Engineering Commons^™

Optimization Of Railroad Bearing Health Monitoring System For Wireless Utilization, Jonas Cuanang, Constantine Tarawneh, Martin Amaro Jr., Jennifer Lima, Heinrich D. Foltz

Mechanical Engineering Faculty Publications and Presentations

In the railroad industry, systematic health inspections of freight railcar bearings are required. Bearings are subjected to high loads and run at high speeds, so over time the bearings may develop a defect that can potentially cause a derailment if left in service operation. Current bearing condition monitoring systems include Hot-Box Detectors (HBDs) and Trackside Acoustic Detection Systems (TADS™). The commonly used HBDs use non-contact infrared sensors to detect abnormal temperatures of bearings as they pass over the detector. Bearing temperatures that are about 94°C above ambient conditions will trigger an alarm indicating that the bearing must be removed from …

An Immersed Boundary Geometric Preprocessor For Arbitrarily Complex Terrain And Geometry, Inanc Senocak, Micah Sandusky, Rey Deleon, Derek Wade, Kyle Felzien, Marianna Budnikova

Mechanical and Biomedical Engineering Faculty Publications and Presentations

There is a growing interest to apply the immersed boundary method to compute wind fields over arbitrarily complex terrain. The computer implementation of an immersed boundary module into an existing flow solver can be accomplished with minor modifications to the rest of the computer program. However, a versatile preprocessor is needed at the first place to extract the essential geometric information pertinent to the immersion of an arbitrarily complex terrain inside a 3D Cartesian mesh. Errors in the geometric information can negatively impact the correct implementation of the immersed boundary method as part of the solution algorithm. Additionally, the distance …

Parallel-Sparse Symmetrical/Unsymmetrical Finite Element Domain Decomposition Solver With Multi-Point Constraints For Structural/Acoustic Analysis, Siroj Tungkahotara, Willie R. Watson, Duc T. Nguyen, Subramaniam D. Rajan

Civil & Environmental Engineering Faculty Publications

Details of parallel-sparse Domain Decomposition (DD) with multi-point constraints (MPC) formulation are explained. Major computational components of the DD formulation are identified. Critical roles of parallel (direct) sparse and iterative solvers with MPC are discussed within the framework of DD formulation. Both symmetrical and unsymmetrical system of simultaneous linear equations (SLE) can be handled by the developed DD formulation. For symmetrical SLE, option for imposing MPC equations is also provided.

Large-scale (up to 25 million unknowns involving complex numbers) structural and acoustic Finite Element (FE) analysis are used to evaluate the parallel computational performance of the proposed DD implementation using …

A Pre-Crash Simulator To Evaluate Vehicle Collision Prediction Algorithms, Dana Desrosiers, Charles Birdsong, Peter Schuster

Mechanical Engineering

This paper describes a software simulator for pre-crash collision predictions. The simulator is a surrogate test bed for evaluating the performance of proposed pre-crash algorithms. It reads data from a file, transfers distance and angular position of a target to a test algorithm, and then records the algorithm’s predictions. To illustrate the simulator functionality, a simplified test algorithm is also described. This algorithm predicts collision risks based on assumptions about the size and acceleration of a target object, and the turning and braking limits of the host vehicle. The test algorithm is shown to be effective for cases where both …

Identification Of Cutting Force In End Milling Operations Using Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

The problem of identifying the cutting force in end milling operations is considered in this study. Recurrent neural networks are used here and are trained using a recursive least squares training algorithm. Training results for data obtained from a SAJO 3-axis vertical milling machine for steady slot cuts are presented. The results show that a recurrent neural network can learn the functional relationship between the feed rate and steady-state average resultant cutting force very well. Furthermore, results for the Mackey-Glass time series prediction problem are presented to illustrate the faster learning capability of the neural network scheme presented here

A Recursive Least Squares Training Algorithm For Multilayer Recurrent Neural Networks, Q. Xu, K. Krishnamurthy, Bruce M. Mcmillin, Wen Feng Lu

Mechanical and Aerospace Engineering Faculty Research & Creative Works

Recurrent neural networks have the potential to perform significantly better than the commonly used feedforward neural networks due to their dynamical nature. However, they have received less attention because training algorithms/architectures have not been well developed. In this study, a recursive least squares algorithm to train recurrent neural networks with an arbitrary number of hidden layers is developed. The training algorithm is developed as an extension of the standard recursive estimation problem. Simulated results obtained for identification of the dynamics of a nonlinear dynamical system show promising results.