Fault Diagnosis in Induction Motor Using Soft Computing Techniques

Induction motors are one of the commonly used electrical machines in industry because of various technical and economical reasons. These machines face various stresses during operating conditions. These stresses might lead to some modes of failures/faults. Hence condition monitoring becomes necessary in order to avoid catastrophic faults. Various fault monitoring techniques for induction motors can be broadly categorized as model based techniques, signal processing techniques, and soft computing techniques. In case of model based techniques, accurate models of the faulty machine are essentially required for achieving a good fault diagnosis. Sometimes it becomes difficult to obtain accurate models of the faulty machines and also to apply model based techniques. Soft computing techniques provide good analysis of a faulty system even if accurate models are unavailable. Besides giving improved performance these techniques are easy to extend and modify. These can be made adaptive by the incorporation of new data or information. Multilayer perceptron neural network using back propagation algorithm have been extensively applied earlier for the detection of an inter-turn short circuit fault in the stator winding of an induction motor. This thesis extends applying other neuro-computing paradigms such as recurrent neural network (RNN), radial basis function neural network (RBFNN), and adaptive neural fuzzy inference system (ANFIS) for the detection and location of an inter-turn short circuit fault in the stator winding of an induction motor. By using the neural networks, one can identify the particular phase of the induction motor where the inter-turn short circuit fault occurs. Subsequently, a discrete wavelet technique is exploited not only for the detection and location of an inter-turn short circuit fault but also to find out the quantification of degree of this fault in the stator winding of an induction motor. In this work, we have developed an experimental setup for the calculation of induction motor parameters under both healthy and inter-turn short circuit faulty conditions. These parameters are used to generate the phase shifts between the line currents and phase voltages under different load conditions. The detection and location of an inter-turn short circuit fault in the stator winding is based on the monitoring of these three phase shifts. Extensive simulation results are presented in this thesis to demonstrate the effectiveness of the proposed methods

EVALUATION OF ANTIDIABETIC AND ANTIHYPERLIPIDEMIC EFFECT OF VERNONIA DIVERGENS IN STREPTOZOTOCIN-INDUCED DIABETIC RATS

Objective: The current investigation for antidiabetic activity of the plant Vernonia divergens (DC.) Edgew. has not been reported till date. However, to enlighten the folkloric claim of the plant, the study was carried out on various animal models such as albino mice, albino rabbits, Wistar rats, rabbits, hamsters, dogs, and monkeys. Methods: The whole plant of V. divergens was studied on various animal models. Screening methods generally have been carried out on rodents and non-rodents, respectively. Various biochemical and hematological parameters such as serum glucose, plasma insulin, lipid profile and activities of liver enzymes, red blood cells, white blood cells, hemoglobin, and differential counts were measured to assess the antihyperglycemic and antihyperlipidemic activities as well as safety profile of the extract. Results: Among all experimental extracts of V. divergens, it was found that the aqueous and methanolic extracts had maximum control of blood glucose in diabetic Wistar rats. While comparing with normoglycemic animals, it was observed that reduction of blood sugar level and increase in plasma insulin level are maximum with test extract. Among the study, the effects of the methanolic extract of V. divergens (MEVD) and aqueous extract of V. divergens (AEVD) were done through oral route in both the models, i.e., normoglycemic and hyperglycemic animal models. The safety profile was evaluated by toxicological evaluation and observed that, even at a higher dose level of 3000 mg/kg body weight, the MEVD and AEVD were safe and retain normal physiological and behavioral effect. The whole protein, whole cholesterol, aspartate aminotransferase, alanine aminotransferase, and alkaline phosphatase enzyme activity of streptozotocin-administered rats showed significantly higher than normal rats, and the test extract-treated rats significantly reduced the elevated levels. Conclusion: It is concluded that the MEVD and AEVD (DC.) Edgew. might be beneficial in effectively reducing the blood glucose concentration and managing the various complications of diabetes. However, in comparison between both the extracts, the methanol extract was found to be significantly more potent than that of the A.E. in all aspects

Identification of Real-Time Maglev Plant using Long-Short Term Memory network based Deep learning Technique

Deep neural network has emerged as one of the most effective networks for modeling of highly non-linear complex real-time systems. The Long-Short Term Memory network (LSTM) which is a one of the variants of Recurrent Neural Network (RNN) has been proposed for the identification of a highly nonlinear Maglev plant. The comparative analysis of its performance is carried out with the Functional Link Artificial Neural Network- Least Mean Square (FLANN-LMS), FLANN-Particle Swarm Optimization (FLANN-PSO), FLANN-Teaching Learning Based Optimization (FLANN-TLBO) and FLANN-Black Widow Optimization (FLANN-BWO) algorithm. The proposed LSTM model is a feed forward neural network trained by a simple iterative method called the ADAM algorithm. The obtained results indicate that the proposed network has better performance than the other competitive networks in terms of the MSE, CPU time and convergence rate. To validate the dominance of the proposed network, a statistical tests, i.e. the Friedman test, is also applied.

Identification of Real-Time Maglev Plant using Long-Short Term Memory Network based Deep Learning Technique

1101-1105Deep neural network has emerged as one of the most effective networks for modeling of highly non-linear complex real-time systems. The long-short term memory network (LSTM) which is a one of the variants of recurrent neural network (RNN) has been proposed for the identification of a highly nonlinear Maglev plant. The comparative analysis of its performance is carried out with the functional link artificial neural network- least mean square (FLANN-LMS), FLANN-particle swarm optimization (FLANN-PSO), FLANN-teaching learning based optimization (FLANN-TLBO) and FLANN-black widow optimization (FLANN-BWO) algorithm. The proposed LSTM model is a feed forward neural network trained by a simple iterative method called the ADAM algorithm. The obtained results indicate that the proposed network has better performance than the other competitive networks in terms of the MSE, CPU time and convergence rate. To validate the dominance of the proposed network, a statistical tests, i.e. the Friedman test, is also applied

Diagnostic Performance of Magnetic Resonance Imaging in Lumbar Disc Prolapse With Focal Neurological Deficits

Introduction: A magnetic resonance imaging (MRI) scan is now theaccepted gold standard for the diagnosing a lumbar disc prolapse. In this study, we aimed to determine the degree to which a 1.5 Tesla MRI corresponds with the clinical features and findings in cases of lumbar disc prolapse with focal neurological deficits. Materials and methods: A prospective cohort study was conducted at PBM Hospital, a tertiary-care institution between July 2018 and July 2020. Over a two-year period, 150 consecutively sampled patients with lumbar disc prolapse were included in this study. All the patients were subjected to a 1.5 Tesla MRI scan. Observations: Out of the total 150 referred patients for low back ache with neurological deficits, 135 (90%) patients were diagnosed with disc prolapse by our reference standard, of which 128 patients (85.33%) were positively diagnosed by magnetic resonance imaging. The calculated sensitivity was 94.81% and specificity was 80.00% with an accuracy of 90.37% .Results:  All of the patients referred to us with chronic low back ache and focal neurological deficits had degenerative findings at various lumbar spinal levels. 1.5 T MRI for patients with low back ache with focal neurological deficits has high accuracy and can be used for detailed evaluation of the etiology of the symptoms, for precise clinical management decisions and for preoperative surgical planning

Correlation of non-alcoholic fatty pancreas with non-alcoholic fatty liver disease by transabdominal ultrasound examination

Background: Nowadays, fat infiltration in the pancreas is a common finding during the routine abdominal ultrasound examination. it is also often noted that fatty infiltration in pancreas is a finding along with the fatty infiltration in the liver. So, an association between no alcoholic fatty pancreas (NAFP) and non-alcoholic fatty liver disease (NAFLD) is plausible which is noted mainly in the urban population and the developing countries. Many studies have been done on this subject. it is also noted in many previous studies, several metabolic factors which play a role in the relation to fatty pancreas are also related to the fatty liver disease. Today, the clinical consequences of non-alcoholic fatty pancreas have brought it to attention of many clinicians, especially those involved in gastroenterology. Our study aimed to identify the possible association between the non-alcoholic fatty pancreas with non-alcoholic fatty liver disease. Materials and method: In this retrospective study 1220 cases were taken from Jan 2018 to Jan 2019. The age group was between 20 to 60 years. This study was undertaken in JJ diagnostic centre, Bhubaneswar, Odisha, India. We had used the GE LOGIQ P5 ultrasound machine with 3.5 MHz curvilinear probe in our diagnostic clinic for this study. The scans were done by two experienced radiologists. Results: Through our study it is observed that there is a positive correlation between prevalence of NAFP and NAFLD. Fatty Pancreas is a common finding during abdominal ultrasound examination Conclusion: though our study has detected increased prevalence of fatty pancreas in the normal population and association with fatty liver. Further studies are required to find out how the metabolic abnormalities are related with non-alcoholic fatty pancreas. Keywords: NAFP (Non-alcoholic fatty pancreas) NAFLD (Non-alcoholic fatty liver disease, Abdominal Ultrasound, Metabolic Diseases

Controlled Crystallization of Acetazolamide from Aqueous Polymeric Solutions for Enhancing Dissolution Rate: Application of Statistical Moment Theory and Molecular Docking

515-521Presence of additives in crystallization process in a controlled manner can lead to different crystal morphologies which could have a favourable impact on drug dissolution rate. Four different hydrophilic polymers (methylcellulose, hydroxypropyl methylcellulose, polyvinyl alcohol, and carboxymethyl cellulose) were used for the controlled crystallization of acetazolamide (ACZ) by solvent evaporation technique. Crystal imperfections of ACZ occurred in the lattice of growing crystal when crystallized from aqueous polymeric solution and evaluated using both the traditional Full Width at Half Maximum (FWHM) () and statistical mean value of the XRD peak width (). Crystal imperfection has brought about significant improvement in the dissolution of newly produced acetazolamide crystals. ACZ crystal produced in presence of Hydroxypropyl methylcellulose (AHPMC) showed crystal imperfection to the maximum extent and also the greatest dissolution of the drug was noticed from AHPMC compared to other crystals. Statistical mean value of the peak width of XRD data as the error-free technique has been utilized successfully for estimating crystallite properties of acetazolamide crystallized from ethanol as solvent and aqueous polymeric solution as anti-solvent. Crystallite properties using traditional Full Width Half Maxima method and the error-free Statistical Moment Analysis were compared. This controlled crystallization technique could be utilized in the design and development of formulation for improved solubility and bioavailability of the drug

The neglected continuously emerging Marburg virus disease in Africa: A global public health threat

Abstract Background and Aim Severe viral hemorrhagic fever (VHF) is caused by Marburg virus which is a member of the Filoviridae (filovirus) family. Many Marburg virus disease (MVD) outbreaks are reported in five decades. A major notable outbreak with substantial reported cases of infections and deaths was in 2022 in Uganda. The World Health Organisation (WHO) reported MVD outbreak in Ghana in July 2022 following the detection of two probable VHF patients there. Further, the virus was reported from two other African countries, the Equatorial Guinea (February 2023) and Tanzania (March 2023). There have been 35 deaths out of 40 reported cases in Equatorial Guinea, and six of the nine confirmed cases in Tanzania so far. Methods Data particularly on the several MVD outbreaks as reported from the African countries were searched on various databases including the Pubmed, Scopus, and Web‐of‐science. Also, the primary data and reports from health agencies like the WHO and the Centers for Disease Control and Prevention CDC) were evaluated and the efficacy reviewed. Results Chiroptera in general and bat species like Rousettus aegyptiacus and Hipposideros caffer in particular are natural reservoirs of the Marburg virus. MVD‐infected nonhuman primate African fruit‐bat and the MVD‐infected humans pose significant risk in human infections. Cross‐border viral transmission and its potential further international ramification concerns raise the risk of its rapid spread and a potential outbreak. Occurrence of MVD is becoming more frequent in Africa with higher case fatality rates. Effective prophylactic and therapeutic interventions to counter this deadly virus are suggested. Conclusion In the face of the lack of effective therapeutics and preventives against MVD, supportive care is the only available option which contributes to the growing concern and disease severity. In view of the preventive approaches involving effective surveillance and monitoring system following the “One Health” model is extremely beneficial to ensure a healthy world for all, this article aims at emphasizing several MVD outbreaks, epidemiology, zoonosis of the virus, current treatment strategies, risk assessments, and the mitigation strategies against MVD