Open-Circuit Fault Detection in a Multilevel Inverter Using Sub-Band Wavelet Energy

Recent research has focused on sustainable development and renewable energy resources, thus motivating nonconventional cutting-edge technology development. Multilevel inverters are cost-efficient devices with IGBT switches that can be used in ac power applications with reduced harmonics. They are widely used in the power electronics industry. However, under extreme stress, the IGBT switches can experience a fault, which can lead to undesirable operation. There is a need for a reliable system for detecting switch faults. This paper proposes a signal processing method to detect open-circuit problems in IGBT switches. Relative wavelet energy has been used as a feature for a machine learning algorithm to diagnose and classify the faulted switches. The switching sequence can be altered to restore a healthy output voltage. Inverter faults have been diagnosed by using support vector machine (SVM) and decision tree (DT), and an ensemble model based on decision tree (DT) and XG boost algorithm was developed, which yielded 92%, 88%, and 94.12% accuracy, respectively.

Hyperspectral Estimation of Winter Wheat Leaf Area Index Based on Continuous Wavelet Transform and Fractional Order Differentiation

Leaf area index (LAI) is highly related to crop growth, and the traditional LAI measurement methods are field destructive and unable to be acquired by large-scale, continuous, and real-time means. In this study, fractional order differential and continuous wavelet transform were used to process the canopy hyperspectral reflectance data of winter wheat, the fractional order differential spectral bands and wavelet energy coefficients with more sensitive to LAI changes were screened by correlation analysis, and the optimal subset regression and support vector machine were used to construct the LAI estimation models for different growth stages. The precision evaluation results showed that the LAI estimation models constructed by using wavelet energy coefficients combined with a support vector machine at the jointing stage, fractional order differential combined with support vector machine at the booting stage, and wavelet energy coefficients combined with optimal subset regression at the flowering and filling stages had the best prediction performance. Among these, both flowering and filling stages could be used as the best growth stages for LAI estimation with modeling and validation R2 of 0.87 and 0.71, 0.84 and 0.77, respectively. This study can provide technical reference for LAI estimation of crops based on remote sensing technology.

Elevator Running Fault Monitoring Method Based on Vibration Signal

According to the one-dimensional characteristics of the vibration signal, this paper proposes an elevator operation fault monitoring method based on one-dimensional convolutional neural network (1-DCNN). It can solve the problems of traditional elevator fault monitoring methods that require complex feature extraction processes and a large amount of diagnostic experience. Because the elevator fault monitoring field has less fault information, it is different from the large sample situation in the field of face recognition. Aiming at the problem of small samples, this paper first preprocesses elevator vibration signals through singular value decomposition (SVD) and wavelet transform, then uses wavelet transform to extract wavelet energy features of the original vibration signals, and then use PCA to reduce the feature data to the dimension with a cumulative contribution rate of greater than 85%. When reducing the dimensionality, the original characteristics of the features are preserved as much as possible. When designing the 1-CNN, the K-fold cross-validation method is added to obtain as many abnormalities from the sample set as possible. The information is finally trained using the 1-CNN and classified by softmax regression. In order to verify the performance of the algorithm, the original vibration signal was used as the input of the 1-CNN, and the wavelet energy feature without PCA dimensionality reduction was used as the input of the 1-CNN. The experimental results showed that the 1-DCNN model with PCA dimension-reduced feature data as input can effectively extract and identify the features of normal and abnormal states and has high fault identification accuracy, and good results have been obtained.

Towards Interpretable Machine Learning in EEG Analysis

In this paper a machine learning model for automatic detection of abnormalities in electroencephalography (EEG) is dissected into parts, so that the influence of each part on the classification accuracy score can be examined. The most successful setup of several shallow artificial neural networks aggregated via voting results in accuracy of 81%. Stepwise simplification of the model shows the expected decrease in accuracy, but a naive model with thresholding of a single extracted feature (relative wavelet energy) is still able to achieve 75%, which remains strongly above the random guess baseline of 54%. These results suggest the feasibility of building a simple classification model ensuring accuracy scores close to the state-of-the-art research but remaining fully interpretable.

Spectral Analysis of Muscle Hemodynamic Responses in Post-Exercise Recovery Based on Near-Infrared Spectroscopy

Spectral analysis of blood flow or blood volume oscillations can help to understand the regulatory mechanisms of microcirculation. This study aimed to explore the relationship between muscle hemodynamic response in the recovery period and exercise quantity. Fifteen healthy subjects were required to perform two sessions of submaximal plantarflexion exercise. The blood volume fluctuations in the gastrocnemius lateralis were recorded in three rest phases (before and after two exercise sessions) using near-infrared spectroscopy. Wavelet transform was used to analyze the total wavelet energy of the concerned frequency range (0.005–2 Hz), which were further divided into six frequency intervals corresponding to six vascular regulators. Wavelet amplitude and energy of each frequency interval were analyzed. Results showed that the total energy raised after each exercise session with a significant difference between rest phases 1 and 3. The wavelet amplitudes showed significant increases in frequency intervals I, III, IV, and V from phase 1 to 3 and in intervals III and IV from phase 2 to 3. The wavelet energy showed similar changes with the wavelet amplitude. The results demonstrate that local microvascular regulators contribute greatly to the blood volume oscillations, the activity levels of which are related to the exercise quantity.

DETECTION OF SEIZURE TYPES FROM THE WAVELET ENERGY OF SCALP EEG

Epilepsy is a disabling and devastating neurological disorder, characterized by recurrent seizures. These seizures are caused by the abrupt disturbance of the brain and are categorized into various types based on the clinical manifestations and localization. Seizures with clinical manifestations require immediate medical attention. In this work, an attempt has been made to differentiate the seizures with and without clinical manifestations using wavelet energy of scalp EEG signals. For this purpose, scalp EEG records from the publically available Temple University Hospital (TUH) database are considered in this work. The first four seconds of scalp EEG during seizure is subjected to seven-level Daubechies (db4) wavelet decomposition and energy is extracted from the resultant coefficients. These features are used to develop k-Nearest Neighbor (k-NN) classification model for the detection. The results show that the energy associated with most of the sub-bands exhibits significant difference (p<0.05) in these two types of seizures. It is found that the machine learning model based on k-NN achieves an accuracy of 87.6% and precision of 87.3%. Therefore, it appears that the proposed approach could aid in detecting life-threatening seizures in clinical settings.

Wavelet Entropy Energy Measure (WEEM): A multiscale measure to grade a geophysical system's predictability

&lt;p&gt;Model-free gradation of predictability of a geophysical system is essential to quantify how much inherent information is contained within the system and evaluate different forecasting methods' performance to get the best possible prediction. We conjecture that Multiscale Information enclosed in a given geophysical time series is the only input source for any forecast model. In the literature, established entropic measures dealing with grading the predictability of a time series at multiple time scales are limited. Therefore, we need an additional measure to quantify the information at multiple time scales, thereby grading the predictability level. This study introduces a novel measure, Wavelet Entropy Energy Measure (WEEM), based on Wavelet entropy to investigate a time series's energy distribution. From the WEEM analysis, predictability can be graded low to high. The difference between the entropy of a wavelet energy distribution of a time series and entropy of wavelet energy of white noise is the basis for gradation. The metric quantifies the proportion of the deterministic component of a time series in terms of energy concentration, and its range varies from zero to one. One corresponds to high predictable due to its high energy concentration and zero representing a process similar to the white noise process having scattered energy distribution. The proposed metric is normalized, handles non-stationarity, independent of the length of the data. Therefore, it can explain the evolution of predictability for any geophysical time series (ex: precipitation, streamflow, paleoclimate series) from past to the present. WEEM metric's performance can guide the forecasting models in getting the best possible prediction of a geophysical system by comparing different methods.&amp;#160;&lt;/p&gt;

Wavelet Energy Accumulation Method Applied on the Rio Papaloapan Bridge for Damage Identification

Large civil structures such as bridges must be permanently monitored to ensure integrity and avoid collapses due to damage resulting in devastating human fatalities and economic losses. In this article, a wavelet-based method called the Wavelet Energy Accumulation Method (WEAM) is developed in order to detect, locate and quantify damage in vehicular bridges. The WEAM consists of measuring the vibration signals on different points along the bridge while a vehicle crosses it, then those signals and the corresponding ones of the healthy bridge are subtracted and the Continuous Wavelet Transform (CWT) is applied on both, the healthy and the subtracted signals, to obtain the corresponding diagrams, which provide a clue about where the damage is located; then, the border effects must be eliminated. Finally, the Wavelet Energy (WE) is obtained by calculating the area under the curve along the selected range of scale for each point of the bridge deck. The energy of a healthy bridge is low and flat, whereas for a damaged bridge there is a WE accumulation at the damage location. The Rio Papaloapan Bridge (RPB) is considered for this research and the results obtained numerically and experimentally are very promissory to apply this method and avoid accidents.