Multiresolution Signal Decomposition Research Articles

Photovoltaic system faults detection using fractional multiresolution signal decomposition

Photovoltaic system faults detection using fractional multiresolution signal decomposition

An aerodynamic digital twin of real-world leading edge erosion: Acquisition, Generation and 3D CFD

Wind turbine blades face extremely challenging environmental conditions over their operating life-time, that can easily exceed 2 decades. Airborne particles (insects, sand, rain, hail, ice, sea spray etc.) strike the blade leading edge (LE) first and erode or attach to it, roughening its surface. Leading edge roughness (LER) can critically alter the aerodynamic performance of blades, as its aerodynamic impact is strongly coupled to its height with respect to the local boundary layer thickness, which is thinnest around the LE. The actual, detailed topographic manifestation of LER on in-service blades—needed to accurately assess its aerodynamic impact—is highly probabilistic, as it depends on the interaction of multiple stochastic parameters, like the environmental conditions, material composition and production process. Yet little high-resolution topographic LER data of this kind is currently available. This paper details how such data is collected from blades of different turbine manufacturers and processed consistently to build digital twins of the measured LER, that can be analysed aerodynamically using 3D CFD or wind tunnels. Here a special focus lies on how to reconstruct the LER topography and build the computational mesh such that the correct aerodynamic response is observed. For this purpose multiresolution signal decomposition is used to process the topographies and a special meshing procedure established.

New formulation for predicting total dissolved gas supersaturation in dam reservoir: application of hybrid artificial intelligence models based on multiple signal decomposition

Total dissolved gas (TDG) concentration plays an important role in the control of the aquatic life. Elevated TDG can cause gas-bubble trauma in fish (GBT). Therefore, controlling TDG fluctuation has become of great importance for different disciplines of surface water environmental engineering.. Nowadays, direct estimation of TDG is expensive and time-consuming. Hence, this work proposes a new modelling framework for predicting TDG based on the integration of machine learning (ML) models and multiresolution signal decomposition. The proposed ML models were trained and validated using hourly data obtained from four stations at the United States Geological Survey. The dataset are composed from: (i) water temperature (Tw), (ii) barometric pressure (BP), and (iii) discharge (Q), which were used as the input variables for TDG prediction. The modelling strategy is conducted based on two different steps. First, six singles ML model namely: (i) multilayer perceptron neural network, (ii) Gaussian process regression, (iii) random forest regression, (iv) random vector functional link, (v) adaptive boosting, and (vi) Bootstrap aggregating (Bagging), were developed for predicting TDG using Tw, BP, and Q, and their performances were compared. Second, a new framework was introduced based on the combination of empirical mode decomposition (EMD), the variational mode decomposition (VMD), and the empirical wavelet transform (EWT) preprocessing signal decomposition algorithms with ML models for building new hybrid ML models. Hence, the Tw, BP, and Q signals were decomposed to extract the intrinsic mode functions (IMFs) by using the EMD and VMD methods and the multiresolution analysis (MRA) components by using the EWT method. Then after, the IMFs and MRA components were selected and regraded as new input variables for the ML models and used as an integral part thereof. The single and hybrid prediction models were compared using several statistical metrics namely, root mean square error, mean absolute error, coefficient of determination (R2), and Nash–Sutcliffe efficiency (NSE). The single and hybrid models were trained several times with high number of repetitions, depending on the kind of modeling process. The obtained results using single models gave good agreement between the predicted TDG and the situ measured dataset. Overall, the Bagging model performed better than the other five models with R2 and NSE values of 0.906 and 0.902, respectively. However, the extracted IMFs and MRA components using the EMD, VMD and the EWT have contributed to an improvement of the hybrid models’ performances, for which the R2 and NSE were significantly increased reaching the values of 0.996 and 0.995. Experimental results showed the superiority of hybrid models and more importantly the importance of signal decomposition in improving the predictive accuracy of TDG.Graphical abstract

Exploiting Wavelet Recurrent Neural Networks for satellite telemetry data modeling, prediction and control

Multidimensional times series prediction is a challenging task. Only recently the increased data availability has made it possible to tackle with such problems. In this work we devised a novel method to exploit the multiple correlated features in the time series. The recurrent neural networks and the wavelet transform have been important innovations in the fields of signal processing and time series prediction. This paper proposes a Wavelet Recurrent Network for multi-steps ahead prediction of multidimensional time series. The proposed model combines these two elements into a neural network that predicts multiple samples in the future that are multiple time steps ahead with respect to the input samples. This Wavelet Recurrent Network carries out a multiresolution decomposition of the input signal through the wavelet transform, predicts the future wavelet coefficients with the recurrent neural network and transforms the output back in the time domain. The proposed model is applied to the prediction of satellite telemetry data, that is composed of readings from multiple sensors which are highly correlated. The prediction of such telemetries can help the engineers to detect anomalies in the system, that, in the context of space missions, are particularly dangerous since they can compromise the entire mission if not handled properly. The results show that the proposed model outperforms the recurrent network without wavelet transform both in terms of accuracy and in the width of the forecast horizon.

Investigation on Surface Condition of the Corona-Aged Silicone Rubber Nanocomposite Adopting Wavelet and LIBS Technique

Silicone rubber nanocomposites were prepared using 1, 3, 5, and 10 wt% of alumina nanofiller. Corona inception voltage (CIV) due to water droplets was measured using fluorescence technique, and it has decreased for alumina-added silicone rubber nanocomposites and with corona-aged samples. The effect of the corona discharges on the surface morphology characteristics of the nanocomposites was analyzed by measuring contact angle, surface profile measurement using atomic force microscopy (AFM), and characterized by adopting multiresolution signal decomposition (MRSD) technique. The addition of the alumina nanofiller suppressed the surface roughness for the corona-aged nanocomposites and 5-wt% sample observed to have the lowest surface roughness values. Recovery of the surface roughness caused by corona discharges was analyzed at different time intervals, and it was observed that alumina nanoparticle-added silicone rubber showed an incremental reduction in the roughness recovery rate. The laser-induced breakdown spectroscopy (LIBS) technique was adopted to analyze the nanocomposites after the corona aging, and further plasma temperature was evaluated at different recovery times. The addition of an alumina nanofiller increased the plasma temperature, and 5-wt% samples have the lowest plasma temperature recovery rate after corona aging. The analysis of the study indicates that the corona-aged silicone rubber specimen surface properties recover in 8 h of resting time period.

Prediction of Squeal Noise Based on Multiresolution Signal Decomposition and Wavelet Representation—Application to FEM Brake Systems Subjected to Friction-Induced Vibration

This paper is devoted to discussion of the efficiency of reduced models based on a Double Modal Synthesis method that combines a classical modal reduction and a condensation at the frictional interfaces by computing a reduced complex mode basis, for the prediction of squeal noise of mechanical systems subjected to friction-induced vibration. More specifically, the use of the multiresolution signal decomposition of acoustic radiation and wavelet representation will be proposed to analyze details of a pattern on different observation scales ranging from the pixel to the size of the complete acoustic pattern. Based on this approach and the definition of specific resulting criteria, it is possible to quantify the differences in the representation of the acoustic fields for different reduced models and thus to perform convergence studies for different scales of representation in order to evaluate the potential of reduced models. The effectiveness of the proposed approach is tested on the finite element model of a simplified brake system that is composed of a disc and two pads. The contact is modeled by introducing contact elements at the two friction interfaces with the classical Coulomb law and a constant friction coefficient. It is demonstrated that the new proposed criteria, based on multiresolution signal decomposition, allow us to provide satisfactory results for the choice of an efficient reduced model for predicting acoustic radiation due to squeal noise.

Understanding the surface condition of gamma irradiated epoxy alumina nanocomposites adopting wavelets and fractal technique

The influence of alumina nanofiller and gamma irradiation on the surface potential variation of epoxy-alumina nanocomposites was investigated. The surface potential decay rate of nanocomposites has increased and the trap depth decreased with alumina nanoparticles addition to the matrix as well as upon exposure to gamma irradiation, Surface roughness was estimated using the wavelets and fractal technique. Daubechies wavelet of order 4 (db4) wavelet was chosen as the most suitable mother wavelet for surface roughness measurement. Multi resolution signal decomposition (MRSD) analysis of surface profile has revealed that with increasing wt% of alumina nanofiller in the nanocomposites, reduction in surface roughness of nanocomposites was observed. Upon gamma irradiation, the surface roughness factor at each level of MRSD has increased marginally. Fractal dimension and lacunarity were calculated for unaged and gamma ray irradiated samples and it exhibits inverse correlation.

Denoising 1D signal using wavelets

Signal denoising is one of the most important areas in signal processing. In the present paper, we have denoised a 1D piecewise constant (PWC) signal corrupted by additive white Gaussian noise (AWGN) using the thresholded Haar wavelet denoising method. The central idea of the wavelet transform is the multiresolution decomposition of signals. This becomes advantageous because small objects require high resolution and low resolution is suitable for large objects. In multiresolution decomposition, an approximation component is created using a scaling function, which is often termed as a lowpass filter. Detail components are obtained using wavelet functions, which are often known as highpass filters. A series of approximations of a signal is thus obtained, which has difference in resolution among them by a factor 2. Detail components contain the difference between adjacent approximations. Proper thresholding of the transformed signal results in reduced noise because, in general, the noise component of a signal has a relatively smaller magnitude and wider bandwidth. We show that our method outperforms recently reported non-convex regularisation-based convex 1D total variation denoising method on PWC signals.

Denoising 1D signal using wavelets

Signal denoising is one of the most important areas in signal processing. In the present paper, we have denoised a 1D piecewise constant (PWC) signal corrupted by additive white Gaussian noise (AWGN) using the thresholded Haar wavelet denoising method. The central idea of the wavelet transform is the multiresolution decomposition of signals. This becomes advantageous because small objects require high resolution and low resolution is suitable for large objects. In multiresolution decomposition, an approximation component is created using a scaling function, which is often termed as a lowpass filter. Detail components are obtained using wavelet functions, which are often known as highpass filters. A series of approximations of a signal is thus obtained, which has difference in resolution among them by a factor 2. Detail components contain the difference between adjacent approximations. Proper thresholding of the transformed signal results in reduced noise because, in general, the noise component of a signal has a relatively smaller magnitude and wider bandwidth. We show that our method outperforms recently reported non-convex regularisation-based convex 1D total variation denoising method on PWC signals.

Effects of liquids immersion and drying on the surface properties of HTV silicone rubber: characterisation by contact angle and surface physical morphology

High-temperature vulcanised (HTV) silicone rubber composite insulator has been widely used in extra-high and ultra-high transmission lines due to its excellent electrical and mechanical performance. In practice use, liquids may encounter the composite insulator intermittently and can lead to the degradation and deterioration of silicone rubber. In the present research, the effects of liquids immersion and subsequent drying on the surface properties of HTV silicone rubber were investigated by means of the contact angle measurement and surface physical morphology measurement which was further analysed in terms of the discrete wavelet transform method, multiresolution signal decomposition algorithm, and three-dimensional fractal dimension calculation. Based on the experimental results, the mechanism of the loss and recovery of the hydrophobicity of HTV silicone rubber surface during the liquids immersion and subsequent drying processes were further explained by taking into consideration the embedded water molecules (and/or hydrated ions) on the HTV silicone rubber surface and the synergy of the increase in roughness/fractal dimension and low molecular weight siloxanes diffusion. This study is helpful for better understanding of the change of surface properties of HTV silicone rubber caused by environmental and electrical stresses.

Stenosis Characterization and Identification for Dialysis Vascular Access.

Vascular access dysfunction is the leading cause of hospitalization for hemodialysis patients and accounts for the most medical costs in this patient population. Vascular access flow is commonly hindered by blood vessel narrowing (stenosis). Current screening methods involving imaging to detect stenosis are too costly for routine use at the point of care. Noninvasive, real-time screening of patients at risk of vascular access dysfunction could potentially identify high-risk patients and reduce the likelihood of emergency surgical interventions. Bruits (sounds produced by turbulent blood flow near stenoses) can be interpreted by skilled clinical staff using conventional stethoscopes. To improve the sensitivity of detection, digital analysis of blood flow sounds (phonoangiograms or PAGs) is a promising approach for classifying vascular access stenosis using non-invasive auditory recordings. Here, we demonstrate auditory and spectral features of PAGs which estimate both the location and degree of stenosis (DOS). Auditory recordings from nine stenosis phantoms with variable DOS and hemodynamic flow rate were obtained using a digital recording stethoscope and analyzed to extract classification features. Autoregressive modeling and discrete wavelet transforms were used for multiresolution signal decomposition to produce 14 distinct features, most of which were linearly correlated with DOS. Our initial results suggest that the widely-used auditory spectral centroid is a simple way to calculate features which can estimate both the location and severity of vascular access stenosis.

Exploring the resilience of crude oil market via nonlinear dynamics and wavelet-based analysis: an international experience

This paper investigates a signal modality analysis for the characterisation and detection of nonlinearity in crude oil markets. Given the nonlinear and time-varying characteristics of international crude oil prices, this study employs the recently proposed delay vector variance (DVV) method that examines local predictability of a signal in the phase space to detect the determinism and nonlinearity in a time series. In addition, wavelet transforms, which have recently emerged as a mathematical tool for multi-resolution decomposition of signals, is utilised. In particular, among the wavelet methodologies considered, the complex Morlet wavelet is useful and best at detecting the various phases of oil prices through the trajectory of market developments. The findings of this paper highlight the significant phases of the series and its relation to real-world phenomena with an indication of early warning signals of future significant events, thereby providing a guide for proper decision making and risk management practices of market participants.

Line-to-Line Fault Detection for Photovoltaic Arrays Based on Multiresolution Signal Decomposition and Two-Stage Support Vector Machine

Fault detection in photovoltaic (PV) arrays becomes difficult as the number of PV panels increases. Particularly, under low irradiance conditions with an active maximum power point tracking algorithm, line-to-line (L-L) faults may remain undetected because of low fault currents, resulting in loss of energy and potential fire hazards. This paper proposes a fault detection algorithm based on multiresolution signal decomposition for feature extraction, and two-stage support vector machine (SVM) classifiers for decision making. This detection method only requires data of the total voltage and current from a PV array and a limited amount of labeled data for training the SVM. Both simulation and experimental case studies verify the accuracy of the proposed method.

Stress-induced surface characterization by wavelet and fractal analysis in Ga-doped ZnO thin films

ABSTRACTThe Ga-doped ZnO (GZO) were deposited by magnetron reactive sputtering on glass substrates at room temperature with different deposited times to obtain various thickness. The root-mean-square (RMS) roughness obtained from the atomic force microscopy (AFM) images is observed to shift linearly with the deposited time, the fractal geometry and multi-resolution signal decomposition (MRSD) based on wavelet transform were applied on the surface profiles and the results does not synchronously changes as the thickness, which is related to the profile’s frequency. The calculated compressive in-plane stress of highly c-axis oriented GZO films also shows an irregular variation as the increase of film thickness, what’s more, the in-plane stress and fractal dimension exhibit a polynomial relationship and the two parameters can be used for describing the surface morphology.

Fault Detection for Photovoltaic Systems Based on Multi-Resolution Signal Decomposition and Fuzzy Inference Systems

This paper presents a detection scheme for DC side short-circuit faults of photovoltaic (PV) arrays that consist of multiple PV panels connected in a series/parallel configuration. Such faults are nearly undetectable under low irradiance conditions, particularly, when a maximum power point tracking algorithm is in-service. If remain undetected, these faults can considerably lower the output energy of solar systems, damage the panels, and potentially cause fire hazards. The proposed fault detection scheme is based on a pattern recognition approach that employs a multiresolution signal decomposition technique to extract the necessary features, based on which a fuzzy inference system determines if a fault has occurred. The presented case studies (both simulation and experimental) demonstrate the effective and reliable performance of the proposed method in detecting PV array faults.

Ga-doped ZnO thin film surface characterization by wavelet and fractal analysis

The change in roughness of various thicknesses Ga-doped ZnO (GZO) thin films deposited by magnetron reactive sputtering on glass substrates at room temperature was measured by atomic force microscopy (AFM). Multi-resolution signal decomposition based on wavelet transform and fractal geometry was applied to process surface profiles, to evaluate the roughness trend of relevant frequency resolution. The results give a six-level decomposition and the results change with deposited time and surface morphology. Also, it is found that fractal dimension is closely connected to the underside diameter (grain size) and the distance between adjacent grains that affect the change rate of surface and the increase of the defects such as abrupt changes lead to a larger value of fractal dimension.

Pollution Severity Analysis of Electric Power Transmission Tower Insulators using K-Means Clustering and Multi Resolution Signal Decomposition Technique

Pollution due to various sources deposits on the surface of insulators used in the electric power transmission towers. Under wet and highly polluted conditions, this pollution layer causes the formation of severe arcing on the surface of insulator which in turn leads to flashover of insulator. Failure of insulators due to pollution based flashover is a major issue faced by electrical utilities and hence accurate prediction of pollution on the surface of insulator using advanced techniques is always a hot research area. This paper deals with the development of a high performance diagnostic system to predict the pollution severity of tower insulators based on the multi resolution signal decomposition technique and k-means clustering approach. Laboratory experiments were carried out on porcelain insulators, which are conventionally used in power transmission system, under AC voltages at different pollution conditions and corresponding leakage current signals were measured. Multi resolution signal decomposition technique is employed to extract important features of leakage current signals. Variation of leakage current magnitude and distortion ratio at different pollution levels were analyzed. K-means algorithm is used to cluster the extracted features of the leakage current data. Test results clearly show that the pollution severity of power transmission tower insulators can be effectively realized through k-means clustering technique and it will be useful to carry out preventive maintenance work.

Identification of Open-Switch and Short-Switch Failure of Multilevel Inverters through DWT and ANN Approach using LabVIEW

In recent times, multilevel inverters are given high priority in many large industrial drive applications. However, the reliability of multilevel inverters are mainly affected by the failure of power electronic switches. In this paper, open-switch and short-switch failure of multilevel inverters and its identification using a high performance diagnostic system is discussed. Experimental and simulation studies were carried out on five level cascaded H-Bridge multilevel inverter and its output voltage waveforms were analyzed at different switch fault cases and at different modulation index values. Salient frequency domain features of the output voltage signal were extracted using the discrete wavelet transform multi resolution signal decomposition technique. Real time application of the proposed fault diagnostic system was implemented through the LabVIEW software. Artificial neural network was trained offline using the Matlab software and the resultant network parameters were transferred to LabVIEW real time system. In the proposed system, it is possible to precisely identify the individual faulty switch (may be due to open-switch (or) short-switch failure) of multilevel inverters.

Developing a New Computer-Aided Clinical Decision Support System for Prediction of Successful Postcardioversion Patients with Persistent Atrial Fibrillation.

We propose a new algorithm to predict the outcome of direct-current electric (DCE) cardioversion for atrial fibrillation (AF) patients. AF is the most common cardiac arrhythmia and DCE cardioversion is a noninvasive treatment to end AF and return the patient to sinus rhythm (SR). Unfortunately, there is a high risk of AF recurrence in persistent AF patients; hence clinically it is important to predict the DCE outcome in order to avoid the procedure's side effects. This study develops a feature extraction and classification framework to predict AF recurrence patients from the underlying structure of atrial activity (AA). A multiresolution signal decomposition technique, based on matching pursuit (MP), was used to project the AA over a dictionary of wavelets. Seven novel features were derived from the decompositions and were employed in a quadratic discrimination analysis classification to predict the success of post-DCE cardioversion in 40 patients with persistent AF. The proposed algorithm achieved 100% sensitivity and 95% specificity, indicating that the proposed computational approach captures detailed structural information about the underlying AA and could provide reliable information for effective management of AF.

Double Fault Detection of Cone-Shaped Redundant IMUs Using Wavelet Transformation and EPSA

A model-free hybrid fault diagnosis technique is proposed to improve the performance of single and double fault detection and isolation. This is a model-free hybrid method which combines the extended parity space approach (EPSA) with a multi-resolution signal decomposition by using a discrete wavelet transform (DWT). Conventional EPSA can detect and isolate single and double faults. The performance of fault detection and isolation is influenced by the relative size of noise and fault. In this paper; the DWT helps to cancel the high frequency sensor noise. The proposed technique can improve low fault detection and isolation probability by utilizing the EPSA with DWT. To verify the effectiveness of the proposed fault detection method Monte Carlo numerical simulations are performed for a redundant inertial measurement unit (RIMU).