Requirements for current trains to be increasingly available have created the need to develop systems that can predict the quality of both trains and infrastructure components. The paper presents a new approach to the detection of rail truck irregularities, based on the measurements of bearing box acceleration during the operation of rail vehicles. The proposed procedure is based on an inverse problem solution, estimating track irregularities from measured acceleration of the applied model of vehicle dynamics. The simulation study of the proposed method, as well as its implementation, is presented. The method has been successfully applied for the identification of rail irregularities on a typical Polish railroad and vehicle.
The aim of this paper was to develop an equivalent numerical model of a disc valve system used in automotive shock absorbers. Numerical model allows for simulation of fluid flow phenomena through the disc valve system. Paper presents a modeling method and technique of fluid-structure interaction simulations.
This paper considers a method for reconstructing sine-wave excitation using an inverse, parametric data-driven model developed to monitor the magnitude and frequency of the load to which a structure is exposed. The main goal of the paper is to discuss, and experimentally verify, the applicability of different SISO and MIMO structures of parametric models, such as the ARX, ARARX, OE, BJ, and PEM models described by the linear system identification theory. Experimental validation tests were conducted on a set-up consisting of a metal frame equipped with two electrodynamic exciters, several acceleration transducers and a data-acquisition system. The fidelity and adequacy of various model structures was judged in time and frequency domains based on stability diagrams, FPE and AIC criteria, as well as on the magnitude of the relative error between the measured and reconstructed load. The experimental test results showed that, in the case of measurement data moderately corrupted by noise, the ARX and OE models provide better accuracy of inversion than advanced models, such as ARARAX, BJ or PEM. This leads to the conclusion that increasing the complexity of a model structure does not result in better reconstruction of the load. Therefore, less complicated structures are acceptable for practical applications and, in fact, should be favored.
Industry 4.0 relies on the adoption of digital technologies to gather data in real time and to analyse it, providing useful information to the manufacturing system. In this paper, what solutions modern production plants that are aspiring towards compliance with philosophy of the Industry 4.0 have to adopt, monitor and analyse the vibration data of the manufacturing systems using existing process and tool monitoring solutions. In addition, detailed explanation of vibration level reading in order to increase the protection of the production sources (machines, devices etc.) against human errors and malfunctions in terms of Total Quality Management (TQM) and Total Productive Maintenance (TPM) with the concept’s levels TPM1(operator level) and TPM2(periodic conditional reviews etc.) will be giving with a Montronix system’s integration on a CNC milling machine. Besides, optimization and monitoring function of production process will be demonstrated with related graphs and tables with values of different scenarios.
The first-principle modeling of a feedwater heater operating in a coal-fired power unit is presented, along with a theoretical discussion concerning its structural simplifications, parameter estimation, and dynamical validation. The model is a part of the component library of modeling environments, called the Virtual Power Plant (VPP). The main purpose of the VPP is simulation of power generation installations intended for early warning diagnostic applications. The model was developed in the Matlab/Simulink package. There are two common problems associated with the modeling of dynamic systems. If an analytical model is chosen, it is very costly to determine all model parameters and that often prevents this approach from being used. If a data model is chosen, one does not have a clear interpretation of the model parameters. The paper uses the so-called grey-box approach, which combines first-principle and data-driven models. The model is represented by nonlinear state-space equations with geometrical and physical parameters deduced from the available documentation of a feedwater heater, as well as adjustable phenomenological parameters (i.e., heat transfer coefficients) that are estimated from measurement data. The paper presents the background of the method, its implementation in the Matlab/Simulink environment, the results of parameter estimation, and a discussion concerning the accuracy of the method.
This work aims to present and discuss the architecture and application aspects of the Virtual Power Plant (VPP) environment, a tool created for the purpose of the modeling, simulation, data management, and visualization of power plant systems. The VPP environment is based on a general-purpose simulation package that provides a framework for incorporating a broad variety of models, ranging from real-time models to detailed models that require off-line execution. The VPP, as a tool, opens up the possibility to develop, test, and validate the feasibility of new software and hardware components within simulated operational scenarios. An original approach to tuning a first-principle model using the example of a feedwater heater by means of a parameter estimation technique is presented, along with a case study of modeling a 225 MW coal-fired power unit. The objectives are as follows: (i) develop a tuning method for this model; (ii) propose key indicators of feedwater heater performance using a model-based approach; and finally (iii) automate the calculation process of the indicators.
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