Clarence W. de Silva

Vibration and Shock Handbook

Preface Fundamentals and Analysis Time-Domain Analysis C.W. de Silva Frequency-Domain Analysis C.W. de Silva Modal Analysis C.W. de Silva Distributed-Parameter Systems C.W. de Silva Random Vibration H. Benaroya Computer Techniques Numerical Techniques M.D. Dahleh Vibration Modeling and Software Tools D. Song, C. Huang, and Z-S. Liu Computer Analysis of Flexibly Supported Multibody Systems I. Esat and M. Dabestani Finite Element Applications in Dynamics M.S. Gadala Vibration Signal Analysis C.W. de Silva Wavelets-Concepts and Applications P.D. Spanos Shock and Vibration Mechanical Shock C. Lalanne Vibration and Shock Problems of Civil Engineering Structures P. Mendis and T. Ngo Reinforced Concrete Structures Y.L. Mo Instrumentation and Testing Vibration Instrumentation C.W. de Silva Virtual Instrumentation M. Sedlak and C. DeFilippo Signal Conditioning and Modification C.W. de Silva Vibration Testing C.W. de Silva Experimental Modal Analysis C.W. de Silva Vibration Suppression and Control Vibration Damping C.W. de Silva Damping Theory R.D. Peters Experimental Techniques in Damping R.D. Peters Structure and Equipment Isolation Y.B. Lang, L.Y. Lu, and J.D. Yau Vibration Control N. Jalili and E. Esmailzadeh Helicopter Rotor Tuning K. Danai Monitoring and Diagnosis Machine Condition Monitoring and Fault Diagnostics C.K. Mechefske Vibration-Based Tool Conditioning Monitoring Systems C. Scheffer and P.S. Heyns Fault Diagnosis of Helicopter Gearboxes K. Danai Vibration Suppression and Monitoring in Precision Motion Systems K.K. Tan, T.H. Lee, K.Z. Tang, S. Huang, S.Y. Lim, W. Lin, and Y.P. Leow Seismic Vibration Seismic Base Isolation and Vibration Control H. Iemura, S.K. Jain, and M.H. Pradono Seismic Random Vibration of Long-Span Structures J. Lin and Y. Zhang Seismic Qualification of Equipment C.W. de Silva Design and Applications Vibration Design and Control C.W. de Silva Structural Dynamic Modification and Sensitivity Analysis S.H. Chen Vibration in Rotating Machinery H.S. Samarasekera Regenerative Chatter in Machine Tools R.G. Landers Fluid-Induced Vibration S.M. Han Acoustics Sound Levels and Decibels S. Akashita Hearing and Psychological Effects S. Akashita Noise Control Criteria and Regulations S. Akashita Instrumentation K. Nagakura Source of Noise S. Akashita Design of Absorption T. Obata Design of Reactive Mufflers T. Obata Design of Sound Insulation K. Okura Statistical Energy Analysis T. Koizumi Glossary Index

A machine-learning approach to multi-robot coordination

This paper presents a machine-learning approach to the multi-robot coordination problem in an unknown dynamic environment. A multi-robot object transportation task is employed as the platform to assess and validate this approach. Specifically, a flexible two-layer multi-agent architecture is developed to implement multi-robot coordination. In this architecture, four software agents form a high-level coordination subsystem while two heterogeneous robots constitute the low-level control subsystem. Two types of machine learning-reinforcement learning (RL) and genetic algorithms (GAs)-are integrated to make decisions when the robots cooperatively transport an object to a goal location while avoiding obstacles. A probabilistic arbitrator is used to determine the winning output between the RL and GA algorithms. In particular, a modified RL algorithm called the sequential Q-learning algorithm is developed to deal with the issues of behavior conflict that arise in multi-robot cooperative transportation tasks. The learning-based high-level coordination subsystem sends commands to the low-level control subsystem, which is implemented with a hybrid force/position control scheme. Simulation and experimental results are presented to demonstrate the effectiveness and adaptivity of the developed approach.

Vibration monitoring, testing, and instrumentation

VIBRATION INSTRUMENTATION Clarence W. de Silva Introduction Vibration Exciters Control System Performance Specification Motion Sensors and Transducers Torque, Force, and Other Sensors Appendix 1A Virtual Instrumentation for Data Acquisition, Analysis, and Presentation SIGNAL CONDITIONING AND MODIFICATION Clarence W. de Silva Introduction Amplifiers Analog Filters Modulators and Demodulators Analog-Digital Conversion Bridge Circuits Linearizing Devices Miscellaneous Signal Modification Circuitry Signal Analyzers and Display Devices VIBRATION TESTING Clarence W. de Silva Introduction Representation of a Vibration Environment Pretest Procedures Testing Procedures Some Practical Information EXPERIMENTAL MODAL ANALYSIS Clarence W. de Silva Introduction Frequency-Domain Formulation Experimental Model Development Curve Fitting of Transfer Functions Laboratory Experiments Commercial EMA Systems MECHANICAL SHOCK Christian Lalanne Definitions Description in the Time Domain Shock Response Spectrum Pyroshocks Use of Shock Response Spectra Standards Damage Boundary Curve Shock Machines Generation of Shock Using Shakers Control by a Shock Response Spectrum Pyrotechnic Shock Simulation MACHINE CONDITION MONITORING AND FAULT DIAGNOSTICS Chris K Mechefske Introduction Machinery Failure Basic Maintenance Strategies Factors which Influence Maintenance Strategy Machine Condition Monitoring Transducer Selection Transducer Location Recording and Analysis Instrumentation Display Formats and Analysis Tools Fault Detection Fault Diagnostics VIBRATION-BASED TOOL CONDITION MONITORING SYSTEMS C. Scheffer and P.S. Heyns Introduction Mechanics of Turning Vibration Signal Recording Signal Processing for Sensor-Based Tool Condition Monitoring Wear Model/Decision-Making for Sensor-Based Tool Condition Monitoring Conclusion FAULT DIAGNOSIS OF HELICOPTER GEARBOXES Kourosh Danai Introduction Abnormality Scaling The Structure-Based Connectionist Network Sensor Location Selection A Case Study Conclusion VIBRATION SUPPRESSION AND MONITORING IN PRECISION MOTION SYSTEMS K.K. Tan, T.H. Lee, K.Z. Tang, S. Huang, S.Y. Lim, W. Lin, and Y.P. Leow Introduction Mechanical Design to Minimize Vibration Adaptive Notch Filter Real-Time Vibration Analyzer Practical Insights and Case Study Conclusions VIBRATION AND SHOCK PROBLEMS OF CIVIL ENGINEERING STRUCTURES Priyan Mendis and Tuan Ngo Introduction Earthquake-Induced Vibration of Structures Dynamic Effects of Wind Loading on Structures Vibrations Due to Fluid - Structure Interaction Blast Loading and Blast Effects on Structures Impact Loading Floor Vibration SEISMIC BASE ISOLATION AND VIBRATION CONTROL Hirokazu Iemura, Sarvesh Kumar Jain, and Mulyo Harris Pradono Introduction Seismic Base Isolation Seismic Vibration Control SEISMIC RANDOM VIBRATION OF LONG-SPAN STRUCTURES Jiahao Lin and Yahui Zhang Introduction Seismic Random-Excitation Fields Pseudoexcitation Method for Structural Random Vibration Analysis Long-Span Structures Subjected to Stationary Random Ground Excitations Long-Span Structures Subjected to Nonstationary Random Ground Excitations Conclusions SEISMIC QUALIFICATION OF EQUIPMENT Clarence W. de Silva Introduction Distribution Qualification Seismic Qualification HUMAN RESPONSE TO VIBRATION Clarence W. de Silva Introduction Vibration Excitations on Humans Human Response to Vibration Regulation of Human Vibration INDEX

Research laboratory for fish processing automation

Abstract This paper outlines the organization, resources, and research and development activities of a newly established laboratory for industrial automation at a major Canadian university. The laboratory has been established in the Department of Mechanical Engineering primarily to support the research and development activities associated with the Natural Sciences and Engineering Research Council (NSERC) Chair of Industrial Automation. The research is focused on the development of advanced and low-cost technology for flexible automation of the fish processing industry. The main objective is to upgrade the technology used in the mechanical processing of fish, thereby reducing wastage of the primary product, improving efficiency, and making the local industry more competitive in export markets. Establishment of an infrastructure in industrial automation within the university and local training/ retraining of engineers with control and automation expertise for local industries are related objectives. As a specific task, an experimental workcell for fish processing is being developed in the laboratory. The theme of the activities of the laboratory is the integration of advanced control, high-level computer vision, and robotic manipulation and devices, for application in the area of fish processing.

Multi-robot Box-pushing: Single-Agent Q-Learning vs. Team Q-Learning

In this paper, two types of multi-agent reinforcement learning algorithms are employed in a task of multi-robot box-pushing. The first one is a direct extension of the single-agent Q-learning, which does not have a solid theoretical foundation because it violates the static environment assumption of the Q-learning algorithm. The second one is the Team Qlearning algorithm, which is a multi-agent reinforcement learning algorithm, and is proved to converge to the optimal policy. The states, actions, and reward function of the algorithms are presented in the paper. Based on the two Q-learning algorithms, a fully distributed multi-robot system is developed. Computer simulations are carried out using the developed system. The simulation results show that the two algorithms are effective in a simple environment. It is shown, however, that the single-agent Q-learning algorithm does a better job than the team Q-learning algorithm in a complicated and unknown environment with many obstacles

An analytical framework for knowledge-based tuning of servo controllers

Abstract This paper investigates the knowledge-based tuner of a two-level control structure in which a crisp servo controller that occupies the lower level is tuned by a fuzzy logic tuner at the upper level. First the knowledge-based tuner is expressed in the conventional fuzzy-logic formulation of a linguistic rule base to which the compositional rule of inference may be applied. Next the concepts of rule dissociation and fuzzy resolution are introduced, and resolution relationships are defined to build an analytical framework for the tuning problem. Stability of the overall control system is discussed in terms of the stability of the original system. Computational requirements for the tuning system are analyzed to show that a substantial reduction in computational effort can be achieved through this analytical framework. An example of tuning a PID controller of a nonminimum-phase plant with transport delay is presented to illustrate the application of the concepts that are developed in the paper.

Mechatronic Systems : Devices, Design, Control, Operation and Monitoring

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Mechatronics: A Foundation Course

Now that modern machinery and electromechanical devices are typically being controlled using analog and digital electronics and computers, the technologies of mechanical engineering in such a system can no longer be isolated from those of electronic and computer engineering. Mechatronics: A Foundation Course applies a unified approach to meet this challenge, developing an understanding of the synergistic and concurrent use of mechanics, electronics, computer engineering, and intelligent control systems for everything from modeling and analysis to design, implementation, control, and integration of smart electromechanical products. This book explains the fundamentals of integrating different types of components and functions, both mechanical and electrical, to achieve optimal operation that meets a desired set of performance specifications. This integration will benefit performance, efficiency, reliability, cost, and environmental impact. With useful features that distinguish it from other comparable books, this solid learning tool: Prioritizes readability and convenient reference Develops and presents key concepts and formulas, summarizing them in windows, tables, and lists in a user-friendly format Includes numerous worked examples, problems, and exercises related to real-life situations and the practice of mechatronics Describes and employs MATLAB, Simulink, LabVIEW, and associated toolboxes, providing various illustrative examples for their use Explores the limitations of available software tools and teaches the reader how to choose proper tools to solve a given problem and interpret and assess the validity of the results The text conveys the considerable experience that author Clarence de Silva gained from teaching mechatronics at the graduate and professional levels, as well as from his time working in industry for organizations such as IBM, Westinghouse Electric, and NASA. It systematically and seamlessly incorporates many different underlying engineering fundamentals into analytical methods, modeling approaches, and design techniques for mechatronicsall in a single resource.

Closed-loop design evolution of engineering system using condition monitoring through internet of things and cloud computing

Flexibility of a manufacturing system is quite important and advantageous in modern industry, which function in a competitive environment where market diversity and the need for customized product are growing. Key machinery in a manufacturing system should be reliable, flexible, intelligent, less complex, and cost effective. To achieve these goals, the design methodologies for engineering systems should be revisited and improved. In particular, continuous or on-demand design improvements have to be incorporated rapidly and effectively in order to address new design requirements or resolve potential weaknesses of the original design. Design of an engineering system, which is typically a multi-domain system, can become complicated due to its complex structure and possible dynamic coupling between domains. An integrated and concurrent approach should be considered in the design process, in particular in the conceptual and detailed design phases. In the context of multi-domain design, attention has been given recently to such subjects as multi-criteria decision making, multi-domain modeling, evolutionary computing, and genetic programing. More recently, machine condition monitoring has been considered for integration into a scheme of design evolution even though many challenges exist for this to become a reality such as lack of systematic approaches and the existence of technical barriers in massive condition data acquisition, transmission, storage and mining. Recently, the internet of things (IoT) and cloud computing (CC) are being developed quickly and they offer new opportunities for evolutionary design for such tasks as data acquisition, storage and processing. In this paper, a framework for the closed-loop design evolution of engineering systems is proposed in order to achieve continuous design improvement for an engineering system through the use of a machine condition monitoring system assisted by IoT and CC. New design requirements or the detection of design weaknesses of an existing engineering system can be addressed through the proposed framework. A design knowledge base that is constructed by integrating design expertise from domain experts, on-line process information from condition monitoring and other design information from various sources is proposed to realize and supervise the design process so as to achieve increased efficiency, design speed, and effectiveness. The framework developed in this paper is illustrated by using a case study of design evolution of an industrial manufacturing system.

Applications of fuzzy logic in the control of robotic manipulators

Abstract Fuzzy logic has been utilized at several hierarchical levels of a typical robotic control system. Four broad levels of application may be identified - task design, system monitoring (including self-tuning and self-organization), information filtering and preprocessing, and in-loop direct control. Even though the need for fuzzy logic is felt mostly at upper levels of the control system, the present applications are mainly concentrated within the lowest level, perhaps driven by convenience rather than necessity. This paper surveys several applications of fuzzy logic in the control of robotic manipulators. Applications are grouped into four hierarchical categories, broadly corresponding to an existing architecture of a robotic control system. Such a classification can be beneficial in ascertaining the appropriateness of fuzzy logic for the specific control task.