A gear system transmits power by means of meshing gear teeth and is conceptually simple and effective in power transmission. Thus typical applications include electric utilities, ships, helicopters, and many other industrial applications. Monitoring the condition of large gearboxes in industries has attracted increasing interest in the recent years owing to the need for decreasing the downtime on production machinery and for reducing the extent of secondary damage caused by failures. This paper addresses the development of a condition monitoring procedure for a gear transmission system using artificial neural networks (ANNs) and support vector machines (SVMs). Seven conditions of the gear were investigated: healthy gear and gear with six stages of depthwise wear simulated on the gear tooth. The features extracted from the measured vibration and sound signals were mean, root mean square (rms), variance, skewness, and kurtosis, which are known to be sensitive to different degrees of faults in rotating machine elements. These characteristics were used as an input features to ANN and SVM. The results show that the multilayer feed forward neural network and multiclass support vector machines can be effectively used in the diagnosis of various gear faults.
In this study, an artificial neural network application was performed to tell if 18 plates of the same material in different shapes and sizes were cracked or not. The cracks in the cracked plates were of different depth and sizes and were non-identical deformations. This ANN model was developed to detect whether the plates under test are cracked or not, when four plates have been selected randomly from among a total of 18 ones. The ANN model used in the study is a model uniquely tailored for this study, but it can be applied to all systems by changing the weight values and without changing the architecture of the model. The developed model was tested using experimental data conducted with 18 plates and the results obtained mainly correspond to this particular case. But the algorithm can be easily generalized for an arbitrary number of items.
The computational intelligence tool has major contribution to analyse the properties of materials without much experimentation. The B4C particles are used to improve the quality of the strength of materials. With respect to the percentage of these particles used in the micro and nano, composites may fix the mechanical properties. The different combinations of input parameters determine the characteristics of raw materials. The load, content of B4C particles with 0%, 2%, 4%, 6%, 8% and 10% will determine the wear behaviour like CoF, wear rate etc. The properties of materials like stress, strain, % of elongation and impact energy are studied. The temperature based CoF and wear rate is analysed. The temperature may vary between 30°C, 100°C and 200°C. In addition, the CoF and wear rate of materials are predicted with respect to load, weight % of B4C and nano hexagonal boron nitride %. The intelligent tools like Neural Networks (BPNN, RBNN, FL and Decision tree) are applied to analyse these characteristics of micro / nano composites with the inclusion of B4C particles and nano hBN % without physically conducting the experiments in the Lab. The material properties will be classified with respect to the range of input parameters using the computational model.
Anne of Green Gables by L.M. Montgomery (1908) enjoys unprecedented popularity in Poland and has played a considerable role in the shaping of modern Polish culture. As many as fourteen different translations of the fi rst volume of the series have been published; moreover, there exists an active Polish fandom of Montgomery’s oeuvre. The authors of this article briefly discuss the cultural and social aspects of this phenomenon which was triggered off in 1911 by Rozalia Bernsteinowa’s Polish translation of Anne of Green Gables. Her translation, still regarded as the canonical text, greatly altered the realities of the original novel. As a result, in Poland Anne of Green Gables has the status of a children’s classic, whereas readers in the English-speaking world have always treated it as an example of the sub-genre of juvenile college (school) girls’ literature. The identity of the Polish translator of L.M. Montgomery’s book remains a mystery, and even the name on the cover may well be pen name (though, at any rate, it strongly suggests that she must have belonged to the Jewish intelligentsia of the early 20th century). What we do know about her for fact is that she was a translator of German, Danish, Swedish, Norwegian and English literature. Comparing Rozalia Bernsteinowa’s Polish text to its English original has been a subject of many Polish B.A. and M.A. theses. The argument of this article is that her key reference for was not the English text, but that of the fi rst Swedish translation by Karin Jensen named Anne på Grönkulla (1909).
Effects of infrared power output and sample mass on drying behaviour, colour parameters, ascorbic acid degradation, rehydration characteristics and some sensory scores of spinach leaves were investigated. Within both of the range of the infrared power outputs, 300–500 W, and sample amounts, 15–60 g, moisture content of the leaves was reduced from 6.0 to 0.1±(0.01) kg water/kg dry base value. It was recorded that drying times of the spinach leaves varied between 3.5–10 min for constant sample amount, and 4–16.5 min for constant power output. Experimental drying data obtained were successfully investigated by using artificial neural network methodology. Some changes were recorded in the quality parameters of the dried leaves, and acceptable sensory scores for the dried leaves were observed in all of the experimental conditions.
Casting is the most widely used manufacturing technique. Furan No-bake mould system is very widely accepted in competitive foundry industries due to its excellent characteristics of producing heavy and extremely difficult castings. These castings have excellent surface finish and high dimensional stability. Self setting and high dimensional stability are the key characteristics of FNB mould system which leads to reduce production cycle time for foundry industries which will ultimately save machining cost, labour cost and energy. Compressive strength is the main aspect of furan no bake mould, which can be improved by analyzing the effect of various parameters on it. ANN is a useful technique for determining the relation of various parameters like Grain Fineness Number, Loss on Ignition, pH, % resin and temperature of sand with compressive strength of the FNB mould. Matlab version: R2015a version 8.3 software with ANN tool box can be used to gain output of relation. This paper deals with the representation of relationship of various parameters affecting on the compressive strength of FNB mould
In this paper, dynamic response improvement of the grid connected hybrid system comprising of the wind power generation system (WPGS) and the photovoltaic (PV) are investigated under some critical circumstances. In order to maximize the output of solar arrays, a maximum power point tracking (MPPT) technique is presented. In this paper, an intelligent control technique using the artificial neural network (ANN) and the genetic algorithm (GA) are proposed to control the MPPT for a PV system under varying irradiation and temperature conditions. The ANN-GA control method is compared with the perturb and observe (P&O), the incremental conductance (IC) and the fuzzy logic methods. In other words, the data is optimized by GA and then, these optimum values are used in ANN. The results are indicated the ANN-GA is better and more reliable method in comparison with the conventional algorithms. The allocation of a pitch angle strategy based on the fuzzy logic controller (FLC) and comparison with conventional PI controller in high rated wind speed areas are carried out. Moreover, the pitch angle based on FLC with the wind speed and active power as the inputs can have faster response that lead to smoother power curves, improving the dynamic performance of the wind turbine and prevent the mechanical fatigues of the generator.
Due to an increasing amount of music being made available in digital form in the Internet, an automatic organization of music is sought. The paper presents an approach to graphical representation of mood of songs based on Self-Organizing Maps. Parameters describing mood of music are proposed and calculated and then analyzed employing correlation with mood dimensions based on the Multidimensional Scaling. A map is created in which music excerpts with similar mood are organized next to each other on the two-dimensional display.