Data Mining and Mathematical Model Development

2016 ◽  
pp. 381-388
Author(s):  
Masahiro Sugimoto ◽  
Masahiro Takada
Keyword(s):  
Data Mining ◽  
Mathematical Model ◽  
Model Development

Mathematical Model of Flat Surface Machining Inaccuracies due to Elastic Strains of Technological System

2018 ◽  
pp. 1-14 ◽  
Author(s):  
I. I. Kravchenko
Keyword(s):  
Mathematical Model ◽  
Vector Field ◽  
Model Development ◽  
Technological System ◽  
Mutual Arrangement ◽  
Real Surface ◽  
Main Bearing ◽  
Flat Surfaces ◽  
Internal Surfaces ◽  
Elastic Strains

The paper considers the mathematical model development technique to build a vector field of the shape deviations when machining flat surfaces of shell parts on multi-operational machines under conditions of anisotropic rigidity in technological system (TS). The technological system has an anisotropic rigidity, as its elastic strains do not obey the accepted concepts, i.e. the rigidity towards the coordinate axes of the machine is the same, and they occur only towards the external force. The record shows that the diagrams of elastic strains of machine units are substantially different from the circumference. The issues to ensure the specified accuracy require that there should be mathematical models describing kinematic models and physical processes of mechanical machining under conditions of the specific TS. There are such models for external and internal surfaces of rotation [2,3], which are successfully implemented in practice. Flat surfaces (FS) of shell parts (SP) are both assembly and processing datum surfaces. Therefore, on them special stipulations are made regarding deviations of shape and mutual arrangement. The axes of the main bearing holes are coordinated with respect to them. The joints that ensure leak tightness and distributed load on the product part are closed on these surfaces. The paper deals with the analytical construction of the vector field F, which describes with appropriate approximation the real surface obtained as a result of modeling the process of machining flat surfaces (MFS) through face milling under conditions of anisotropic properties.

Experimental and Theoretical Investigation of Performance of a Solar Chimney Model Part II: Model Development and Validation

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
Ibrahim A Abuashe ◽  
Bashir H Arebi ◽  
Essaied M Shuia
Keyword(s):  
Mathematical Model ◽  
Power Output ◽  
Model Development ◽  
Geometrical Parameters ◽  
Balance Equations ◽  
Solar Chimney ◽  
And Performance ◽  
Development And Validation ◽  
The Mathematical Model ◽  
Good Agreement

A mathematical model based on the momentum, continuity and energy balance equations was developed to simulate the behavior of the air flow inside the solar chimney system. The model can estimate the power output and performance of solar chimney systems. The developed mathematical model is validated by the experimental data that were collected from small pilot solar chimney; (experiment was presented in part I). Good agreement was obtained between the experimental results and that from the mathematical model. The model can be used to analyze the solar chimney systems and to determine the effect of geometrical parameters such as chimney height and collector diameter on the power output and the efficiency of the system

Extended validation of a ground-based three-axis spacecraft simulator model

2020 ◽  
pp. 095441002093896
Author(s):  
H Sh Ousaloo ◽  
Gh Sharifi ◽  
B Akbarinia
Keyword(s):  
Mathematical Model ◽  
Attitude Control ◽  
Aerodynamic Drag ◽  
Center Of Mass ◽  
Model Development ◽  
Optimization Method ◽  
Experimental Results ◽  
Spacecraft Dynamics ◽  
Detailed Model ◽  
Mass Properties

The ground-based spacecraft dynamics simulator plays an important role in the implementation and validation of attitude control scenarios before a mission. The development of a comprehensive mathematical model of the platform is one of the indispensable and challenging steps during the control design process. A precise mathematical model should include mass properties, disturbances forces, mathematical models of actuators and uncertainties. This paper presents an approach for synthesizing a set of trajectories scenarios to estimate the platform inertia tensor, center of mass and aerodynamic drag coefficients. Reaction wheel drag torque is also estimated for having better performance. In order to verify the estimation techniques, a dynamics model of the satellite simulator using MATLAB software was developed, and the problem reduces to a parameter estimation problem to match the experimental results obtained from the simulator using a classical Lenevnberg-Marquardt optimization method. The process of parameter identification and mathematical model development has implemented on a three-axis spherical satellite simulator using air bearing, and several experiments are performed to validate the results. For validation of the simulator model, the model and experimental results must be carefully matched. The experimental results demonstrate that step-by-step implementation of this scenario leads to a detailed model of the platform which can be employed to design and develop control algorithms.

scholarly journals Developing Data Mining-Based Prognostic Models for CF-18 Aircraft

2010 ◽  
Author(s):  
Marvin Zaluski ◽  
Sylvain Le´tourneau ◽  
Jeff Bird ◽  
Chunsheng Yang
Keyword(s):  
Data Mining ◽  
Model Building ◽  
Health Management ◽  
Model Development ◽  
Data Gathering ◽  
Software Tool ◽  
Prognostic Models ◽  
Canadian Forces ◽  
Fusion Data ◽  
Operating Organization

The CF-18 aircraft is a complex system for which a variety of data are systematically being recorded: operational flight data from sensors and Built-In Test Equipment (BITE) and maintenance activities recorded by personnel. These data resources are stored and used within the operating organization but new analytical and statistical techniques and tools are being developed that could be applied to these data to benefit the organization. This paper investigates the utility of readily available CF-18 data to develop data mining-based models for prognostics and health management (PHM) systems. We introduce a generic data mining methodology developed to build prognostic models from operational and maintenance data and elaborate on challenges specific to the use of CF-18 data from the Canadian Forces. We focus on a number of key data mining tasks including: data gathering, information fusion, data pre-processing, model building, and evaluation. The solutions developed to address these tasks are described. A software tool developed to automate the model development process is also presented. Finally, the paper discusses preliminary results on the creation of models to predict F404 No. 4 Bearing and MFC (Main Fuel Control) failures on the CF-18.

scholarly journals Prediction of maintenance cost for road construction equipment: a case study

2016 ◽  
Vol 43 (5) ◽  
pp. 480-492 ◽  
Author(s):  
Sharif Mohammad Bayzid ◽  
Yasser Mohamed ◽  
Maria Al-Hussein
Keyword(s):  
Data Mining ◽  
Model Development ◽  
Road Construction ◽  
Study Data ◽  
Construction Equipment ◽  
Maintenance Cost ◽  
Cost Models ◽  
Construction Operations ◽  
Development Data

Equipment maintenance cost is significant in construction operations budgets. This study proposes a systematic approach to predict maintenance cost of road construction equipment. First, maintenance cost data over more than 10 years was collected from a partner company’s equipment management information system. Data was cleaned and analyzed to obtain a general understanding of maintenance costs trends. Next, traditional cumulative cost models and alternative data mining models were generated to predict maintenance cost based on available equipment and operation attributes. Data mining models were evaluated and validated using portions of the collected data that have not been used in model development. Data collection, analyses, modeling, and validation steps are discussed. The paper also presents the performance of different model types. Based on the case study data, regression model trees performed better than other model types with equipment work hours being the most significant parameter for predicting maintenance cost.

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