scholarly journals Simulation model of selective assembly of two parts with sorting by the estimated values

2018 ◽  
Vol 224 ◽  
pp. 01114 ◽  
Author(s):  
Oleg Filipovich ◽  
Vadim Kopp
Keyword(s):  
Kalman Filter ◽  
Simulation Model ◽  
Comparative Modeling ◽  
Parameter Estimates ◽  
Assembly Process ◽  
Selective Assembly ◽  
Sorting Process

The process of one-parameter selective assembly of two parts is considered. Methods for reducing the probability of sorting errors are given. It is suggested to perform the sorting process according to the values of the parameter estimates. For this estimate, a recursion algorithm (the Kalman filter) is used in each cycle. In the environment of GPSS World, a simulation model of selective acquisition and assembly of two parts for determining the parameters of the assembly process is constructed. Comparative modeling results are presented that prove the effectiveness of the algorithm for estimating parameters for selective assembly. The variants of using the algorithm and the prospects for further research are suggested.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

scholarly journals An Active Fault-Tolerant PWM Tracker for Unknown Nonlinear Stochastic Hybrid Systems: NARMAX Model and OKID-Based State-Space Self-Tuning Control

2010 ◽  
Vol 2010 ◽  
pp. 1-27 ◽  
Author(s):  
Chu-Tong Wang ◽  
Jason S. H. Tsai ◽  
Chia-Wei Chen ◽  
You Lin ◽  
Shu-Mei Guo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Space ◽  
Active Fault ◽  
Stochastic Systems ◽  
Fault Tolerant ◽  
Initial Guess ◽  
Process Time ◽  
Parameter Estimates ◽  
Unknown Parameters ◽  
Self Tuning

An active fault-tolerant pulse-width-modulated tracker using the nonlinear autoregressive moving average with exogenous inputs model-based state-space self-tuning control is proposed for continuous-time multivariable nonlinear stochastic systems with unknown system parameters, plant noises, measurement noises, and inaccessible system states. Through observer/Kalman filter identification method, a good initial guess of the unknown parameters of the chosen model is obtained so as to reduce the identification process time and enhance the system performances. Besides, by modifying the conventional self-tuning control, a fault-tolerant control scheme is also developed. For the detection of fault occurrence, a quantitative criterion is exploited by comparing the innovation process errors estimated by the Kalman filter estimation algorithm. In addition, the weighting matrix resetting technique is presented by adjusting and resetting the covariance matrix of parameter estimates to improve the parameter estimation for faulty system recovery. The technique can effectively cope with partially abrupt and/or gradual system faults and/or input failures with fault detection.

scholarly journals Simulation Model of Selective Assembly of the Conrod-Piston Group Unit of Internal Combustion Engines, Taking into Account Measurement Errors During Sorting

2021 ◽  
Vol 2096 (1) ◽  
pp. 012188
Author(s):  
O V Filipovich ◽  
A I Balakin ◽  
N A Balakina ◽  
V O Filipovich
Keyword(s):  
Simulation Model ◽  
Measurement Errors ◽  
Internal Combustion Engines ◽  
Combustion Engine ◽  
Internal Combustion ◽  
Metrological Problem ◽  
Selective Assembly ◽  
Engine Model ◽  
Piston Group ◽  
Size Groups

Abstract The process of selective assembly of the conrod-piston group of an internal combustion engine, which provides the required accuracy of the connections, is considered. The functioning of a unit consisting of three main elements is described, the scheme of the assembly set-making is shown. The simulation model of a one-parameter selective assembly of three elements has been modernized by taking into account measurement errors arising from sorting into size groups. The modeling of the assembly process of the unit for a specific engine model has been carried out. A dependence is obtained that connects the required output indicator of the technological process with the parameter of the measurement quality. This research will allow solving the metrological problem associated with the choice of the optimal accuracy of measuring equipment under technical and economic constraints.

scholarly journals Application of computer agent modeling for optimization of the assembly process

2020 ◽  
pp. 18-25
Author(s):  
І.О. Ушакова
Keyword(s):  
Simulation Model ◽  
Computer Modeling ◽  
Model Simulation ◽  
Optimal Number ◽  
Experimental Result ◽  
Computer Simulation Model ◽  
Assembly Process ◽  
Systems Simulation ◽  
The Impact ◽  
Machine Assembly

Computer modeling is a method for solving the problems of analysis or synthesis of a complex system based on the use of its computer model. Simulation, as a component of computer modeling, allows you to construct most of the possible states of the analyzed system. In this way it allows to correct the processes of assembling machines in a minimum time and with minimal costs, to foresee possible risks and to avoid unreasonable decisions regarding the organization of processes, to reduce material costs. Simulation gives the best results for modeling uncertain or probabilistic systems. Simulation, as a component of computer modeling, gives the best results for modeling systems with uncertainty or with a probabilistic nature. Mathematically calculating all possible variants of the system’s behavior is a laborious task, and using average values in the calculations gives inaccurate results. For assembly production systems, simulation modeling is used to select the optimal production organization parameters. The aim of this work is to build a computer simulation model that allows you to evaluate the impact of production factors (number of employees, operating parameters and type of assembly process) on the assembly process and substantiate the effectiveness of the model. The current assembly process of the machine was considered for modeling. The assembly process includes: two streams of verification, synchronization of request from these two streams, and a machine collection stream. The vacated line starts servicing the application that entered the system earlier than others. Such a discipline is called “earlier entered - earlier served” (FIFO - First In - First Out).s from flows, and a machine assembly flow. An optimization experiment was carried out after the construction and verification of the simulation model. The maximum value of profit was chosen as the objective function. Iterations were performed during an optimization experiment. The optimal number of employees was selected for two inspection flows and a machine assembly stream in the assembly shop. The first experimental result showed the effectiveness of the request flow synchronization method. The following optimization experiment showed the relationship between the number of workers in certain threads of the assembly process and profit. The results of using agent-based models for simulation can be used to optimize assembly processes.

scholarly journals Modeling of the two shuttle box system within the internal logistics system using simulation software

2021 ◽  
Vol 11 (1) ◽  
pp. 887-895
Author(s):  
David Strnad ◽  
Gabriel Fedorko ◽  
Patrik Ščavnický
Keyword(s):  
Simulation Model ◽  
Transport System ◽  
Production System ◽  
Simulation Software ◽  
Assembly Process ◽  
Material Flows ◽  
Assembly Lines ◽  
Logistics System ◽  
Shuttle Box ◽  
Efficiency And Effectiveness

Abstract The efficiency and effectiveness of the production system are influenced by the logistical arrangement of material flows. The smooth production, especially for assembly lines in the required quantity and at the required time (JIT or JIS systems), is currently ensured by automatically controlled towing sets (logistics trains), which consist of a tractor and several trucks. Another factor that affects the smooth production of assembly lines is the system of controlling the circulation of transport units. The number of transport units used in combination with the transport system significantly affects the efficiency of assembly workplaces. This article presents an analysis of the supply of assembly workplaces using a system of two shuttle boxes. The aim of this article is to investigate the influence of transport speed and capacity on the smoothness of the assembly process. Within the described analysis, the transport of 50,000 components is analyzed using a simulation model, while the length of the transport route is 32 km and 65 transport units per shift are used.

scholarly journals The Vehicles Positioning in Ultra-Dense 5G/V2X Radio Access Networks Using the Extended Kalman Filter

2020 ◽  
Vol 6 (4) ◽  
pp. 45-59
Author(s):  
G. Fokin ◽  
A. Vladyko
Keyword(s):  
Kalman Filter ◽  
Simulation Model ◽  
Extended Kalman Filter ◽  
Traffic Control ◽  
Access Networks ◽  
Line Of Sight ◽  
Angle Of Arrival ◽  
Radio Access Networks ◽  
Radio Access ◽  
Vehicle Positioning

This work is devoted to the study of mathematical models of vehicle positioning in ultra-dense V2X / 5G radio access networks using the extended Kalman filter. Based on the study of the probability of line-of-sight availability in the conditions of ultra-dense distribution of reference radio access stations and vehicles, as well as existing mathematical prototype positioning models, a new simulation model for constructing the trajectory of a vehicle has been developed to assess compliance with the requirements for the accuracy of coordinate assessment on the example of the scenario of priority passage of intersections. The simulation model implements the procedures for collecting primary angle and rangefinder measurements by reference stations received from the vehicle for subsequent secondary processing using the extended Kalman filter, as a result of which the vehicle trajectory is built in real time. In contrast to the existing prototype models, the simulation model developed in this work makes it possible to assess compliance with the specified requirements and other specifications depending on the current conditions of line-of-sight availability, as well as the accuracy of collecting primary angle measurements determined by the antenna array installed on the support device. The results of simulation are consistent with the known estimates of prototype models and confirm the possibility of achieving an accuracy of up to 1 m for a traffic control scenario with an error in determining the angle of arrival of a signal of 2 °.

The Selection of Process Noise Covariance Q and Measurement Noise Covariance R on Kalman Filter

2014 ◽  
Vol 513-517 ◽  
pp. 4342-4345 ◽  
Author(s):  
Chao Yi Wei ◽  
Shu Jian Ye ◽  
Xu Guang Li ◽  
Mei Zhi Xie ◽  
Feng Yan Yi
Keyword(s):  
Kalman Filter ◽  
Simulation Model ◽  
Dynamic Model ◽  
Measurement Noise ◽  
State Estimator ◽  
Process Noise ◽  
Measurement Noise Covariance ◽  
High Measurement ◽  
Noise Covariance ◽  
Selection Of

At first, a seven degree of freedom dynamic model of tractor semi-trailer was established, and a simulation model with the Matlab/Simulink software was established too. Based on the model, a state estimator based on Kalman theory was designed, using easily measured parameters to estimate the parameters that are difficultly measured or have high measurement cost, after that, compared estimated values and simulation measurements and analyzed the influence with changes of process noise covariance Q and measurement noise covariance R.

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