Design of Neural Predictor for Performance Analysis of Mountain Bicycles

In recent years, bicycle races, along with the crest of the high technology continues to increase. Because of this increased races, performance of bicycles, in both biological and mechanical terms, is extraordinarily important and efficient. In terms of the ratio of cargo weight a bicycle can carry to total weight, it is also a most efficient means of cargo transportation. In spite of advanced technology, there are still some problems on bicycles during working conditions and road roughness such as on the mountain from tire and mechanical parts. In this investigation, a extraordinary designed with fiber-carbon body and light bicycle is tested on mountain road conditionswith prescribed trajectory on the mountain for different elevation, speed, hearth rate, bike cadence and average temperature. The real time measured parameters are predicted with proposed two types of neural networks for approaching real time neural network predictors. The results of the proposed neural network have shown that neural predictor has superior performance to adopt the real time bicycle performance

Instaliranje opreme i mjernih postupaka pri određivanju hidrauličke vodljivosti jelovine

For a hydraulic conductor, through which liquid flows, hydraulic conductance (K, ml·s-1·MPa-1) is defined as the ratio of pressure difference at the inlet and outlet to the fluid amount passing through the hydraulic conductor in a unit time period. This property is one of the key functions of the wood, and is obtained by the flow rate (F – Flow, ml·s-1) along the wood sample divided by the pressure difference driving the flow (DP, MPa). This study aimed to establish a test setup to determine the hydraulic conductance values of Uludağ Fir (Abies bornmulleriana Mattf.). A test setup was established to measure the amount of water that flows in samples and pressure difference in characterized capillary tubes. In addition, calibration of the test apparatus is explained in detail. Fir wood samples taken from Yedigoller, which is affiliated to Kale Operation Chieftainship and Bolu Forest Regional Directorate, of 4 mm in diameter and 3 cm in length were prepared and hydraulic conductance measurements were performed, and the results are presented in this article. The installed test setup was used to obtain the following information about trees: operation of the hydraulic conduction system, the amount of needed water, seasonal effects and stress-related changes.Hidraulička vodljivost (K, ml·s-1·MPa-1) definira se kao omjer količine tekućine koja prolazi u određenom vremenu hidrauličkim vodičem i razlike tlaka na ulazu i izlazu vodiča. To je svojstvo jedna od ključnih funkcija drva, a njegova se veličina određuje mjerenjem brzine protoka (F – protok, ml·s-1) uzduž uzorka drva zbog razlika tlakova (DP, MPa). Cilj ovog istraživanja bio je instaliranje mjernog sustava za određivanje hidrauličke vodljivosti jelovine (Abies bornmulleriana Mattf.). Postavkama mjerenja utvrđena je količina vode koja teče u uzorcima jelovine i razlika tlaka u karakterističnim kapilarama. Usto je detaljno objašnjeno kalibriranje ispitne opreme. Uzorci jelovine na kojima su provedena mjerenja hidrauličke vodljivosti bili su promjera 4 mm i duljine 3 cm i uzeti su s područja Yedigoller, a rezultati ispitivanja prikazani su u ovom radu. Instaliranim mjernim sustavom dobivene su ove informacije o stablima: djelovanje sustava hidrauličke vodljivosti u drvu, količina potrebne vode, utjecaji godišnjih doba i promjene povezane sa stresom

Experimentally vibration and noise analysis of two types of washing machines with a proposed neural network predictor

WOS: 000328196600023Due to unpredictable noises, there are plenty of health problems on human. This paper is focused on neural networks (NNs) based prediction analyzer for two types drive schemes of washing machines that are direct drive and belt-pulley drive, for vibration and fault diagnosis of motors bearings. Furthermore, eight different cases including, during washing with direct drive and belt-pulley, squeezing during washing with direct drive and belt-pulley for noises and acceleration of the washing machines systems are investigated using the proposed algorithm of NNs. An Intelligent Data Acquisition (IDA), a microphone and PC are used to measure the system noise. For the case of different working conditions of the system, three types of NN are used to investigate the noise levels. The results show that NN with quick propagation algorithm gives superior performance for predicting and evaluating the noise of washing machine systems. (C) 2013 Elsevier Ltd. All rights reserved

Investigations on the effect of oil quality on gearboxes using neural network predictors

WOS: 000351282600003Purpose - The purpose of this paper was to perform an experimental investigation to analyze vibration and noise of unloaded gearbox with different oil quality. All motor-driven machinery used in the modern world can develop faults. The maintenance plans include analyzing the external relevant information of critical components, in order to evaluate its internal state. From the beginning of the twentieth century, different technologies have been used to process signals of dynamical systems. Design/methodology/approach - A proposed neural network (NN) is also employed to predict vibration parameters of the experimental test rig. Moreover, four types of oils are used for gearbox to predict reliable oil. Vibration signals extracted from rotating parts of machineries carry lot many information within them about the condition of the operating machine. Further processing of these raw vibration signatures measured at a convenient location of the machine unravels the condition of the component or the assembly under study. The experimental stand for testing an unloaded gearbox is composed by actuated direct current (DC) driving system. Findings - This paper deals with the effectiveness of wavelet-based features for fault diagnosis of a gearbox using two types of artificial neural networks (ANNs) and stress analyzed with computer-based software ANNs. The results improved that the proposed NN has superior performance to adapt experimental results. Practical implications - This paper is one such attempt to apply machine learning methods like ANN. This work deals with extraction of wavelet features from the vibration data of a gearbox system and classification of gear faults using ANNs. Originality/value - These kind of NN-based approaches are novel approaches to predict real-time vibration and acceleration parameters of unloaded gearbox with five types of oils. Also, the investigation contains new information about studied process, containing elements of novelty

Design of Neural Predictor for Performance Analysis of Mountain Bicycles

In recent years, bicycle races, along with the crest of the high technology continues to increase. Because of this increased races, performance of bicycles, in both biological and mechanical terms, is extraordinarily important and efficient. In terms of the ratio of cargo weight a bicycle can carry to total weight, it is also a most efficient means of cargo transportation. In spite of advanced technology, there are still some problems on bicycles during working conditions and road roughness such as on the mountain from tire and mechanical parts. In this investigation, a extraordinary designed with fiber-carbon body and light bicycle is tested on mountain road conditionswith prescribed trajectory on the mountain for different elevation, speed, hearth rate, bike cadence and average temperature. The real time measured parameters are predicted with proposed two types of neural networks for approaching real time neural network predictors. The results of the proposed neural network have shown that neural predictor has superior performance to adopt the real time bicycle performance

Oils quality and performance analysis of vehicle's engines using radial basis neural networks

WOS: 000271209200003Purpose - The purpose of this paper is to improve the application of neural networks on vehicle engine systems for fault detecting and analysing engine oils. Design/methodology/approach - Three types of neural networks are employed to find exact neural network predictor of vehicle engine oil performance and quality. Nevertheless, two oil types are analysed for predicting performance in the engine. These oils are used and unused oils. In experimental work, two accelerometers are located at the bottom of the car engine to measure related vibrations for analysing oil quality of both cases. Findings - The results of both computer simulation and experimental work show that the radial basis neural network predictor gives good performance at adapting different cases. Research limitations/implications - The results of the proposed neural network analyser follow the desired results of the vehicle engine's vibration variation. However, this kind of neural network scheme can be used to analyse oil quality of the car in experimental applications. Practical implications - As theoretical and practical studies are evaluated together, it is hoped that oil analysers and interested researchers will obtain significant results in this application area. Originality/value - This paper is an original contribution on vehicle oil quality analysis using a proposed artificial neural network and it should be helpful for industrial applications of vehicle oil quality analysis and fault detection

Design of artificial neural networks for rotor dynamics analysis of rotating machine systems (Retraction of vol 15, pg 573, 2005)

WOS: 000318830700016