of these updates to represent each musical note with multiple atoms and cluster the atoms for source separation purposes. Second, we study how spectral and temporal information extracted by nonnegative factorizations can improve upon musical instrument recognition. Musical instrument recognition in melodic signals is difficult, especially for classification systems that rely entirely upon spectral information instead of temporal information. Here, we propose a simple and effective method of combining spectral and temporal information for instrument recognition. While existing classification methods use traditional features such as statistical moments, we extract novel features from spectral and temporal atoms generated by NMF using a biologically motivated multiresolution gamma filterbank. Unlike other methods that require thresholds, safeguards, and hierarchies, the proposed spectral-temporal method requires only simple filtering and a flat classifier. Finally, we study how to perform sparse factorization when a large dictionary of musical atoms is already known. Sparse coding methods such as matching pursuit (MP) have been applied to problems in music information retrieval such as transcription and source separation with moderate success. However, when the set of dictionary atoms is large, identification of the best match in the dictionary with the residual is slow -- linear in the size of the dictionary. Here, we propose a variant called approximate matching pursuit (AMP) that is faster than MP while maintaining scalability and accuracy. Unlike MP, AMP uses an approximate nearest-neighbor (ANN) algorithm to find the closest match in a dictionary in sublinear time. One such ANN algorithm, locality-sensitive hashing (LSH), is a probabilistic hash algorithm that places similar, yet not identical, observations into the same bin. While the accuracy of AMP is comparable to similar MP methods, the computational complexity is reduced. Also, by using LSH, this method scales easily; the dictionary can be expanded without reorganizing any data structures

Matching pursuit and atomic signal models based on recursive filter banks

The matching pursuit algorithm can be used to derive signal decompositions in terms of the elements of a dictio- nary of time–frequency atoms. Using a structured overcomplete dictionary yields a signal model that is both parametric and signal adaptive. In this paper, we apply matching pursuit to the derivation of signal expansions based on damped sinusoids. It is shown that expansions in terms of complex damped sinusoids can be efficiently derived using simple recursive filter banks. We discuss a subspace extension of the pursuit algorithm that provides a framework for deriving real-valued expansions of real signals based on such complex atoms. Furthermore, we consider symmetric and asymmetric two-sided atoms constructed from underlying one-sided damped sinusoids. The primary concern is the application of this approach to the modeling of signals with transient behavior such as music; it is shown that time–frequency atoms based on damped sinusoids are more suitable for represent- ing transients than symmetric Gabor atoms. The resulting atomic models are useful for signal coding and analysis modification synthesis

Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

[EN] Induction machines drive many industrial processes and their unexpected failure can cause heavy producti on losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, and so forth. In these cases, an analysis in the time-frequency domain¿such as a spectrogram¿is required for detecting faults signatures. The spectrogram is built using the short time Fourier transform, but its resolution depends critically on the time window used to generate it¿short windows provide good time resolution but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms and combines them into as single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.This research was funded by the Spanish "Ministerio de Ciencia, Innovacion y Universidades (MCIU)", the "Agencia Estatal de Investigacion (AEI)" and the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i - Retos Investigacion 2018", project reference RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Riera-Guasp, M.; Sapena-Bano, A.; Pineda-Sanchez, M. (2019). Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines. Energies. 12(17):1-18. https://doi.org/10.3390/en12173361S118121

Image Restoration Methods for Retinal Images: Denoising and Interpolation

Retinal imaging provides an opportunity to detect pathological and natural age-related physiological changes in the interior of the eye. Diagnosis of retinal abnormality requires an image that is sharp, clear and free of noise and artifacts. However, to prevent tissue damage, retinal imaging instruments use low illumination radiation, hence, the signal-to-noise ratio (SNR) is reduced which means the total noise power is increased. Furthermore, noise is inherent in some imaging techniques. For example, in Optical Coherence Tomography (OCT) speckle noise is produced due to the coherence between the unwanted backscattered light. Improving OCT image quality by reducing speckle noise increases the accuracy of analyses and hence the diagnostic sensitivity. However, the challenge is to preserve image features while reducing speckle noise. There is a clear trade-off between image feature preservation and speckle noise reduction in OCT. Averaging multiple OCT images taken from a unique position provides a high SNR image, but it drastically increases the scanning time. In this thesis, we develop a multi-frame image denoising method for Spectral Domain OCT (SD-OCT) images extracted from a very close locations of a SD-OCT volume. The proposed denoising method was tested using two dictionaries: nonlinear (NL) and KSVD-based adaptive dictionary. The NL dictionary was constructed by adding phases, polynomial, exponential and boxcar functions to the conventional Discrete Cosine Transform (DCT) dictionary. The proposed denoising method denoises nearby frames of SD-OCT volume using a sparse representation method and combines them by selecting median intensity pixels from the denoised nearby frames. The result showed that both dictionaries reduced the speckle noise from the OCT images; however, the adaptive dictionary showed slightly better results at the cost of a higher computational complexity. The NL dictionary was also used for fundus and OCT image reconstruction. The performance of the NL dictionary was always better than that of other analytical-based dictionaries, such as DCT and Haar. The adaptive dictionary involves a lengthy dictionary learning process, and therefore cannot be used in real situations. We dealt this problem by utilizing a low-rank approximation. In this approach SD-OCT frames were divided into a group of noisy matrices that consist of non-local similar patches. A noise-free patch matrix was obtained from a noisy patch matrix utilizing a low-rank approximation. The noise-free patches from nearby frames were averaged to enhance the denoising. The denoised image obtained from the proposed approach was better than those obtained by several state-of-the-art methods. The proposed approach was extended to jointly denoise and interpolate SD-OCT image. The results show that joint denoising and interpolation method outperforms several existing state-of-the-art denoising methods plus bicubic interpolation.4 month

Prédiction de l'instabilité dynamique des réseaux électriques par apprentissage supervisé des signaux de réponses post-contingence sur des dictionnaires surcomplets

Ces dernières décennies, l'intégration aux réseaux électriques de capteurs intelligents incorporant la mesure synchronisée des phaseurs a contribué à enrichir considérablement les bases de données de surveillance en temps réel de la stabilité des réseaux électriques. En parallèle, la lutte aux changements climatiques s'est accompagnée d'un déploiement généralisé des sources d'énergies renouvelables dont l'intermittence de la production et le déficit d'inertie dû à l'interface de celle-ci par l'électronique de puissance, contribuent à augmenter les risques d'instabilité à la suite de contingences de réseau. Dans ce contexte, nous proposons d'appliquer aux données de synchrophaseurs de nouvelles approches d'intelligence de données inspirées par l'analyse massive des séries chronologiques et l'apprentissage sur des dictionnaires supervisés, permettant d'extraire des centaines d'attributs décrivant concisément les estimations d'état dynamique des générateurs de réseaux électriques. La mise en évidence d'une signification physique de ces attributs permet ensuite une classification de la stabilité dynamique qui s'éloigne de ce fait des boîtes noires produites par un apprentissage en profondeur « à l'aveugle » des séries chronologiques, pour évoluer vers une approche transparente plus adaptée à la salle de conduite des réseaux et acceptable pour les ingénieurs d'exploitation. Cette approche d'apprentissage machine « interprétable » par les humains, débouche de surcroît sur une détection fiable, utilisant de courtes fenêtres de données de vitesses d'alternateurs directement mesurées ou reconstituées par estimation d'état dynamique à partir de l'instant d'élimination du défaut, pour détecter toute instabilité subséquente, avec un temps de préemption suffisant pour activer des contremesures permettant de sauvegarder la stabilité du réseau et ainsi prévenir les pannes majeures. Notre travail aborde l'exploitation de cette nouvelle niche d'information par deux approches complémentaires d'intelligence des données : 1) une analyse non parcimonieuse d'une base d'attributs se chiffrant par centaines, calculés automatiquement par l'analyse numérique massive des séries chronologiques de signaux de réponses post-contingence des générateurs; et 2) une analyse parcimonieuse exploitant l'apprentissage supervisée de grands dictionnaires surcomplets pour habiliter une prédiction de l'instabilité sur de courtes fenêtres de données avec une représentation vectorielle creuse (contenant un grand nombre de zéros) et donc numériquement très efficiente en plus de l'interprétabilité inhérente des atomes constituant les dictionnaires. Au niveau méthodologique, l'approche non parcimonieuse vise à implémenter plusieurs méthodes analytiques combinées (notamment la transformée de Fourier, la transformée en ondelette, la méthode de Welch, la méthode de périodogramme et les exposants de Lyapunov) pour extraire du signal de réponse de chaque générateur des centaines d'attributs labellisés et servant à construire un espace physique d'indicateurs de stabilité à haute dimension (HDSI). Ceux-ci sont ensuite utilisés pour développer les prédicteurs de stabilité sur la base d'algorithmes standard de machine learning, par exemple le convolutional neural network (CNN), long short-term memory (LSTM), support vector machine (SVM), AdaBoost ou les forêts aléatoires. L'approche parcimonieuse implémentée consiste à développer deux techniques complémentaires : 1) un dictionnaire d'apprentissage supervisé joint (SLOD) au classificateur et 2) vingt dictionnaires d'apprentissage séparés des signaux associés aux cas stable/instable. Alors que le SLOD utilise des dictionnaires adaptatifs inspirés des données mesurées et apprises hors-ligne, la deuxième approche utilise des dictionnaires fixes pour reconstruire séparément les signaux des classes stables et instables. Dans les deux cas, l'étape finale consiste à identifier automatiquement en temps réel, la classe d'appartenance d'une réponse par reconstruction des signaux associés à partir des dictionnaires appris hors-ligne. L'analyse parcimonieuse des réponses des générateurs sur un dictionnaire d'apprentissage adaptatif joint au classificateur a été implémenté à partir de l'algorithme K-singular value de composition (KSVD) couplé à l'orthogonal matching pursuit (OMP), afin de reconstruire et prédire la stabilité dynamique des réseaux électriques. De plus, vingt décompositions parcimonieuses des signaux sur des dictionnaires fixes (simples et hybrides) ont permis de développer des classificateurs prédisant chaque classe séparément sur la base de la transformée en cosinus discrète (DCT), en sinus discrète (DST), en ondelette (DWT), de la transformée de Haar (DHT), et le dictionnaire de Dirac (DI) couplés à l'orthogonal matching pursuit (OMP). Cette étude démontre que la décomposition parcimonieuse sur un dictionnaire adaptatif joint au classificateur offre une performance proche de l'idéal (c'est-à-dire : 99,99 % précision, 99,99 % sécurité et 99,99 % fiabilité) de loin supérieure à celle d'un classificateur à reconstruction de signaux basée sur les vingt dictionnaires fixes ou adaptatifs séparés, et les classificateurs basés sur les moteurs de machine learning (SVM, ANN, DT, RF, AdaBoost, CNN et LSTM) implémentés à partir des indices HDSI extraits de la base de données des vitesses des rotors des réseaux IEEE 2 area 4 machines, IEEE 39 -bus et IEEE 68 -bus. Toutefois, le temps de resimulation (replay) en temps réel des dictionnaires fixes/adaptatifs séparés est nettement inférieur (de 30-40%) à celui observé pour le dictionnaire adaptatif à classificateur joint / SLOD, et les algorithmes modernes de machine learning utilisant les attributs de type HDSI comme intrants.In recent decades, the integration of smart sensors incorporating synchronized phasor measurements units (PMU) into power grids has contributed to a significant improvement of the databases for real-time monitoring of power grid stability. In parallel, the fight against climate change has been accompanied by a widespread deployment of renewable energy sources whose intermittency of production and the lack of inertia due to the interface of the latter by power electronics; contribute to increase the risks of instability following network contingencies. In this context, we propose to apply new data intelligence approaches inspired by massive time series analysis and supervised dictionary learning to synchrophasor data, allowing the extraction of hundreds of attributes concisely describing the dynamic state estimates of power system generators. The physical meaning identification of these attributes then allows for an online classification of dynamic stability, thus moving away from the black boxes produced by «blind» deep learning of time series to a transparent approach more suitable for the network control room and acceptable to operating engineers. This human-interpretable machine learning approach also leads to reliable detection, using short windows of generator speed data directly measured or reconstructed by dynamic state estimation from the instant of fault elimination, to detect any subsequent instability, with sufficient preemption time to activate false measures to safeguard the network stability and thus prevent major outages. Our work addresses the exploitation of this new information through two complementary data intelligence approaches : 1) a non-sparse analysis of an attribute base numbering in the hundreds, computed automatically by massive numerical analysis of post-contingency response signal time series from generators; and 2) a sparse analysis exploiting supervised learning of large overcomplete dictionaries to enable instability prediction over short windows of data with a hollow vector representation (containing a large number of zeros) and thus numerically very efficient in addition to the inherent interpretability of the atoms constituting the dictionaries. Methodologically, the non-sparse approach aims to implement several combined analytical methods (including Fourier transform, wavelet transform, Welch's method, periodogram method and Lyapunov exponents) to extract hundreds of labeled attributes from the response signal of each generator and used to construct a physical space of high-dimensional stability indicators (HDSI). These are used to develop stability predictors based on standard machine learning algorithms, e.g., CNN, LSTM, SVM, AdaBoost or random forests. The implemented sparse approach consists in developing two complementary techniques: 1) a supervised learning dictionary attached (SLOD) to the classifier and 2) twenty separate dictionaries learning of the signals associated with the stable/instable cases. While the SLOD uses adaptive dictionaries inspired by the measured and learned offline data, the second approach uses fixed dictionaries to reconstruct the stable and unstable signals classes separately. In both cases, the final step is automatically identified in real time the status to which a response belongs by reconstructing the associated signals from the off-line learned dictionaries. The sparse analysis of generator responses on an adaptive learning dictionary attached to the classifier was implemented using the K-singular value decomposition (KSVD) algorithm coupled with orthogonal matching pursuit (OMP), to reconstruct and predict online the dynamic stability of power systems. In addition, twenty sparse signal decompositions on fixed dictionaries (simple and hybrid) were used to develop classifiers predicting each class separately based on the discrete cosine transform (DCT), discrete sine transform (DST), wavelet transform (DWT), Haar transform (DHT), and Dirac dictionary (DI) coupled with the orthogonal matching pursuit (OMP). This study demonstrates that sparse decomposition on joined adaptive dictionary to the classifier provides near ideal performance (i.e.: 99.99% accuracy, 99.99% security, and 99.99% reliability) far superior to that of a classifier has signal reconstruction based on the twenty separate fixed or adaptive dictionaries and classifiers based on machine learning engines (SVM, ANN, DT, RF, AdaBoost, CNN, and LSTM) implemented from HDSI indices extracted from the rotor speed database of the IEEE 2 area 4 machines, IEEE 39 -bus, and IEEE 68 -bus test systems. However, the real-time replay time of the separate fixed/adaptive dictionaries is significantly lower (by 30-40%) than that observed for the adaptive dictionary with joint classifier/SLOD, and modern machine learning algorithms using HDSI-like attributes as inputs

Unattended acoustic sensor systems for noise monitoring in national parks

2017 Spring.Includes bibliographical references.Detection and classification of transient acoustic signals is a difficult problem. The problem is often complicated by factors such as the variety of sources that may be encountered, the presence of strong interference and substantial variations in the acoustic environment. Furthermore, for most applications of transient detection and classification, such as speech recognition and environmental monitoring, online detection and classification of these transient events is required. This is even more crucial for applications such as environmental monitoring as it is often done at remote locations where it is unfeasible to set up a large, general-purpose processing system. Instead, some type of custom-designed system is needed which is power efficient yet able to run the necessary signal processing algorithms in near real-time. In this thesis, we describe a custom-designed environmental monitoring system (EMS) which was specifically designed for monitoring air traffic and other sources of interest in national parks. More specifically, this thesis focuses on the capabilities of the EMS and how transient detection, classification and tracking are implemented on it. The Sparse Coefficient State Tracking (SCST) transient detection and classification algorithm was implemented on the EMS board in order to detect and classify transient events. This algorithm was chosen because it was designed for this particular application and was shown to have superior performance compared to other algorithms commonly used for transient detection and classification. The SCST algorithm was implemented on an Artix 7 FPGA with parts of the algorithm running as dedicated custom logic and other parts running sequentially on a soft-core processor. In this thesis, the partitioning and pipelining of this algorithm is explained. Each of the partitions was tested independently to very their functionality with respect to the overall system. Furthermore, the entire SCST algorithm was tested in the field on actual acoustic data and the performance of this implementation was evaluated using receiver operator characteristic (ROC) curves and confusion matrices. In this test the FPGA implementation of SCST was able to achieve acceptable source detection and classification results despite a difficult data set and limited training data. The tracking of acoustic sources is done through successive direction of arrival (DOA) angle estimation using a wideband extension of the Capon beamforming algorithm. This algorithm was also implemented on the EMS in order to provide real-time DOA estimates for the detected sources. This algorithm was partitioned into several stages with some stages implemented in custom logic while others were implemented as software running on the soft-core processor. Just as with SCST, each partition of this beamforming algorithm was verified independently and then a full system test was conducted to evaluate whether it would be able to track an airborne source. For the full system test, a model airplane was flown at various trajectories relative to the EMS and the trajectories estimated by the system were compared to the ground truth. Although in this test the accuracy of the DOA estimates could not be evaluated, it was show that the algorithm was able to approximately form the general trajectory of a moving source which is sufficient for our application as only a general heading of the acoustic sources is desired