Prediction of Motor Failure Time Using An Artificial Neural Network

Industry is constantly seeking ways to avoid corrective maintenance so as to reduce costs. Performing regular scheduled maintenance can help to mitigate this problem, but not necessarily in the most efficient way. In the context of condition-based maintenance, the main contributions of this work were to propose a methodology to treat and transform the collected data from a vibration system that simulated a motor and to build a dataset to train and test an Artificial Neural Network capable of predicting the future condition of the equipment, pointing out when a failure can happen. To achieve this goal, a device model was built to simulate typical motor vibrations, consisting of a computer cooler fan and several magnets. Measurements were made using an accelerometer, and the data were collected and processed to produce a structured dataset. The neural network training with this dataset converged quickly and stably, while the tests performed, k-fold cross-validation and model generalization, presented excellent performance. The same tests were performed with other machine learning techniques, to demonstrate the effectiveness of neural networks mainly in their generalizability. The results of the work confirm that it is possible to use neural networks to perform predictive tasks in relation to the conditions of industrial equipment. This is an important area of study that helps to support the growth of smart industries.

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Analysing 21cm signal with artificial neural network

Proceedings of the International Astronomical Union ◽

10.1017/s174392131701081x ◽

2017 ◽

Vol 12 (S333) ◽

pp. 39-42

Author(s):

Hayato Shimabukuro ◽

Benoit Semelin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Spectrum ◽

Machine Learning Techniques ◽

Inversion Method ◽

Observation Data ◽

The Neural Network ◽

Learning Techniques ◽

Artificial Neural ◽

Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

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Application of artificial neural networks to predict the deflections of reinforced concrete beams

Studia Geotechnica et Mechanica ◽

10.1515/sgem-2016-0017 ◽

2016 ◽

Vol 38 (2) ◽

pp. 37-46 ◽

Cited By ~ 4

Author(s):

Mateusz Kaczmarek ◽

Agnieszka Szymańska

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Reinforced Concrete ◽

Concrete Beams ◽

Reinforced Concrete Beams ◽

The Neural Network ◽

Eurocode 2 ◽

Artificial Neural ◽

Concrete Elements

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.

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Artificial Neural Networks

Intelligent Information Technologies ◽

10.4018/978-1-59904-941-0.ch010 ◽

2011 ◽

pp. 222-243

Author(s):

Joarder Kamruzzaman ◽

Ruhul Sarker

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Network Models ◽

Neural Network Models ◽

Network Applications ◽

The Neural Network ◽

Artificial Neural ◽

Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

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Artificial Neural Networks and Learning Techniques

Psychology and Mental Health ◽

10.4018/978-1-5225-0159-6.ch004 ◽

2016 ◽

pp. 89-112

Author(s):

Pushpendu Kar ◽

Anusua Das

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Networks ◽

Research Work ◽

The Neural Network ◽

Learning Techniques ◽

Learning Procedure ◽

Processing Component ◽

Artificial Neural ◽

Artificial Neural Network Ann

The recent craze for artificial neural networks has spread its roots towards the development of neuroscience, pattern recognition, machine learning and artificial intelligence. The theoretical neuroscience is basically converging towards the basic concept that the brain acts as a complex and decentralized computer which can perform rigorous calculations in a different approach compared to the conventional digital computers. The motivation behind the study of neural networks is due to their similarity in the structure of the human central nervous system. The elementary processing component of an Artificial Neural Network (ANN) is called as ‘Neuron'. A large number of neurons interconnected with each other mimic the biological neural network and form an ANN. Learning is an inevitable process that can be used to train an ANN. We can only transfer knowledge to the neural network by the learning procedure. This chapter presents the detailed concepts of artificial neural networks in addition to some significant aspects on the present research work.

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An Automatic Logistics System using Artificial Intelligence

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b1183.0782s419 ◽

2019 ◽

Vol 8 (2S4) ◽

pp. 924-926

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Neural Networks ◽

Artificial Neural Network ◽

Optimal Route ◽

Logistics System ◽

The Neural Network ◽

Artificial Intelligence Technology ◽

Artificial Neural ◽

Set Up

In this paper, we propose a method to utilize machine learning to automate the system of classifying and transporting large quantities of logistics. First, establish an environment similar to the task of transferring logistics to the desired destination, and set up basic rules for classification and transfer. Next, each of the logistics that need sorting and transportation is defined as one entity, and artificial intelligence is introduced so that each individual can go to an optimal route without collision between the objects to the destination. Artificial intelligence technology uses artificial neural networks and uses genetic algorithms to learn neural networks. The artificial neural network is generated by each chromosome, and it is evolved based on the most suitable artificial neural network, and a score is given to each operation to evaluate the fitness of the neural network. In conclusion, the validity of this algorithm is evaluated through the simulation of the implemented system.

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An Assessment of Machine Learning Techniques for Predicting Turbine Airfoil Component Temperatures, Using FEA Simulations for Training Data

Volume 5A: Heat Transfer ◽

10.1115/gt2019-91004 ◽

2019 ◽

Author(s):

James A. Tallman ◽

Michal Osusky ◽

Nick Magina ◽

Evan Sewall

Keyword(s):

Neural Network ◽

Machine Learning ◽

Artificial Neural Network ◽

Surrogate Model ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Techniques ◽

Learning Techniques ◽

Artificial Neural

Abstract This paper provides an assessment of three different machine learning techniques for accurately reproducing a distributed temperature prediction of a high-pressure turbine airfoil. A three-dimensional Finite Element Analysis thermal model of a cooled turbine airfoil was solved repeatedly (200 instances) for various operating point settings of the corresponding gas turbine engine. The response surface created by the repeated solutions was fed into three machine learning algorithms and surrogate model representations of the FEA model’s response were generated. The machine learning algorithms investigated were a Gaussian Process, a Boosted Decision Tree, and an Artificial Neural Network. Additionally, a simple Linear Regression surrogate model was created for comparative purposes. The Artificial Neural Network model proved to be the most successful at reproducing the FEA model over the range of operating points. The mean and standard deviation differences between the FEA and the Neural Network models were 15% and 14% of a desired accuracy threshold, respectively. The Digital Thread for Design (DT4D) was used to expedite all model execution and machine learning training. A description of DT4D is also provided.

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Artificial Neural Networks

Artificial Neural Networks in Finance and Manufacturing ◽

10.4018/978-1-59140-670-9.ch001 ◽

2006 ◽

pp. 1-27 ◽

Cited By ~ 6

Author(s):

Joarder Kamruzzaman ◽

Ruhul A. Sarker

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Network Models ◽

Neural Network Models ◽

Network Applications ◽

The Neural Network ◽

Artificial Neural ◽

Neural Network Applications

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Performance Based Machine Learning Model to Enhance Performance of Students

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.d8429.0210421 ◽

2021 ◽

Vol 10 (4) ◽

pp. 1-4

Author(s):

Bhavesh Patel

Keyword(s):

Neural Network ◽

Machine Learning ◽

Decision Making ◽

Artificial Neural Network ◽

Research Paper ◽

Machine Learning Techniques ◽

Precision Error ◽

Learning Techniques ◽

Educational Institute ◽

Artificial Neural

Machine learning techniques are used by many organizations to analyze the data and finding some meaningful hidden pattern from the data, this process is useful by an organization to take the decision making process. Various organizations used like marketing, health care, software organization and education institute etc used it in decision making. We have used machine learning techniques to enhance the performance of students. It will be ultimately used by educational institute to improve the status of educational institute. This research paper includes Naïve Bayes (NB), Logistic Regression (LR), Artificial Neural Network(ANN) and Decision Tree machine learning techniques. Performance of these models have been compared using accuracy measures parameters and ROC index. This research paper has used various parameters like academic performance and demographic information to build the model. In addition to judge the performance also used some additional parameters to measure the performance like F-measure, precision, error rate and recall. The dataset is collected using survey methodology to build the model. As a conclusion found that the Artificial Neural Network model get the best performance among all the models.

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