Comparison of two classifiers; K-nearest neighbor and artificial neural network, for fault diagnosis on a main engine journal-bearing,”

Abstract. Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine

Adaptive Neuro-Fuzzy Inference System (Anfis) to Predict Ci Engine Parameters Fueled with Nano-Particles Additive to Diesel Fuel

This paper studies the use of adaptive neuro-fuzzy inference system (ANFIS) to predict the performance parameters and exhaust emissions of a diesel engine operating on nanodiesel blended fuels. In order to predict the engine parameters, the whole experimental data were randomly divided into training and testing data. For ANFIS modelling, Gaussian curve membership function (gaussmf) and 200 training epochs (iteration) were found to be optimum choices for training process. The results demonstrate that ANFIS is capable of predicting the diesel engine performance and emissions. In the experimental step, Carbon nano tubes (CNT) (40, 80 and 120 ppm) and nano silver particles (40, 80 and 120 ppm) with nano-structure were prepared and added as additive to the diesel fuel. Six cylinders, four-stroke diesel engine was fuelled with these new blended fuels and operated at different engine speeds. Experimental test results indicated the fact that adding nano particles to diesel fuel, increased diesel engine power and torque output. For nano-diesel it was found that the brake specific fuel consumption (bsfc) was decreased compared to the net diesel fuel. The results proved that with increase of nano particles concentrations (from 40 ppm to 120 ppm) in diesel fuel, CO2 emission increased. CO emission in diesel fuel with nano-particles was lower significantly compared to pure diesel fuel. UHC emission with silver nano-diesel blended fuel decreased while with fuels that contains CNT nano particles increased. The trend of NOx emission was inverse compared to the UHC emission. With adding nano particles to the blended fuels, NOx increased compared to the net diesel fuel. The tests revealed that silver & CNT nano particles can be used as additive in diesel fuel to improve combustion of the fuel and reduce the exhaust emissions significantly

Primary composite lymphoma of the lung: A case report

Herein, we report a rare case of primary lung lymphoma in a 61 year-old woman with a history of 6-month nonspecific symptoms like dry cough, fever, chills and weight loss. She was admitted to a hospital and received broadspectrum antibiotics but discharged without full recovery. In her second hospital admission, a bronchoscopic evaluation and transbronchial biopsy were performed, which were not diagnostic. Finally, an open lung biopsy was done. Immunohistochemical (IHC) staining of the specimen suggested pulmonary Hodgkin lymphoma. Because of disease recurrence, a second bronchoscopy was performed and endobronchial biopsy revealed transformation to anaplastic lymphoma. In the second recurrence, we decided to reevaluate the last biopsy specimens in greater details. Finally, after conduction of several staining patterns, the diagnosis of primary composite lymphoma of the lung was made. © 2014 NRITLD

Comparison of Two Classifiers; K-Nearest Neighbor and Artificial Neural Network, for Fault Diagnosis on a Main Engine Journal-Bearing

Vibration analysis is an accepted method in condition monitoring of machines, since it can provide useful and reliable information about machine working condition. This paper surveys a new scheme for fault diagnosis of main journal-bearings of internal combustion (IC) engine based on power spectral density (PSD) technique and two classifiers, namely, K-nearest neighbor (KNN) and artificial neural network (ANN). Vibration signals for three different conditions of journal-bearing; normal, with oil starvation condition and extreme wear fault were acquired from an IC engine. PSD was applied to process the vibration signals. Thirty features were extracted from the PSD values of signals as a feature source for fault diagnosis. KNN and ANN were trained by training data set and then used as diagnostic classifiers. Variable K value and hidden neuron count (N) were used in the range of 1 to 20, with a step size of 1 for KNN and ANN to gain the best classification results. The roles of PSD, KNN and ANN techniques were studied. From the results, it is shown that the performance of ANN is better than KNN. The experimental results dèmonstrate that the proposed diagnostic method can reliably separate different fault conditions in main journal-bearings of IC engine

Validity of urine neutrophile gelatinase-associated lipocalin in children with primary vesicoureteral reflux

Background: Vesicoureteral reflux (VUR) is the most common congenital urinary tract abnormality in children. The objective of this study was to evaluate the diagnostic value of urine neutrophil gelatinase-associated lipocalin (NGAL) in children with primary vesicoureteral reflux (VUR). Materials and methods: A total of 69 patients were evaluated in 2 groups with (32) and without (37) VUR. Patients with secondary VUR, infectious or inflammatory disorders, obstructive uropathies, and acute or chronic kidney disease were excluded. Urine NGAL level was measured by ELISA kit. Results: Mean age of children with VUR was 36.84 ± 28.16, compared to those without VUR 32.32 ± 29.08, with no significant difference (p = 0.51). Mean urine NGAL (p = 0.012) and urine NGAL/Cr (p = 0.003) were higher in patients with VUR. In addition, urine NGAL/Cr increased significantly in patients with decreased parenchymal function, compared to those with normal DMSA scan. Using the cutoff value of 0.888, urine NGAL had 84 sensitivity and 81 specificity for diagnosis of VUR. Based on AUC (0.86), urine NGAL had acceptable diagnostic accuracy in children with VUR. Conclusion: The results of this study support the evidence that urine NGAL/Cr is a sensitive, specific and accurate biomarker for diagnosis of children with primary VUR. © 2019, Springer Nature B.V

Predictive Value of Serum Interleukins in Children with Idiopathic Nephrotic Syndrome

Pro-inflammatory cytokines have been suggested in the pathogenesis of idiopathic nephrotic syndrome (INS), with conflicting results. This study was performed to identify alteration of different serum interleukins (ILs) in children with INS, and their predictive value in response to steroid treatment. Three groups of children (27; steroid-sensitive INS, 21; steroid-resistant INS, and 19 healthy controls) with normal serum C3, negative serologic tests of hepatitis B virus (HBV), hepatitis C virus (HCV), human immune deficiency virus (HIV), and parasitic infections were included in this study. Serum concentrations of IL-1β, IL-2, IL-6, IL-8, IL-13, and IL-18 were measured, using quantitative colorimetric sandwich ELISA kits. Children with secondary nephrotic syndrome, inflammations, systemic disorders, and chronic kidney disease were excluded. The serum concentration of all ILs; except IL-13 and IL-18; was significantly higher in children with INS, compared with the healthy controls. Serum IL-2 had the highest sensitivity of (95.24) in patients with INS. All of the serum ILs had acceptable accuracy in children with INS, compared with the control group. The serum concentration of IL-1β, IL-6, and IL-8 was significantly higher in children with steroid-sensitive nephrotic syndrome (SSNS), compared with steroid-resistant nephrotic syndrome (SRNS). All of these ILs had acceptable accuracy for the prediction of steroid response in patients with INS. Our findings suggested the pathogenic role of pro-inflammatory cytokines in children with INS, of which IL-1β, IL-6, and IL-8 were accurate biomarkers for the prediction of steroid response in these patients