Artificial neural network based classification of faults in centrifugal water pump

The detection and diagnosis of faults are of great practical significance for the safe operation of a plant. Early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. This paper presents the design and development of ANN-based model for the fault detection of centrifugal water pump using a back-propagation learning algorithm and multi-layer perceptron neural network. The centrifugal pump conditions were considered to be healthy pump and faulty impeller and faulty seal and cavitation, which were four neurons of output layer with the aim of fault detection and identification. Features vector, which is one of the most significant parameters to design an appropriate neural network, was extracted from analysis of vibration signals in frequency domain by means of FFT method. The statistical features of vibration signals such as mean, standard deviation, variance, skewness and kurtosis were used as input to ANN. Different neural network structures are analyzed to determine the optimal neural network with regards to the number of hidden layers. The results indicate that the designed system is capable of classifying records with 100 % accuracy with one hidden layer of neurons in the neural network

Sensoriamento remoto multiespectral no manejo sítio‑específico da adubação nitrogenada

The objective of this work was to evaluate the use of multispectral remote sensing for site‑specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn‑planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil‑adjusted vegetation index (Savi), optimized soil‑adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R2=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0–2.5 kg), medium (2.5–3 kg), and high (3–3.3 kg).O objetivo deste trabalho foi avaliar o uso de sensoriamento remoto multiespectral no manejo sítio‑específico da adubação nitrogenada. Imagens de satélite do “advanced spaceborne thermal emission e reflection radiometer” (Aster) foram obtidas em uma área de 23 ha cultivados com milho, no Irã. Para a coleta das amostras de campo, foi feita a seleção de 53 pixels, por meio do método de amostragem aleatória sistemática. Avaliou-se o teor de nitrogênio total nos tecidos foliares do milho, nesses pixels. Para estimar o teor de nitrogênio da parte aérea do milho, foram utilizados diferentes índices de vegetação, como “normalized difference vegetation index” (NDVI), “soil‑adjusted vegetation index” (Savi), “optimized soil‑adjusted vegetation index” (Osavi), “modified chlorophyll absorption ratio index 2” (MCARI2) e “modified triangle vegetation index 2” (MTVI2). Utilizou-se a técnica de classificação supervisionada com classificador “spectral angle mapper” (SAM) para a geração do mapa de adubação nitrogenada. O MTVI2 apresentou maior correlação (R2=0,87) e é um bom previsor do conteúdo de nitrogênio no estágio V13, 60 dias após o cultivo. Imagens Aster podem ser utilizadas para prever o status de nitrogênio na parte aérea do milho. Os resultados de classificação indicam três níveis de nitrogênio requerido por pixel: baixo (0–2,5 kg), médio (2,5–3 kg) e alto (3–3,3 kg)

Bis(2,6-dimethyl­pyridine-κN)gold(I) tetra­chloridoaurate(III)

In the cation of the title compound, [Au(C7H9N)2][AuCl4], the AuI atom is two-coordinated in a linear arrangement by two N atoms from two 2,6-dimethyl­pyridine ligands. In the anion, the AuIII atom has a virtually square-planar coordination geometry. The Au atoms both are located on centers of inversion. The crystal structure involves inter­molecular C—H⋯Cl hydrogen bonds

FEDSM2008-78249 Motion of Liquid Drops through Complex 3D Clothing

Abstract This study was concerned with the numerical simulation of a moving drop through a fabric due to a wettability gradient. The wettability gradient was introduced by varying the contact angle along the staggered fibers of a fabric. The unsteady laminar Navier-Stokes equation was solved using a fixed Eulerian unstructured grid. The Volume of Fluid Model (VOF) was used to account for tracking the gas/liquid interface. A water drop was placed on top of the fabric with an initial velocity, and the motion of the drop through the fabric was studied. Several computer simulations under different conditions such as the distance between fibers, contact angle distribution, and drop initial velocity were performed, and the results were compared with each other. In order to verify the accuracy of the computational model, the motion of a drop on a surface due to a wettabily gradient was simulated as a benchmark

(4,4′-Dimethyl-2,2′-bipyridine-κ2 N,N′)(dimethyl sulfoxide-κO)diiodidozinc(II)

In the title compound, [ZnI2(C12H12N2)(C2H6OS)], the ZnII ion is coordinated by two N atoms from a 4,4′-dimethyl-2,2′-bipyridine ligand, one O atom from a dimethyl sulfoxide mol­ecule and two I atoms in a distorted trigonal-bipyramidal geometry. Intra­molecular C—H⋯O hydrogen bonds and inter­molecular π–π stacking inter­actions between the pyridine rings [centroid–centroid distances = 3.637 (4) and 3.818 (4) Å] are present in the crystal structure

Aqua­(4,4′-dimethyl-2,2′-bipyridine-κ2 N,N′)(nitrato-κO)(nitrato-κ2 O,O′)zinc

In the title compound, [Zn(NO3)2(C12H12N2)(H2O)], the ZnII atom is six-coordinated in a distorted octa­hedral geometry by two N atoms from a chelating 4,4′-dimethyl-2,2′-bipyridine ligand, one water O atom, one O atom from a monodentate nitrate anion and two O atoms from a chelating nitrate anion. In the crystal, there are aromatic π–π contacts between the pyridine rings [centroid–centroid distances = 3.9577 (13) Å] and inter­molecular O—H⋯O and C—H⋯O hydrogen bonds

Dibromido(4,4′-dimethyl-2,2′-bipyridine-κ2 N,N′)zinc(II)

The asymmetric unit of the title compound, [ZnBr2(C12H12N2)], contains two half-mol­ecules; both are completed by crystallographic twofold axes running through the ZnII atoms which are coordinated by an N,N′-bidentate 4,4′-dimethyl-2,2′-bipyridine ligand and two Br− ions, resulting in distorted ZnN2Br2 tetra­hedral coordination geometries. In the crystal, C—H⋯Br inter­actions link the mol­ecules

Artificial neural network based classification of faults in centrifugal water pump

The detection and diagnosis of faults are of great practical significance for the safe operation of a plant. Early detection of fault can help avoid system shutdown, breakdown and even catastrophe involving human fatalities and material damage. This paper presents the design and development of ANN-based model for the fault detection of centrifugal water pump using a back-propagation learning algorithm and multi-layer perceptron neural network. The centrifugal pump conditions were considered to be healthy pump and faulty impeller and faulty seal and cavitation, which were four neurons of output layer with the aim of fault detection and identification. Features vector, which is one of the most significant parameters to design an appropriate neural network, was extracted from analysis of vibration signals in frequency domain by means of FFT method. The statistical features of vibration signals such as mean, standard deviation, variance, skewness and kurtosis were used as input to ANN. Different neural network structures are analyzed to determine the optimal neural network with regards to the number of hidden layers. The results indicate that the designed system is capable of classifying records with 100 % accuracy with one hidden layer of neurons in the neural network

Validity and reliability of the perceived benefits/barriers scale of physical activity among Iranian elderly

Abstract Background: The purpose of this study was investigating the validity and reliability of Persian version of perceived benefits/barriers of physical activity in Iranian elderly. Methods: 388 elderly subjects (up to 60 years) completed demographic characteristics questionnaire, Exercise Benefits/Barriers (EBBS) and Yale physical activity scale. Results: Persian version of EBBS showed nine components, and 31 items predicted 60.26% of variance. Cronbach's alpha for internal consistency in total and subscales was respectively 0.75, 0.91 and 0.71. As well positive and significant correlation between total benefits and its subscales and between total barriers and its subscales were found. Research results showed significant and positive correlation between physical activity and the benefits of physical activity (r=0.178, P<0.05) and significant and negative correlation between physical activity and the barriers of physical activity (r=0.249, P<0.05). Conclusion: The results showed acceptable reliability and validity of Persian version of perceived benefits/barriers of physical activity in Iranian elderly

Prevalence and correlates of psychiatric disorders in a national survey of Iranian children and adolescents

Objective: Considering the impact of rapid sociocultural, political, and economical changes on societies and families, population-based surveys of mental disorders in different communities are needed to describe the magnitude of mental health problems and their disabling effects at the individual, familial, and societal levels. Method: A population-based cross sectional survey (IRCAP project) of 30 532 children and adolescents between 6 and 18 years was conducted in all provinces of Iran using a multistage cluster sampling method. Data were collected by 250 clinical psychologists trained to use the validated Persian version of the semi-structured diagnostic interview Kiddie-Schedule for Affective Disorders and Schizophrenia-PL (K-SADS-PL). Results: In this national epidemiological survey, 6209 out of 30 532 (22.31%) were diagnosed with at least one psychiatric disorder. The anxiety disorders (14.13%) and behavioral disorders (8.3%) had the highest prevalence, while eating disorders (0.13%) and psychotic symptoms (0.26%) had the lowest. The prevalence of psychiatric disorders was significantly lower in girls (OR = 0.85; 95% CI: 0.80-0.90), in those living in the rural area (OR = 0.80; 95% CI: 0.73-0.87), in those aged 15-18 years (OR = 0.92; 95% CI: 0.86-0.99), as well as that was significantly higher in those who had a parent suffering from mental disorders (OR = 1.96; 95% CI: 1.63-2.36 for mother and OR = 1.33; 95% CI: 1.07-1.66 for father) or physical illness (OR = 1.26; 95% CI: 1.17-1.35 for mother and OR = 1.19; 95% CI: 1.10-1.28 for father). Conclusion: About one fifth of Iranian children and adolescents suffer from at least one psychiatric disorder. Therefore, we should give a greater priority to promoting mental health and public health, provide more accessible services and trainings, and reduce barriers to accessing existing services. © 2019 Tehran University of Medical Scienc