Jangbom Chai

Dynamic analysis of a pantograph–catenary system using absolute nodal coordinates

The dynamic interaction between the catenary and the pantographs of high-speed trains is a very important factor that affects the stable electric power supply. In order to design a reliable current collection system, a multibody simulation model can provide an efficient and economical method to analyze the dynamic behavior of the catenary and pantograph. In this article, a dynamic analysis method for a pantograph–catenary system for a high-speed train is presented, employing absolute nodal coordinates and rigid body reference coordinates. The highly flexible catenary is modeled using a nonlinear continuous beam element, which is based on an absolute nodal coordinate formulation. The pantograph is modeled as a rigid multibody system. The analysis results are compared with experimental data obtained from a running high-speed train. In addition, using a derived system equation of motion, the calculation method for the dynamic stress in the catenary conductor is presented. This study may have significance in providing an example that a structural and multibody dynamics model can be unified into one numerical system.

Adaptive neural controller for space robot system with an attitude controlled base

In this paper, an adaptive neural network-based controller is proposed for a space robot system with an attitude controlled base without joint acceleration measurements and in the presence of parametric uncertainties and external disturbances. Based on the dynamic model, a neural network-based controller is proposed that achieves the required tracking effectively. A feedforward neural network is employed to learn the existing unknown dynamics of robot system. The uniform ultimate boundedness of all signals in the closed-loop system is guaranteed by the Lyapunov approach. It is shown that the neural network can cope with the unknown nonlinearities through the adaptive learning process and requires no preliminary off learning. Finally, simulation study has been performed to evaluate the controller performance.

The noise reduction of a DC motor using multi-body dynamics

The DC motor of a vehicle may cause noise and vibration due to high-speed revolution, which can make a driver feel uncomfortable There have been various studies attempting to solve these problems, mostly focusing on the causes of noise and vibration and a means of preventing them The CAE methodology is more efficient than a real test for the purpose of looking for various design parameters to reduce the noise and vibration of the DC motor In this study, a design process for reducing brush noise is presented with the use of a computer model, which is made by using a multi-body dynamics program (DADS) The design parameters to reduce the brush noise and vibration were proposed using a computer model They were used to reduce the noise and vibration of the DC motor and verified by the test tesults of the fan DC motor in the vehicle This method may be applicable to various DC motors

Non-invasive diagnostics of motor-operated valves

This paper is concerned with the development of data analysis methods to be used in a model-based approach to the online monitoring and diagnosis of motor-operated valves (MOVs). The technique to be utilized will include the extensive integration of mechanical and electrical measurements and signal processing. A torque estimator is developed and tested to obtain electric torque of the induction motors which are attached to the MOV system. Transfer functions between the actuator housing vibration and gear meshing force are measured and inverse filters are designed to recover the source waveforms. To monitor the operating condition, amplitudes and frequencies of the recovered signal are examined. Various frequency demodulation techniques are studied to find the most robust method. An adaptive linear phase bandpass filter is developed to improve signal-to-noise ratio and to track the important frequency components for diagnostic purposes during the operation. Finally, stand-alone valve experiments are carried out to validate the developed estimation scheme. Faults are introduced into the experiment set-up by placing obstructions in the path of the closing valve. The results of this study will be directly applied in the monitoring of MOVs and the methods developed can be applied to other diagnostic system as well. An additional benefit of this study will be a reduction in reliability problems associated with torque switch failures in existing MOVs.

Enhancing precision performance of trajectory tracking controller for robot manipulators using RBFNN and adaptive bound

In this paper the design issues of trajectory tracking controller for robot manipulators are considered. The performance of classical model based controllers is reduced due to the presence of inherently existing uncertainties in the dynamic model of the robot manipulator. An intermediate approach between model based controllers and neural network based controllers is adopted to enhance the precision of trajectory tracking. The performance of the model based controller is enhanced by adding an RBF neural network and an adaptive bound part. The controller is able to learn the existing structured and unstructured uncertainties in the system in online manner. The RBF network learns the unknown part of the robot dynamics with no requirement of the offline training. The adaptive bound part is used to estimate the unknown bounds on unstructured uncertainties and neural network reconstruction error. The overall system is proved to be asymptotically stable. Finally, the numerical simulation results are produced with various controllers and the effectiveness of the proposed controller is shown in a comparative study for the case of a Microbot type robot Manipulator.

Nonlinear magnetic method for estimation of wall thinning in carbon steel pipe

This paper proposes a nonlinear magnetic method to estimate the thickness of carbon steel pipe used in the secondary steam cycle of nuclear power plants. The principle of the proposed method is based on utilizing the nonlinear property of magnetic saturation in carbon steel. A magnetic sensor system for the nonlinear magnetic method is designed by the magnetic circuit concept, and is modeled and analyzed with the finite element method and an equivalent electric circuit. The sensor system is built to experimentally test its performance in the estimation of wall thinning. To demonstrate the feasibility and validity of the proposed nonlinear magnetic method, the measured data from the sensor system are used to estimate the wall thinning, and are compared with the analysis results.

New algorithms for diagnosing defects of an air-operated valve for self diagnostic monitoring system

We have developed a self-diagnostic monitoring system for an air operated valve system which produces arrow patterns according to the states of the system and makes a diagnosis whenever the system shows the corresponding symptom [1, 2]. In our first model, we have used a neural network and a simple comparison method for decision processor. In this paper, we modify and improve the decision processor module. We developed a logistic regression algorithm for the simple decision algorithm and modified the neural network algorithm. By changing the rule for translating arrow symbols into 2-D tuples, we could make unambiguous and rich training data set. With this, we performed some simulations and present a result.

Electrical/Mechanical Diagnosis of Local Deterioration in 600V Shielded Twist Pair Cable in a Nuclear Power Plant

In this paper, we propose a electrical/mechanical method to effectively diagnose the local deterioration of a 10m long power shielded twist pair cable defined by the American Wire Gauge (AWG) 14 specification using electrical/mechanical methods. The rapid deterioration of the cable proceeded by using the heating furnace, which is based on the Arrhenius equations proceeds from 0 to 35 years with the deteriorated equivalent model. In this paper, we introduce a method to diagnose the characteristics of locally deteriorated cable by using S21 phase and frequency change rate measured by vector network analyzer which is the electrical diagnostic method. The measured S21 phase and rate of change of frequency show a constant correlation with the number of years of locally deteriorated cable, thus it can be useful for diagnosing deteriorated cables. The change of modulus due to deterioration was measured by a modulus measuring device, which is defined by the ratio of deformation from the force externally applied to the cable, and the rate of modulus change also shows a constant correlation with the number of years of locally deteriorated cable. Finally, By combining the advantages of electrical/ mechanical diagnostic methods, we can efficiently diagnose the local deterioration in the power shielded cable.

Estimation Method of Wall Thinning of Carbon Steel Pipe Using Nonlinearity of Magnetic Saturation

This paper proposes a nonlinear magnetic method to estimate wall thinning of carbon steel pipe used in the secondary steam cycle of nuclear power plants. The principle of the proposed method is based on utilizing the nonlinear property of magnetic saturation in the carbon steel. A magnetic sensor system for the nonlinear magnetic method is designed by the magnetic circuit concept, and is modeled and analyzed with the finite element method and an equivalent electric circuit. The experimental setup of the sensor system is built and tested for its performance of estimation. The data measured from the sensor system, that is used to estimate the wall thinning, are compared with the analysis results to demonstrate feasibility and validity of the proposed nonlinear magnetic method.

NERI Final Project Report: On-Line Intelligent Self-Diagnostic Monitoring System for Next Generation Nuclear Power Plants

This project provides a proof-of-principle technology demonstration for SDMS, where a distributed suite of sensors is integrated with active components and passive structures of types expected to be encountered in next generation nuclear power reactor and plant systems. The project employs state-of-the-art operational sensors, advanced stressor-based instrumentation, distributed computing, RF data network modules and signal processing to improve the monitoring and assessment of the power reactor system and gives data that is used to provide prognostics capabilities.