A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks

2020 ◽  
pp. 39-58
Author(s):  
Tuğba Özge Onur ◽  
Yusuf Aytaç Onur
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Testing ◽  
Steel Wire ◽  
Experimental Studies ◽  
Fatigue Lifetime ◽  
Bending Fatigue ◽  
Wire Ropes ◽  
Significant Case ◽  
Artificial Neural

Steel wire ropes are frequently subjected to dynamic reciprocal bending movement over sheaves or drums in cranes, elevators, mine hoists, and aerial ropeways. This kind of movement initiates fatigue damage on the ropes. It is a quite significant case to know bending cycles to failure of rope in service which is also known as bending over sheave fatigue lifetime. It helps to take precaution in the plant in advance and eliminate catastrophic accidents due to usage of rope when allowable bending cycles are exceeded. To determine bending fatigue lifetime of ropes, experimental studies are conducted. However, bending over sheave fatigue testing in laboratory environments require high initial preparation cost and longer time to finalize the experiments. Due to those reasons, this chapter focuses on a novel prediction perspective to the bending over sheave fatigue lifetime of steel wire ropes by means of artificial neural networks.

A Novel Prediction Perspective to the Bending Over Sheave Fatigue Lifetime of Steel Wire Ropes by Means of Artificial Neural Networks

2022 ◽  
pp. 648-667
Author(s):  
Tuğba Özge Onur ◽  
Yusuf Aytaç Onur
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fatigue Testing ◽  
Steel Wire ◽  
Experimental Studies ◽  
Fatigue Lifetime ◽  
Wire Ropes ◽  
Long Time ◽  
Significant Case ◽  
Artificial Neural

Steel wire ropes are frequently subjected to dynamic reciprocal bending movement over sheaves or drums in cranes, elevators, mine hoists, and aerial ropeways. This kind of movement initiates fatigue damage on the ropes. It is a quite significant case to know bending cycles to failure of rope in service, which is also known as bending over sheave fatigue lifetime. It helps to take precautions in the plant in advance and eliminate catastrophic accidents due to the usage of rope when allowable bending cycles are exceeded. To determine the bending fatigue lifetime of ropes, experimental studies are conducted. However, bending over sheave fatigue testing in laboratory environments require high initial preparation cost and a long time to finalize the experiments. Due to those reasons, this chapter focuses on a novel prediction perspective to the bending over sheave fatigue lifetime of steel wire ropes by means of artificial neural networks.

scholarly journals A method for mobile device positioning using a sensor network of BLE beacons, approximation of the RSSI value and artificial neural networks

2021 ◽  
Vol 45 (2) ◽  
pp. 277-285
Author(s):  
A.V. Astafiev ◽  
D.V. Titov ◽  
A.L. Zhiznyakov ◽  
A.A. Demidov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sensor Network ◽  
Mobile Device ◽  
Experimental Studies ◽  
Test Sample ◽  
Small Scale ◽  
Approximation Function ◽  
Artificial Neural

The paper considers the development of a method for positioning a mobile device using a sensor network of BLE-beacons, the approximation of RSSI values and artificial neural networks. The aim of the work is to develop a method for positioning small-scale industrial mechanization equipment for building unmanned systems for product movement tracking. The work is divided into four main parts: data synthesis, signal filtering, selection of BLE beacons, translation of the RSSI values into a distance, and multilateration. A simplified Kalman filter is proposed for filtering the input signal to suppress Gaussian noise. A description of two approaches to translating the RSSI value into a distance is given: an exponential approximation function with a coefficient of determination of 0.6994 and an artificial feedforward neural network. A comparison of the results of these approaches is carried out on several test samples: a training one, a test sample at a known distance (0–50 meters) and a test sample at an unknown distance (60–100 meters). The artificial neural network is shown to perform better in all experiments, except for the test sample at a known distance (0–50 meters), for which the r.m.s. error is higher by 0.02 m 2 than that for the approximation function, which can be neglected. An algorithm for positioning a mobile device based on the multilateration method is proposed. Experimental studies of the developed method have shown that the positioning error does not exceed 0.9 meters in a 5×5.5 m room under monitoring. The positioning accuracy of a mobile device using the proposed method in the experiment is 40.9 % higher. Experimental studies are also conducted in a 58.4×4.5 m room, showing more accurate results compared to similar studies.

Performance evaluation of artificial neural networks for identification of failure modes in composite plates

2021 ◽  
Vol 63 (6) ◽  
pp. 565-570
Author(s):  
Serkan Balli ◽  
Faruk Sen
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Failure Modes ◽  
Experimental Studies ◽  
Composite Plates ◽  
Geometric Parameters ◽  
Sandwich Composite ◽  
Geometrical Parameters ◽  
Training Algorithms ◽  
Artificial Neural

Abstract The aim of this work is to identify failure modes of double pinned sandwich composite plates by using artificial neural networks learning algorithms and then analyze their accuracies for identification. Mechanically pinned specimens with two serial pins/bolts for sandwich composite plates were used for recognition of failure modes which were obtained in previous experimental studies. In addition, the empirical data of the preceding work was determined with various geometric parameters for various applied preload moments. In this study, these geometric parameters and fastened/bolted joint forms were used for training by artificial neural networks. Consequently, ten different backpropagation training algorithms of artificial neural network were applied for classification by using one hundred data values containing three geometrical parameters. According to obtained results, it was seen that the Levenberg-Marquardt backpropagation training algorithm was the most successful algorithm with 93 % accuracy rate and it was appropriate for modeling of this problem. Additionally, performances of all backpropagation training algorithms were discussed taking into account accuracy and error ratios.

The influence of rotation speed on the bending fatigue lifetime of steel wire ropes

2010 ◽  
Vol 225 (3) ◽  
pp. 520-525 ◽  
Author(s):  
Y A Onur ◽  
C E İmrak
Keyword(s):  
Rotation Speed ◽  
Steel Wire ◽  
Experimental Tests ◽  
Wire Rope ◽  
Experimental Investigations ◽  
Thermal Camera ◽  
Fatigue Lifetime ◽  
Bending Fatigue ◽  
Wire Ropes ◽  
Different Diameters

This article presents experimental investigations to determine the influence of rotation speed on the bending fatigue lifetime of rotation-resistant rope and non-rotation-resistant rope. Heat generated by the rotation speed on steel wire rope samples has been measured by a thermal camera. Two sheaves with different diameters have been used to obtain the effect of sheave diameters on the heat alterations and bending fatigue lifetime. Two experimental tests have been conducted to determine the effect of insufficient lubrication on the bending fatigue lifetime. The results indicate that rotation speed affects the steel wire rope lifetime subjected to bending fatigue.

Optimum Mixture Design of Bottom Ash Based Alkali-Activated Mortar Using Artificial Neural Networks

2010 ◽  
Vol 452-453 ◽  
pp. 733-736
Author(s):  
Su Tae Kang ◽  
Hyun Jin Kang ◽  
Gum Sung Ryu ◽  
Gyung Taek Koh ◽  
Jang Hwa Lee
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Sodium Hydroxide ◽  
Sodium Silicate ◽  
Experimental Studies ◽  
Bottom Ash ◽  
Mixture Design ◽  
Curing Temperature ◽  
Artificial Neural ◽  
Alkali Activated

Bottom ash based alkali-activated mortar is prepared by incorporating sodium hydroxide and sodium silicate with some additional water if needed, and is activated with temperature curing. This research was conducted to derive an optimum mixture design of the bottom ash based alkali-activated mortar. The experimental studies were first performed to estimate the effect of the added water content, alkali activator to bottom ash ratio, sodium silicate to sodium hydroxide ratio as well as curing temperature on workability and strength. In order to optimize the mix proportion, based on the experimental results, artificial neural networks were introduced.

The Modeling of Interval-Valued Time Series: A Method Based on Fuzzy Set Theory and Artificial Neural Networks

2019 ◽  
Vol 18 (01) ◽  
pp. 1950002
Author(s):  
Wei Zhou ◽  
Dan Shan ◽  
Jianhua Yang ◽  
Wei Lu
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Experimental Studies ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Interval Valued

Interval-valued time series (ITS) are interval-valued data that are collected in chronological order. The modeling of ITS is an ongoing issue in domain of time series analysis. This paper presents a new modeling method of ITS based on the synergy of fuzzy set theory and artificial neural networks. The proposed method involves the construction of collection of fuzzy sets describing characteristics of amplitude of ITS, the expression and reconstruction mechanism of ITS and the emergence of model of ITS based on artificial neural network (ANN). The resulting model of ITS not only supports the linguistic output but also the numeric output in interval format. A series of experimental studies is reported for two publicly available financial datasets showing different dynamic characteristics. Experimental results clearly show that the constructed ITS model has the better performance on the linguistic and numeric level.

Prediction of PAHs Emitted from Marine Diesel Engine Using Artificial Neural Networks Combining Genetic Algorithms

2014 ◽  
Vol 599-601 ◽  
pp. 1233-1236
Author(s):  
Ming Yu Wang ◽  
Shao Jun Zhang ◽  
Xiao Zhang
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Artificial Neural Networks ◽  
Diesel Engine ◽  
Network Performance ◽  
Experimental Studies ◽  
Marine Diesel Engine ◽  
Marine Diesel ◽  
Electronic Parameters ◽  
Artificial Neural

Experimental studies on operating a marine diesel engine to determine the performance map under different working conditions need to consume a lot of money and labor. To solve this problem, a mathematical model based on Artificial Neural Networks (ANNs) combined genetic algorithms (GA) to predicate the performance emissions of the marine diesel engine is firstly reported in this paper. The predicted result showed that the network performance is sufficient for all target emission outputs. The input layer without transfer function consisted of 11 neurons is used, and output layer predicted 16 polycyclic aromatic hydrocarbons (PAHs). Electronic parameters such as VIC, SOI, CRP, NUN, VEO and VEC have influences on the PAHs emissions. The actual data obtained from the diesel is well agreed with the predicted data. The usage of ANNs is highly recommended to predict engine emissions instead of having to undertake complex and time-consuming experimental studies.

scholarly journals Mathematical Modeling of Photovoltaic Properties of Nipc/P-Si (Organic/Inorganic) Heterojunction by Using Artificial Neural Networks Model

2020 ◽  
Vol 17 ◽  
pp. 306-321
Author(s):  
R. A. Mohamed ◽  
Mahmoud. Y. El-Bakry ◽  
D. M. Habashy ◽  
E. H. Aamer
Keyword(s):  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Experimental Studies ◽  
Back Propagation ◽  
Ann Model ◽  
Photovoltaic Properties ◽  
Nickel Phthalocyanine ◽  
Artificial Neural ◽  
Model Training

In this research, the artificial neural network (ANN) and resilient back propagation (R-prop) training algorithm are utilized to model the photovoltaic properties of Nickel–phthalocyanine (NiPc/p-Si) heterojunction. The experimental data are extracted from experimental studies. Experimental data are utilized as inputs in the ANN model. Training of different structures of the ANN is processed to approach the minimum value of error. Eight artificial neural networks are trained to get a better mean square error (MSE) and best execution for the networks. The ANN performances are also investigated and their values are very small (MSE < 10-3). The simulation results of the current-voltage characteristics of NiPc films are produced and provided excellent matching with the corresponding experimental data. Utilization of ANN model for predictions is also processed and gives accurate results.  The equation which describes the relation between the inputs and outputs is obtained. The high accuracy of the ANN model has appeared in the major guessing power and the ability of generalization depending on the obtained equations.

Social Spider Algorithm for Training Artificial Neural Networks

2021 ◽  
pp. 1100-1119
Author(s):  
Burak Gülmez ◽  
Sinem Kulluk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Heuristic Algorithms ◽  
Experimental Studies ◽  
Benchmark Problems ◽  
Real World Data ◽  
Social Spider ◽  
Local Optima ◽  
Artificial Neural

Artificial neural networks (ANNs) are one of the most widely used techniques for generalization, classification, and optimization. ANNs are inspired from the human brain and perform some abilities automatically like learning new information and making new inferences. Back-propagation (BP) is the most common algorithm for training ANNs. But the processing of the BP algorithm is too slow, and it can be trapped into local optima. The meta-heuristic algorithms overcome these drawbacks and are frequently used in training ANNs. In this study, a new generation meta-heuristic, the Social Spider (SS) algorithm, is adapted for training ANNs. The performance of the algorithm is compared with conventional and meta-heuristic algorithms on classification benchmark problems in the literature. The algorithm is also applied to real-world data in order to predict the production of a factory in Kayseri and compared with some regression-based algorithms and ANNs models. The obtained results and comparisons on classification benchmark datasets have shown that the SS algorithm is a competitive algorithm for training ANNs. On the real-world production dataset, the SS algorithm has outperformed all compared algorithms. As a result of experimental studies, the SS algorithm is highly capable for training ANNs and can be used for both classification and regression.

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