1,380 research outputs found

    Recognition of cavitation characteristics in non-clogging pumps based on the improved Lévy flight bat algorithm

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    The performance and operational stability of non-clogging pumps can be affected by cavitation. To accurately identify the cavitation state of the non-clogging pump and provide technical references for monitoring its operation, a study was conducted on the optimization of Elman neural networks for cavitation monitoring and identification using the Improved Lévy Flight Bat Algorithm (ILBA) on the basis of the traditional Bat Algorithm (BA). The ILBA employs multiple bats to interact and search for targets and utilizes the local search strategy of Lévy flight, effectively avoiding local minima by taking advantage of the non-uniform random walk characteristics of large jumps. The ILBA algorithm demonstrates superior performance compared to other traditional algorithms through simulation testing and comparative calculations with eight benchmark test functions. On this basis, the optimization of the weights and thresholds of the Elman neural network was carried out by the improved bat algorithm. This leads to an enhancement in the accuracy of the neural network for identifying and classifying cavitation data, and the establishment of the ILBA-Elman cavitation diagnosis model was achieved. Collect pressure pulsation signals at the tongue of the non-clogging pump volute through cavitation tests. Through the cavitation feature extraction method based on Variational Mode Decomposition (VMD) and Multi-scale Dispersion Entropy (MDE), the interference signal can be effectively suppressed and the complexity of the time series can be measured from multiple angles, thereby creating a cavitation feature data set. The improved cavitation diagnosis model (ILBA-Elman) can realize the effective identification of the cavitation characteristics of non-clogging pumps through a variety of algorithm comparison experiments

    Development of a machine learning based methodology for bridge health monitoring

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    Tesi en modalitat de compendi de publicacionsIn recent years the scientific community has been developing new techniques in structural health monitoring (SHM) to identify the damages in civil structures specially in bridges. The bridge health monitoring (BHM) systems serve to reduce overall life-cycle maintenance costs for bridges, as their main objective is to prevent catastrophic failures and damages. In the BHM using dynamic data, there are several problems related to the post-processing of the vibration signals such as: (i) when the modal-based dynamic features like natural frequencies, modes shape and damping are used, they present a limitation in relation to damage location, since they are based on a global response of the structure; (ii) presence of noise in the measurement of vibration responses; (iii) inadequate use of existing algorithms for damage feature extraction because of neglecting the non-linearity and non-stationarity of the recorded signals; (iv) environmental and operational conditions can also generate false damage detections in bridges; (v) the drawbacks of traditional algorithms for processing large amounts of data obtained from the BHM. This thesis proposes new vibration-based parameters and methods with focus on damage detection, localization and quantification, considering a mixed robust methodology that includes signal processing and machine learning methods to solve the identified problems. The increasing volume of bridge monitoring data makes it interesting to study the ability of advanced tools and systems to extract useful information from dynamic and static variables. In the field of Machine Learning (ML) and Artificial Intelligence (AI), powerful algorithms have been developed to face problems where the amount of data is much larger (big data). The possibilities of ML techniques (unsupervised algorithms) were analyzed here in bridges taking into account both operational and environmental conditions. A critical literature review was performed and a deep study of the accuracy and performance of a set of algorithms for detecting damage in three real bridges and one numerical model. In the literature review inherent to the vibration-based damage detection, several state-of-the-art methods have been studied that do not consider the nature of the data and the characteristics of the applied excitation (possible non-linearity, non-stationarity, presence or absence of environmental and/or operational effects) and the noise level of the sensors. Besides, most research uses modal-based damage characteristics that have some limitations. A poor data normalization is performed by the majority of methods and both operational and environmental variability is not properly accounted for. Likewise, the huge amount of data recorded requires automatic procedures with proven capacity to reduce the possibility of false alarms. On the other hand, many investigations have limitations since only numerical or laboratory cases are studied. Therefore, a methodology is proposed by the combination of several algorithms to avoid them. The conclusions show a robust methodology based on ML algorithms capable to detect, localize and quantify damage. It allows the engineers to verify bridges and anticipate significant structural damage when occurs. Moreover, the proposed non-modal parameters show their feasibility as damage features using ambient and forced vibrations. Hilbert-Huang Transform (HHT) in conjunction with Marginal Hilbert Spectrum and Instantaneous Phase Difference shows a great capability to analyze the nonlinear and nonstationary response signals for damage identification under operational conditions. The proposed strategy combines algorithms for signal processing (ICEEMDAN and HHT) and ML (k-means) to conduct damage detection and localization in bridges by using the traffic-induced vibration data in real-time operation.En los últimos años la comunidad científica ha desarrollado nuevas técnicas en monitoreo de salud estructural (SHM) para identificar los daños en estructuras civiles especialmente en puentes. Los sistemas de monitoreo de puentes (BHM) sirven para reducir los costos generales de mantenimiento del ciclo de vida, ya que su principal objetivo es prevenir daños y fallas catastróficas. En el BHM que utiliza datos dinámicos, existen varios problemas relacionados con el procesamiento posterior de las señales de vibración, tales como: (i) cuando se utilizan características dinámicas modales como frecuencias naturales, formas de modos y amortiguamiento, presentan una limitación en relación con la localización del daño, ya que se basan en una respuesta global de la estructura; (ii) presencia de ruido en la medición de las respuestas de vibración; (iii) uso inadecuado de los algoritmos existentes para la extracción de características de daño debido a la no linealidad y la no estacionariedad de las señales registradas; (iv) las condiciones ambientales y operativas también pueden generar falsas detecciones de daños en los puentes; (v) los inconvenientes de los algoritmos tradicionales para procesar grandes cantidades de datos obtenidos del BHM. Esta tesis propone nuevos parámetros y métodos basados en vibraciones con enfoque en la detección, localización y cuantificación de daños, considerando una metodología robusta que incluye métodos de procesamiento de señales y aprendizaje automático. El creciente volumen de datos de monitoreo de puentes hace que sea interesante estudiar la capacidad de herramientas y sistemas avanzados para extraer información útil de variables dinámicas y estáticas. En el campo del Machine Learning (ML) y la Inteligencia Artificial (IA) se han desarrollado potentes algoritmos para afrontar problemas donde la cantidad de datos es mucho mayor (big data). Aquí se analizaron las posibilidades de las técnicas ML (algoritmos no supervisados) teniendo en cuenta tanto las condiciones operativas como ambientales. Se realizó una revisión crítica de la literatura y se llevó a cabo un estudio profundo de la precisión y el rendimiento de un conjunto de algoritmos para la detección de daños en tres puentes reales y un modelo numérico. En la revisión de literatura se han estudiado varios métodos que no consideran la naturaleza de los datos y las características de la excitación aplicada (posible no linealidad, no estacionariedad, presencia o ausencia de efectos ambientales y/u operativos) y el nivel de ruido de los sensores. Además, la mayoría de las investigaciones utilizan características de daño modales que tienen algunas limitaciones. Estos métodos realizan una normalización deficiente de los datos y no se tiene en cuenta la variabilidad operativa y ambiental. Asimismo, la gran cantidad de datos registrados requiere de procedimientos automáticos para reducir la posibilidad de falsas alarmas. Por otro lado, muchas investigaciones tienen limitaciones ya que solo se estudian casos numéricos o de laboratorio. Por ello, se propone una metodología mediante la combinación de varios algoritmos. Las conclusiones muestran una metodología robusta basada en algoritmos de ML capaces de detectar, localizar y cuantificar daños. Permite a los ingenieros verificar puentes y anticipar daños estructurales. Además, los parámetros no modales propuestos muestran su viabilidad como características de daño utilizando vibraciones ambientales y forzadas. La Transformada de Hilbert-Huang (HHT) junto con el Espectro Marginal de Hilbert y la Diferencia de Fase Instantánea muestran una gran capacidad para analizar las señales de respuesta no lineales y no estacionarias para la identificación de daños en condiciones operativas. La estrategia propuesta combina algoritmos para el procesamiento de señales (ICEEMDAN y HHT) y ML (k-means) para detectar y localizar daños en puentes mediante el uso de datos de vibraciones inducidas por el tráfico en tiempo real.Postprint (published version

    Partial discharge feature extraction based on ensemble empirical mode decomposition and sample entropy

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    Partial Discharge (PD) pattern recognition plays an important part in electrical equipment fault diagnosis and maintenance. Feature extraction could greatly affect recognition results. Traditional PD feature extraction methods suffer from high-dimension calculation and signal attenuation. In this study, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Sample Entropy (SamEn) is proposed. In order to reduce the influence of noise, a wavelet method is applied to PD de-noising. Noise Rejection Ratio (NRR) and Mean Square Error (MSE) are adopted as the de-noising indexes. With EEMD, the de-noised signal is decomposed into a finite number of Intrinsic Mode Functions (IMFs). The IMFs, which contain the dominant information of PD, are selected using a correlation coefficient method. From that, the SamEn of selected IMFs are extracted as PD features. Finally, a Relevance Vector Machine (RVM) is utilized for pattern recognition using the features extracted. Experimental results demonstrate that the proposed method combines excellent properties of both EEMD and SamEn. The recognition results are encouraging with satisfactory accuracy

    Short-Term Wind Power Prediction Based on Data Decomposition and Combined Deep Neural Network

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    A hybrid short-term wind power prediction model based on data decomposition and combined deep neural network is proposed with the inclusion of the characteristics of fluctuation and randomness of nonlinear signals, such as wind speed and wind power. Firstly, the variational mode decomposition (VMD) is used to decompose the wind speed and wind power sequences in the input data to reduce the noise in the original signal. Secondly, the decomposed wind speed and wind power sub-sequences are reconstructed into new data sets with other related features as the input of the combined deep neural network, and the input data are further studied for the implied features by convolutional neural network (CNN), which should be passed into the long and short-term memory neural network (LSTM) as input for prediction. At the same time, the improved particle swarm optimization algorithm (IPSO) is adopted to optimize the parameters of each prediction model. By superimposing each predicted sub-sequence, the predicting wind power could be obtained. Simulations based on a short-term power prediction in different months with huge weather differences is carried out for a wind farm in Guangdong, China. The simulated results validate that the proposed model has a high prediction accuracy and generalization ability.Guangdong-Guangxi Joint Foundation of China: Research on Distributed Optimal Control of Renewable Multi-microgrids based on the Integration of Multi-Agent Dynamic Game and Prediction Mechanism [Project Number 2021A1515410009]

    A critical review of online battery remaining useful lifetime prediction methods.

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    Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods

    Integrated processing method for microseismic signal based on deep neural network

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    Denoising and onset time picking of signals are essential before extracting source information from collected seismic/microseismic data. We proposed an advanced deep dual-tasking network (DDTN) that integrates these two procedures sequentially to achieve the optimal performance. Two homo-structured encoder–decoder networks with specially designed structures and parameters are connected in series for handling the denoising and detection of microseismic signals. Based on the similarity of data types, the output of the denoising network will be imported into the detection network to obtain labels for the signal duration. The procedures of denoising and duration detection can be completed in an integrated way, where the denoised signals can improve the accuracy of onset time picking. Results show that the method has a good performance for the denoising of microseismic signals that contain various types and intensities of noise. Compared with existing methods, DDTN removes the noise with a minor waveform distortion. It is ideal for recovering the microseismic signal while maintaining a good capacity for onset time picking when the signal-to-noise ratio is low. Based on that, the second network can detect a more accurate duration of microseismic signals and thus obtain more accurate onset time. The method has great potential to be extended to the study of exploration seismology and earthquakes

    A Decentralized Fault Section Location Method Using Autoencoder and Feature Fusion in Resonant Grounding Distribution Systems

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    In industrial applications, the existing fault location methods of resonant grounding distribution systems suffer from low accuracy due to excessive dependence on communication, lack of field data, difficulty in artificial feature extraction and threshold setting, etc. To address these problems, this study proposes a decentralized fault section location method, which is implemented by the primary and secondary fusion intelligent switch (PSFIS) with two preloaded algorithms: autoencoder (AE) and backpropagation neural network. The relation between the transient zero-sequence current and the derivative of the transient zero-sequence voltage in each section is analyzed, and its features are extracted adaptively by using AE, without acquiring network parameters or setting thresholds. The current and voltage data are processed locally at PSFISs throughout the whole procedure, making it is insusceptible to communication failure or delay. The feasibility and effectiveness of the approach are investigated in PSCAD/EMTDC and real-time digital simulation system, which is then validated by field data. Compared with other methods, the experiment results indicate that the proposed method performs well in various scenarios with strong robustness to harsh on-site environment and roughness of data

    Volitional Control of Lower-limb Prosthesis with Vision-assisted Environmental Awareness

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    Early and reliable prediction of user’s intention to change locomotion mode or speed is critical for a smooth and natural lower limb prosthesis. Meanwhile, incorporation of explicit environmental feedback can facilitate context aware intelligent prosthesis which allows seamless operation in a variety of gait demands. This dissertation introduces environmental awareness through computer vision and enables early and accurate prediction of intention to start, stop or change speeds while walking. Electromyography (EMG), Electroencephalography (EEG), Inertial Measurement Unit (IMU), and Ground Reaction Force (GRF) sensors were used to predict intention to start, stop or increase walking speed. Furthermore, it was investigated whether external emotional music stimuli could enhance the predictive capability of intention prediction methodologies. Application of advanced machine learning and signal processing techniques on pre-movement EEG resulted in an intention prediction system with low latency, high sensitivity and low false positive detection. Affective analysis of EEG suggested that happy music stimuli significantly (

    BEMDEC: An Adaptive and Robust Methodology for Digital Image Feature Extraction

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    The intriguing study of feature extraction, and edge detection in particular, has, as a result of the increased use of imagery, drawn even more attention not just from the field of computer science but also from a variety of scientific fields. However, various challenges surrounding the formulation of feature extraction operator, particularly of edges, which is capable of satisfying the necessary properties of low probability of error (i.e., failure of marking true edges), accuracy, and consistent response to a single edge, continue to persist. Moreover, it should be pointed out that most of the work in the area of feature extraction has been focused on improving many of the existing approaches rather than devising or adopting new ones. In the image processing subfield, where the needs constantly change, we must equally change the way we think. In this digital world where the use of images, for variety of purposes, continues to increase, researchers, if they are serious about addressing the aforementioned limitations, must be able to think outside the box and step away from the usual in order to overcome these challenges. In this dissertation, we propose an adaptive and robust, yet simple, digital image features detection methodology using bidimensional empirical mode decomposition (BEMD), a sifting process that decomposes a signal into its two-dimensional (2D) bidimensional intrinsic mode functions (BIMFs). The method is further extended to detect corners and curves, and as such, dubbed as BEMDEC, indicating its ability to detect edges, corners and curves. In addition to the application of BEMD, a unique combination of a flexible envelope estimation algorithm, stopping criteria and boundary adjustment made the realization of this multi-feature detector possible. Further application of two morphological operators of binarization and thinning adds to the quality of the operator
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