THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.

A Survey of Fault Detection, Isolation, and Reconfiguration Methods

Past, present and future of nonlinear system identification in structural dynamics

Technology developments in structural health monitoring of large-scale bridges

https://people.duke.edu/~hpgavin/SystemID/References/Markovsky+VanHuffel-SP-2007.pdf

Overview of total least-squares methods

Nowadays, many of the proposed solutions to improve automotive vehicle efficiency, such as downsized engines and advanced torque lock-up strategies (for automatic transmissions), can lead to worse noise and vibration characteristics. A typical phenomenon that occurs in such situations is low-frequency booming noise, which happens because of the irregular torque vibrations that are transferred through the flexible driveline elements. This paper presents a combined test and 1D modelling approach used to analyze and predict driveline torsional oscillations and their effect on low frequency booming noise and vibration. In this context, Model Based System Testing (MBST) can be defined as the framework that combines physical testing and simulation with the objective of validating and improving the behavior of 1D multiphysical models. Tests are carried out to obtain insight in the dynamical system behavior, as well as to obtain specific component parameters. This data is then used to create and improve 1D models of the full vehicle driveline, and to predict booming noise characteristics.

The Combination of Testing and 1D Modeling for Booming Noise Prediction in the Model Based System Testing Framework

This paper presents a safe imitation learning approach for autonomous vehicle driving, with attention on real-life human driving data and experimental validation. In order to increase occupant's acceptance and gain drivers' trust, the autonomous driving function needs to provide a both safe and comfortable behavior such as risk-free and naturalistic driving. Our goal is to obtain such behavior via imitation learning of a planning policy from human driving data. In particular, we propose to incorporate barrier functions and smooth spline-based motion parametrization in the training loss function. The advantage is twofold: improving safety of the learning algorithm, while reducing the amount of needed training data. Moreover, the behavior is learned from highway driving data, which is collected consistently by a human driver and then processed towards a specific driving scenario. For development validation, a digital twin of the real test vehicle, sensors, and traffic scenarios are reconstructed toward high-fidelity and physics-based modeling technologies. These models are imported to simulation tools and co-simulated with the proposed algorithm for validation and further testing. Finally, we present experimental results and analyses, and compare with the conventional imitation learning technique (behavioral cloning) to justify the proposed development.

Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving

—As the development of autonomous driving (AD) and advanced driver assistance systems (ADAS) progresses, the relevance of the comfort of users is gaining increasing interest. It becomes signiﬁcant to test and validate perceived comfort performance from the early phase of system development before driving on roads. Most of the present ADAS test procedures are not efﬁcient in performing such comfort evaluation. One of the main challenges is to integrate high-quality, realistic and predictable virtual trafﬁc scenarios into an ADAS testing framework that has physics-based sensors capable of sensing the virtual environment. In this paper, we present our development of a virtual reality based ADAS testing framework that enhances human perception evaluation. The main contribution relies on three aspects. First, we introduce our development of a large and high-quality (realism, structure, texture) 3D trafﬁc model of the Munich city in Germany. Second, we optimize the 3D model for virtual reality purpose, and real-time capable for human-in-the-loop ADAS testing. Finally, the model is then integrated into an ADAS framework for testing and validating ADAS functionalities and perceived comfort performance. The developed framework components are presented with illustra- tive examples.

Virtual Reality Assisted Human Perception in ADAS Development: a Munich 3D Model Study

The use of electric machines as vehicles raises the concern of the vibration generated by the structure. Radial force electrical machines have a high contribution This paper presents a coupled finite element calculating the radial force, torque rippl acoustic noise in synchronous reluctance m The knowledge of the modal parameters of (mode shapes and natural frequencies) a spectrum of the radial magnetic force can be design SynRMs with minimal noise through variations at an early stage. An automati developed for designing a synchronous relu enable faster implementation of the geometry investigation of electromechanical properti also the problems related to noise. The automation is demonstrated on modifying th design for a SynRM that would apply for electric vehicles. The effect of the stator ge distribution on acoustic noise can also be model presented in this paper. traction in electric and the noise level es in the stator of to these vibrations. t method (FEM) for le and predict the machines (SynRMs). the stator structure and the frequency e effectively used to h geometrical design c process has been uctance machine, to y modifications and ies while regarding practicality of the he rotor geometrical r traction of small eometry or winding analyzed with the

Torque Ripple and Nois nt Rotor Topologies of nous Reluctance Mach

Operational Modal Analysis (OMA), also known as output-only modal analysis, allows identifying modal parameters only by using the response measurements of the structures in operational conditions when the input forces cannot be measured. These information can then be used to improve numerical models in order to monitor the operating and structural conditions of the system. This is a critical aspect both for condition monitoring and maintenance of large wind turbines, particularly in the off-shore sector where operation and maintenance represent a high percentage of total costs. Although OMA is widely applied, the wind turbine case still remains an open issue. Numerical aeroelastic models could be used, once they have been validated, to introduce virtual damages to the structures in order to analyze the generated data. Results from such models can then be used as baseline to monitor the operating and structural condition of the machine.

/pdf/virtual-assessment-of-damage-detection-techniques-for-2wbecfb8kb.pdf

Herman Van der Auweraer

Papers

The Combination of Testing and 1D Modeling for Booming Noise Prediction in the Model Based System Testing Framework

Safe Imitation Learning on Real-Life Highway Data for Human-like Autonomous Driving

Virtual Reality Assisted Human Perception in ADAS Development: a Munich 3D Model Study

Torque Ripple and Nois nt Rotor Topologies of nous Reluctance Mach

Virtual Assessment of Damage Detection Techniques for Operational Wind Turbine