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

The present invention describes an order tracking system (200) and method (100) for tracking at least one order from mechanical and/or acoustic vibrations generated by a periodic excitation process of a physical system. The order tracking system (200) comprises a means for obtaining (212) a mechanical and/or acoustic vibration data of a physical system and a means for obtaining (214) system reference data . It further comprises a means for combining (224) the mechanical and/or acoustic vibration data with the system reference data and a means for applying (226) a digital FIR filter to at least the mechanical and/or acoustic vibration data for deriving based thereon at least one order.

Order tracking method and system

This paper presents a novel approach in the field of experimental and numerical investigation of mechanical properties of composite structures. It takes into account test data variability resulting from structural dynamic properties measurement and uses them to quantify uncertainties in model parameters updating. The main goal of the conducted research is to investigate the dynamic properties of fibre reinforced composite structures. Non-destructive experimental and numerical simulation methods are used hereto. In the experimental part, different test configurations were taken into account. The excitation was performed by means of random and harmonic, single and multi point stimuli while the response measurement was done through contact and non-contact acceleration, velocity and dynamic strain sensing. The test results are applied in two ways: for the structural identification of the object and for non-deterministic updating of the numerical model according to a range of experimental models obtained from test. The sources of the test data variabilities were related to the excitation and measurement technique applied for the investigated object. Non - deterministic model updating and verification & validation included uncertainties of its parameters by means of interval and stochastic methods. A number of variable test modal models were statistically assessed to investigate impact of variability source onto modal model parameters. The presented research was conducted in the context of the FP6 Marie Curie project UNVICO-2.

/pdf/uncertain-parameter-numerical-model-updating-according-to-421b0n9e4c.pdf

Uncertain parameter numerical model updating according to variable modal test data in application of large composite fuselage panel

Fault diagnosis is important to avoid unforeseen failures of IC engines, but normally requires an expert to interpret analysis results. Artificial Neural Networks are potential tools for the automated fault diagnosis of IC engines, as they can learn the patterns corresponding to various faults. Most engine faults can be classified into two categories: combustion faults and mechanical faults. Misfire is a typical combustion fault; piston slap and big end bearing knock are common mechanical faults. The automated diagnostic system proposed in this paper has three main stages, each stage including three neural networks. The first stage is the fault detection stage, where the neural networks detect whether there are faults in the engine and if so which kind. In the second stage, based on the detection results, the severity of the faults was identified. In the third stage, the neural networks localize which cylinder has a fault. The critical thing for a neural network is its input feature vector, and a previous study had indicated a number of features that should differentiate between the different faults and their location, based on advanced signal processing of the vibration signals measured for different normal and fault conditions. In this study, an advanced feature selection technology was employed to select the significant features as the inputs to networks. The input vectors were separated into two groups, one for training the network, and the other for its validation. Finally it has been demonstrated that the neural network based system can automatically differentiate and diagnose a number of engine faults, including location and severity.

Herman Van der Auweraer

Papers

Order tracking method and system

Uncertain parameter numerical model updating according to variable modal test data in application of large composite fuselage panel

Artificial Neural Network Based Fault Diagnosis of IC Engines

Critical assessment of Operational Path Analysis: effect of neglected paths

Simulating Acoustic Engine Performance Over a Broad Frequency Range