Machine-learning approach for fault detection in brushless synchronous generator using vibration signals

In order to maintain continuous production and to avoid the maintenance cost increment in power plants, it is important to monitor the condition of equipment, especially the generator. Regarding the impossibility of direct access to rotating diodes in brushless synchronous generators, the condition monitoring of these elements is very important. Here, a novel fault detection method is proposed for the diode rectifier of brushless synchronous generator. At the first stage of this method, the vibration signals are recorded and feature extraction is performed by calculating the relative energy of discrete wavelet transform components. Multiclass support vector machine (MSVM) is used for classification, and the best mother wavelet and number of decomposition level are chosen based on classification performance. To enhance the performance of the classification, a modified sequential forward subset selection approach is included by which the best statistical features are selected. In this approach, besides selecting the best subset of statistical features, the classification parameter is tuned according to the selected subset to achieve the best performance. The result of the proposed method is eventually compared with those results of classification performance using conventional subset selection. Experimental results show that the proposed method can detect rectifier faults effectively

Finding the existence of of Helicobacter pylori in springs of Chaharmahal and Bakhtiari Province, Iran by polymerase chain reaction

Background and aims: Helicobacter pylori is the primary cause of various diseases. This study was conducted to determine the existence of H. pylori in springs and surface water in Chaharmahal and Bakhtiari province. Methods: In this study, 29 water samples were collected from three different locations of Zayanderud River, 24 springs, and two subterranean from June 2014 to August 2014 in Chaharmahal and Bakhtiari province. Samples were filtered and DNAs were extracted. Then, the existence of H. pylori DNA was tested by polymerase chain reaction (PCR) for 16S rRNA, glm and Cag genes. Results: In the present study, the total infection rate of samples by H. pylori was 24.13. H. pylori and DNA was detected in 100 of samples of Zayanderud River, 12.5 in springs of Dehcheshmeh in Farsan and Baram in Lordegan, and 50 in aqueducts. Conclusion: These findings show the existence of H. pylori in springs and surface water in Chaharmahal and Bakhtiari province and additional evidence for waterborne transmission of H. pylori in some environments

A review of model predictive control strategies for matrix converters

Matrix converters are a well-known class of direct AC-AC power converter topologies that can be used in applications in which compact volume and low weight are necessary. For good performance, special attention should be paid to the control scheme used for these converters. Model predictive control strategy is a promising, straightforward and flexible choice for controlling various different matrix converter topologies. This work provides a comprehensive study and detailed classification of several predictive control methods and techniques, discussing special capabilities they each add to the operation and control scheme for a range of matrix converter topologies. The paper also considers the issues regarding the implementation of model predictive control strategies for matrix converters. This survey and comparison is intended to be a useful guide for solving the related drawbacks of each topology and to enable the application of this control scheme to matrix converters in practical applications

Evaluation of the Effect of Combination Therapy on Treatment of COVID-19: A Cohort Study

Background: COVID-19 is a new disease for which a definitive treatment has not yet been proposed. Objectives: The present study aimed to investigate the effect of combination therapy on the treatment of COVID-19 due to the importance of finding an appropriate treatment for this epidemic disease. Methods: This two-center cohort study included 175 confirmed COVID-19 inpatients at two medical centers designated for the treatment of COVID-19 patients in Qom and Qazvin, Iran. In this study, four different groups of drug regimens were studied which included G1 (azithromycin, prednisolone, and naproxen), G2 (lopinavir/ritonavir, azithromycin, naproxen, and prednisolone), G3 (hydroxychloroquine, azithromycin, naproxen, and prednisolone), and G4 (levofloxacin, vancomycin, hydroxychloroquine, and oseltamivir). It should be noted that G1, G2, G3, and G4 treatment regimens were used on 48, 39,30, and 77 patients, respectively. Results: The study participants included 175 confirmed COVID-19 patients with mean±SD age of 58.9 ±15.1 years, out of whom 80 (46%) patients were male and the rest were females. The results indicated that the hospital stay period was significantly shorter in the G1 compared to other groups (G1:5.9±2.4, G2:8.1±4.2, G3: 6.3±1.7, and G4: 6.4±2.9; [P-value=0.008]). It should be noted that pulse rate, oxygen saturation, hemoglobin, and platelet count (PLT) changed significantly during the study in four treatment groups; however, a significant change in temperature, creatinine, and white blood cell (WBC) was observed only in G3, G4, and G1 groups, respectively. The number of ICU admissions and deaths were not statistically significant among the patients who received the four treatment regimens (P=0.785). Based on the results, the history of ischemic heart disease, baseline oxygen saturation, WBC, neutrophil, lymphocyte count, and C-reactive protein (CRP) are the risk factors for the prolonged hospital stay in COVID-19 patients. Conclusion: The obtained results in this study indicated that the combination of azithromycin, prednisolone, and naproxen is the most effective regimen for the treatment of COVID-19, compared to three other combination treatment regimens. Keywords: Anti-inflammatory drugs, Antiviral drugs, Combination therapy, Corticosteroid, COVID-19, Immunomodulators drug

Application of Acoustic Signals for Rectifier Fault Detection in Brushless Synchronous Generator

One of the most important issues that power companies face when trying to reduce time and cost maintenance is condition monitoring. In electricity market worldwide, a significant amount of electrical energy is produced by synchronous machines. One type of these machines is brushless synchronous generators in which the rectifier bridge is mounted on rotating shafts. Since bridge terminals are not accessible in this type of generators, it is difficult to detect the possible faults on the rectifier bridge. Therefore, in this paper, a method is proposed to facilitate the rectifier fault detection. The proposed method is then evaluated by applying two conventional kinds of faults on rectifier bridges including one diode open-circuit and two diode open-circuit (one phase open-circuit of the armature winding in the auxiliary generator in experimental set). To extract suitable features for fault detection, the wavelet transform has been used on recorded audio signals. For classifying faulty and healthy states, K-Nearest Neighbours (KNN) supervised classification method was used. The results show a good accuracy of the proposed method

Modeling of Pulsed Transformer with Nanocrystalline Cores

Recently tape wound cores, due to their excellent properties, are widely used in transformers for pulsed or high frequency applications. The spiral structure of these cores affects the flux distribution inside the core and causes complication of the magnetic analysis and consequently the circuit analysis. In this paper, a model based on reluctance networks method is used to analyze the magnetic flux in toroidal wound cores and losses calculation. A Preisach based hysteresis model is included in the model to consider the nonlinear characteristic of the core. Magnetic losses are calculated by having the flux density in different points of the core and using the hysteresis model. A transformer for using in a series resonant converter is modeled and implemented. The modeling results are compared with experimental measurements and FEM results to evaluate the validity of the model. Comparisons show the accuracy of the model besides its simplicity and fast convergence