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A state-of-the-art, Advanced Weigh-In-Motion (WIM) system has been designed, installed, and tested on the west bound side of Interstate I-75/I-40 near the Knox County Weigh Station. The project is a Cooperative Research and Development Agreement (CRADA) between Oak Ridge National Laboratory (ORNL) and International Road Dynamics, Inc. (IRD) sponsored by the Office of Uranium Programs, Facility and Technology Management Division of the Department of Energy under CRADA No. ORNL95-0364. ORNL, IRD, the Federal Highway Administration, the Tennessee Department of Safety and the Tennessee Department of Transportation have developed a National High Speed WIM Test Facility for test and evaluation of high-speed WIM systems. The WIM system under evaluation includes a Single Load Cell WIM scale system supplied and installed by IRD. ORNL developed a stand-alone, custom data acquisition system, which acquires the raw signals from IRD's in-ground single load cell transducers. Under a separate contract with the Federal Highway Administration, ORNL designed and constructed a laboratory scale house for data collection, analysis and algorithm development. An initial advanced weight-determining algorithm has been developed. The new advanced WIM system provides improved accuracy and can reduce overall system variability by up to 30% over the existing high accuracy commercial WIM system.
"Once solely the domain of engineers, quality control has become a vital business operation used to increase productivity and secure competitive advantage. Introduction to Statistical Quality Control offers a detailed presentation of the modern statistical methods for quality control and improvement. Thorough coverage of statistical process control (SPC) demonstrates the efficacy of statistically-oriented experiments in the context of process characterization, optimization, and acceptance sampling, while examination of the implementation process provides context to real-world applications. Emphasis on Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) provides a strategic problem-solving framework that can be applied across a variety of disciplines.Adopting a balanced approach to traditional and modern methods, this text includes coverage of SQC techniques in both industrial and non-manufacturing settings, providing fundamental knowledge to students of engineering, statistics, business, and management sciences.A strong pedagogical toolset, including multiple practice problems, real-world data sets and examples, provides students with a solid base of conceptual and practical knowledge."--
This paper provides a summary of the weigh-in-motion (WIM) calibration practices used by state highway and load enforcement agencies in the United States. The detailed statistical data presented were collected through a web-based survey questionnaire. It covers three common WIM calibration practices, namely utilizing multiple passes of test trucks, utilizing traffic stream vehicles of known static weight, and employing only WIM data quality control (QC) techniques. To put the actual practice in perspective, an overview is provided of the current WIM calibration standard (ASTM E1318-02) and the new provisional standard for quantifying pavement roughness at the approach to WIM systems (AASHTO MP 14-05). Most agencies use a combination of two or more of these methods for WIM system calibration. The majority of agencies uses WIM data QC on a routine basis and they resort to one of the other two calibration methods when WIM data quality deteriorates. Test truck calibration typically involves one or two Class 9 trucks running at several speeds. Few of these agencies, however, perform actual pavement roughness measurements on the approach to the WIM sites. Agencies that use traffic stream vehicles of known static weight for WIM calibration obtain static weights manually using permanent static scales. The method involves up to 100 trucks selected by class, speed or both class and speed. Agencies use a variety of traffic elements and formulas for computing calibration factors. Similarly, a variety of traffic data element errors are computed and various approaches are used for computing calibration factors. In the light of these findings, the paper provides a number of recommendations for improving current WIM calibration practices.
Machine Vision systems combine image processing with industrial automation. One of the primary areas of application of Machine Vision in the Industry is in the area of Quality Control. Machine vision provides fast, economic and reliable inspection that improves quality as well as business productivity. Building machine vision applications is a challenging task as each application is unique, with its own requirements and desired outcome. A Guide to Machine Vision in Quality Control follows a practitioner’s approach to learning machine vision. The book provides guidance on how to build machine vision systems for quality inspections. Practical applications from the Industry have been discussed to provide a good understanding of usage of machine vision for quality control. Real-world case studies have been used to explain the process of building machine vision solutions. The book offers comprehensive coverage of the essential topics, that includes: Introduction to Machine Vision Fundamentals of Digital Images Discussion of various machine vision system components Digital image processing related to quality control Overview of automation The book can be used by students and academics, as well as by industry professionals, to understand the fundamentals of machine vision. Updates to the on-going technological innovations have been provided with a discussion on emerging trends in machine vision and smart factories of the future. Sheila Anand is a PhD graduate and Professor at Rajalakshmi Engineering College, Chennai, India. She has over three decades of experience in teaching, consultancy and research. She has worked in the software industry and has extensive experience in development of software applications and in systems audit of financial, manufacturing and trading organizations. She guides Ph.D. aspirants and many of her research scholars have since been awarded their doctoral degree. She has published many papers in national and international journals and is a reviewer for several journals of repute. L Priya is a PhD graduate working as Associate Professor and Head, Department of Information Technology at Rajalakshmi Engineering College, Chennai, India. She has nearly two decades of teaching experience and good exposure to consultancy and research. She has delivered many invited talks, presented papers and won several paper awards in International Conferences. She has published several papers in International journals and is a reviewer for SCI indexed journals. Her areas of interest include Machine Vision, Wireless Communication and Machine Learning.
This book reviews current design paths for soft sensors, and guides readers in evaluating different choices. The book presents case studies resulting from collaborations between the authors and industrial partners. The solutions presented, some of which are implemented on-line in industrial plants, are designed to cope with a wide range of applications from measuring system backup and what-if analysis through real-time prediction for plant control to sensor diagnosis and validation.