Groundwater Drought and Cycles in Xuchang City, China

The urban groundwater system is complex and affected by the interaction of natural and human factors. Groundwater scarcity can no longer reflect this complex situation, and the concept of groundwater drought can better interpret this situation. The groundwater drought cycle is the time interval in which groundwater droughts occur repeatedly and twice in a row. The study of the groundwater drought cycle can more comprehensively grasp the development characteristics of the groundwater drought, which is of great importance for the development, utilization, and protection of groundwater. This study used monthly observation data from seven groundwater wells in Xuchang, China, in the period 1980–2018. We applied the Kolmogorov–Smirnov test to select the best fitting distribution function and constructed a Standardized Groundwater Index (SGI). We analyzed groundwater drought at different time scales and used Morlet’s continuous complex wavelet transform to analyze the groundwater drought cycles. The following results were obtained: 1) the maximum intensity of groundwater drought in the seven observation wells ranged from 104.40 to 187.10. Well-3# has the most severe groundwater drought; 2) the drought years of well-5# were concentrated in 1984–1987 and 2003–2012 and those in the other wells in 1994–1999 and 2014–2018; and 3) the groundwater drought cycles in the seven observation wells were 97–120 months, and the average period is about 110 months. The cycle length had the following order: well-7# > well-4# > well-5# > well-2# > well-1# > well-3# > well-6. Therefore, Morlet wavelet transform analysis can be used to study the groundwater drought cycles and can be more intuitive in understanding the development of regional groundwater droughts. In addition, through the study of the Xuchang groundwater drought and its cycle, the groundwater drought in Xuchang city has been revealed, which can help local relevant departments to provide technical support and a scientific basis for the development, utilization, and protection of groundwater in the region.

Motor Imagery EEG Signal Recognition Using Deep Convolution Neural Network

Brain computer interaction (BCI) based on EEG can help patients with limb dyskinesia to carry out daily life and rehabilitation training. However, due to the low signal-to-noise ratio and large individual differences, EEG feature extraction and classification have the problems of low accuracy and efficiency. To solve this problem, this paper proposes a recognition method of motor imagery EEG signal based on deep convolution network. This method firstly aims at the problem of low quality of EEG signal characteristic data, and uses short-time Fourier transform (STFT) and continuous Morlet wavelet transform (CMWT) to preprocess the collected experimental data sets based on time series characteristics. So as to obtain EEG signals that are distinct and have time-frequency characteristics. And based on the improved CNN network model to efficiently recognize EEG signals, to achieve high-quality EEG feature extraction and classification. Further improve the quality of EEG signal feature acquisition, and ensure the high accuracy and precision of EEG signal recognition. Finally, the proposed method is validated based on the BCI competiton dataset and laboratory measured data. Experimental results show that the accuracy of this method for EEG signal recognition is 0.9324, the precision is 0.9653, and the AUC is 0.9464. It shows good practicality and applicability.

Rotor imbalance detection and quantification in wind turbines via vibration analysis

Rotor imbalance is a common fault in wind turbines, which may enhance radial loads that induce faults on the main bearing and gearbox. It usually results from the asymmetry of the air dynamics caused by blade crack, icing, etc. A simple and effective method on rotor imbalance detection and quantification is presented using the vibration signal collected from the accelerometer monitoring the wind turbine drive train. A vibration model describing the rotor imbalance under blade crack is proposed. The complex morlet wavelet transform is applied to the detection of the rotational frequency of rotor hub which represents the rotor hub. A health indicator that can quantify the degree of the rotor imbalance is designed. The proposed methods have successfully detected and quantified the rotor imbalance caused by blade crack in an on-site wind turbine.

Abstract 364: Continuous Electrocardiogram Classification During Resuscitation Using Transfer Learning

Introduction: Cardiac arrest resuscitation requires CPR interruption for ECG rhythm analysis, but pausing CPR is adversely associated with survival. Ideally, automated rhythm analysis would occur agnostic of CPR state throughout resuscitation and discriminate non-shockable from shockable rhythms. Transfer learning of pre-trained deep convolutional neural networks (CNNs) may enable accurate ECG analysis when applied to time-frequency representations of the ECG. We designed and evaluated a transfer learning algorithm to identify ventricular fibrillation (VF), asystole (AS), and organized (OR) rhythms agnostic of CPR. Methods: In this observational study of out-of-hospital cardiac arrest, rhythms were manually diagnosed in continuous defibrillator ECG recordings. Non-overlapping adjacent 2-s ECG segments were extracted from the first 30 min of each case regardless of CPR during VF, AS, and OR. Each segment was represented by an intrafrequency-normalized Morlet wavelet transform from 4-40 Hz. Using a 2/3 subset of patients for training, a series of two ResNet-101 CNNs were retrained to perform a shock decision (VF vs. non-shockable) followed by a specific non-shockable prediction (AS, OR, or Indeterminate). Performance was evaluated in a 1/3 validation subset of patients using a range of probability decision thresholds to predict the class of each segment. Results: In total, 275100 segments were collected from 461 patients. Of 90962 segments from 152 validation patients, using a 0.7 probability threshold for class prediction, 21% (18930/90962) were indeterminate, shock vs. no-shock sensitivity and specificity were 90% (19702/21930) and 97% (48421/50102), and specificities among non-shockable rhythms for AS vs. OR were 84% (5032/5998) and 86% (37760/44104), respectively (Fig 1). Conclusion: Transfer learning may enable shock/no-shock and rhythm-specific ECG classification continuously throughout resuscitation regardless of CPR.

Detecting Punctual Damage to Gears through the Continuous Morlet Wavelet Transform

In predictive maintenance, vibration signal analyses are frequently used to diagnose reducer failures because these analyses contain information about the conditions of the mechanical components. Reducer vibration signals are very noisy and the signal-to-noise ratio is so low that extracting information from the signal components is complex, especially in practical situations. Therefore, signal processing techniques are used to solve this problem and facilitate the retrieval of information. In this work, the adopted technique included noise-canceling technique, synchronous temporal mean (TSA), and continuous Morlet wavelet transform (CWT), designed to extract resources and diagnose local gear damage. These techniques are used in measured signals in an experimental workbench consisting of the gear pair coupled to a motor and a generator. The experiment was monitored according to the conditions of a gear pair throughout its useful life. The continuous wavelet transforms accurately identified faults in the gear teeth, and it was possible to detect in which tooth the fault was occurring.

Rotor Blade Pitch Imbalance Fault Detection for Variable-Speed Marine Current Turbines via Generator Power Signal Analysis

<div>Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic environments. However, due to the harsh conditions that these turbines operate in, system performance naturally degrades over time. Thus, ensuring efficient condition-based maintenance is imperative towards guaranteeing reliable operation and reduced costs for hydroelectric power. </div><div>This paper proposes a novel framework aimed at identifying and classifying the severity of rotor blade pitch imbalance faults experienced by marine current turbines (MCTs). In the framework, a Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire the wavelet coefficients encompassed within the 1P frequency range of the turbine's rotor shaft. From these coefficients, several statistical indices are tabulated into a six-dimensional feature space. Next, Principle Component Analysis (PCA) is employed on the resulting feature space for dimensionality reduction, followed by the application of a K-Nearest Neighbor (KNN) machine learning algorithm for fault detection and severity classification. The framework's effectiveness is validated using a high-fidelity MCT numerical simulation platform, where results demonstrate that pitch imbalance faults can be accurately detected 100% of the time and classified based upon severity more than 97% of the time.</div>

Rotor Blade Pitch Imbalance Fault Detection for Variable-Speed Marine Current Turbines via Generator Power Signal Analysis

<div>Marine hydrokinetic (MHK) turbines extract renewable energy from oceanic environments. However, due to the harsh conditions that these turbines operate in, system performance naturally degrades over time. Thus, ensuring efficient condition-based maintenance is imperative towards guaranteeing reliable operation and reduced costs for hydroelectric power. </div><div>This paper proposes a novel framework aimed at identifying and classifying the severity of rotor blade pitch imbalance faults experienced by marine current turbines (MCTs). In the framework, a Continuous Morlet Wavelet Transform (CMWT) is first utilized to acquire the wavelet coefficients encompassed within the 1P frequency range of the turbine's rotor shaft. From these coefficients, several statistical indices are tabulated into a six-dimensional feature space. Next, Principle Component Analysis (PCA) is employed on the resulting feature space for dimensionality reduction, followed by the application of a K-Nearest Neighbor (KNN) machine learning algorithm for fault detection and severity classification. The framework's effectiveness is validated using a high-fidelity MCT numerical simulation platform, where results demonstrate that pitch imbalance faults can be accurately detected 100% of the time and classified based upon severity more than 97% of the time.</div>

Experimental Study of a Bulb Turbine Model During Start-Up and at Speed-No-Load Conditions, Based on the Measurement of Unsteady Pressure

Experimental analysis of a bulb turbine during the start-up sequence and in speed-no-load (SNL) operating conditions was performed in a closed-loop circuit. This study focuses on pressure fluctuations across the machine. The turbine was equipped with 26 pressure sensors on one runner blade and 16 in the stationary reference frame. Strain measurements were also performed on two other runner blades. The first section of this analysis focuses on SNL operating conditions using standard Fourier data processing. The results show that three rotating flow phenomena are only present close to the runner. One of them corresponds to the interblade vortex at f/fr=4.00, whereas the two others, which have subsynchronous runner frequencies, are consistent with a possible rotating stall. These phenomena, which exist predominantly on the suction side, have a strong influence on runner blade strain. The second section of the study concentrates on a time-frequency analysis using the Morlet wavelet transform. It reveals that the two subsynchronous flow structures appear at the end of the start-up and exhibit bistable behavior. As well, each of these phenomena acts differently on the blade. These phenomena also interact with the interblade vortex.