Response analysis of cracked structure subjected to transit mass – a parametric study

This work is focused on determining the response of a multi-cracked structure in the presence of different types of cracks vibrated due to a transit mass. The open transverse and inclined edge cracks of random crack depth are present at various locations of the cracked structure. The mass is moving on the beam at the different critical speeds of the structure. Runge-Kutta fourth order method is employed to evaluate the response of the structure numerically. The significance of different factors like the magnitude of the moving mass, moving speed, crack depth, crack inclination angle and their effects on the response of the deteriorated structure are investigated. Numerical analyses with numerous examples are carried out and validated the results with finite element analysis (FEA) and experimental investigations

Intelligent neuro-controller for navigation of mobile robot

This paper deals with the reactive control of an autonomous robot which move safely in a crowded real world unknown environment and to reach specified target by avoiding static as well as dynamic obstacle. The inputs to the proposed neurocontroller consist of left, right, and front obstacle distance to its locations and target angle between a robot and a specified target being acquired by an array of sensors. A four layer neural networks is used to design and develop the neurocontroller to solve the path and time optimization problem of mobile robots which deals the with cognitive tasks such as learning, adaptation, generalization and optimization. Back propagation method is used to trained the network. This paper analyzes the kinematical modeling of mobile robots as well as the design of control systems for the autonomous motion of the robot. The training of the nets and the control performances analysis have been done in a real experimental setup. The simulation results are compared with experimental results which are satisfactory and shows a very good agreement

Optimum path planning of mobile robot in unknown static and dynamic environments using Fuzzy-Wind Driven Optimization algorithm

This article introduces a singleton type-1 fuzzy logic system (T1-SFLS) controller and Fuzzy-WDO hybrid for the autonomous mobile robot navigation and collision avoidance in an unknown static and dynamic environment. The WDO (Wind Driven Optimization) algorithm is used to optimize and tune the input/output membership function parameters of the fuzzy controller. The WDO algorithm is working based on the atmospheric motion of infinitesimal small air parcels navigates over an N-dimensional search domain. The performance of this proposed technique has compared through many computer simulations and real-time experiments by using Khepera-III mobile robot. As compared to the T1-SFLS controller the Fuzzy-WDO algorithm is found good agreement for mobile robot navigation

A New Intelligent Motion Planning for Mobile Robot Navigation using Multiple Adaptive Neuro-Fuzzy Inference System

and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as effective and suitable methods for modeling an engineering system. This paper presents a new hybrid technique based on the combination of fuzzy inference system and artificial neural network for addressing navigational problem of autonomous mobile robot. First we developed an adaptive fuzzy controller with four input parameters, two output parameters and three parameters each. Afterwards each adaptive fuzzy controller acts as a single takagi-sugeno type fuzzy inference system, where inputs are front obstacle distance (FOD), left obstacle distance (LOD), right obstacle distance (ROD) (from robot), heading angle (HA) (angle to target) and output corresponds to the wheel velocities ( Left wheel and right wheel) for the mobile robot. The effectiveness, feasibility and robustness of the proposed navigational controller have been demonstrated by means of simulation experiments. The real time experimental results were verified with simulation experiments, showing that the proposed navigational algorithm consistently performs better results to navigate the mobile robot safely in a completely or partially unknown environment

Response of Damaged Structure to High Speed Mass

AbstractThe present research article analyzes the response of a single cracked cantilever beam subjected to moving load. The equation of motion of the damaged structure under moving load has been developed using continuum mechanics and Duhamel integral has been incorporated to get the solution. The deflection produced during the traversing of the load across the structure has been determined at the free end as well as each position of the mass on the beam. A computational work with various examples has been carried out for the damaged structure and the influence of numerous parameters such as speed, mass, crack depth and location on the response of the damaged structure have been analyzed

On the way to fault detection method in moving load dynamics problem by modified recurrent neural networks approach

Parameters identification on structure subjected to moving load can be predicted by using the accurate and reliable data. The concepts of recurrent neural networks (RNNs) approach have been used in parameters (crack locations and severities) identifications in structure subjected to moving load in the present methodology. This methodology has incorporated the knowledge based Elman's recurrent neural networks (ERNNs) and Jordan's recurrent neural networks (JRNNs) jointly for the identification of parameters. This approach has been addressed as the inverse problem for predicting the locations and quantification of cracks in the structure in a supervised manner. The Levenberg-Marquardt's back propagation algorithm is implemented to train the proposed networks. To check the robustness of the present method, Numerical studies followed by Finite Element Analysis (FEA) and experimental verifications (Forward problems) are presented as a case study by considering a multi-cracked simply supported structure under a moving mass. The estimated crack locations and severities obtained from the proposed RNNs model converge well with those of FEA and experiments. From the demonstration of the case study, it concludes that the proposed analogy can identify and quantify the crack locations and severities effectively

Structural damage detection in moving load problem using JRNNs based method

Damage detection in a structure using the vibration signature is a quiet smart method for condition monitoring of the structure. In this problem, the Recurrent Neural Networks (RNNs) based method has been implemented for damage detection in the moving load problem as an inverse method. A multi-cracked simply supported beam under a traversing load has been considered for the present problem. The localization and severities of the supervised cracks on the structure are determined using the adapted Jordan’s Recurrent Neural Networks (JRNNs) approach. The mechanism of Levenberg-Marquardt’s back propagation algorithm has been implemented to train the networks. To check the adoptability of the proposed JRNNs method, numerical analyses along with laboratory test verifications have been conducted and found to be well emerged