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

计量经济分析 = Econometric analysis

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

https://www.ece.lsu.edu/mcu/lawss/add_materials/FaultDetectionPart1.pdf

A Review of Process Fault Detection and Diagnosis Part I : Quantitative Model-Based Methods

A review of process fault detection and diagnosis: Part III: Process history based methods

Audit of sensor networks for efficient fault diagnosis

We propose a generalized parametrized optimal filter (GPOF) for linear stochastic direct feed-through systems with correlated process and measurement disturbances. We consider a generic case in which any set of unknown parameters can simultaneously affect the stochastic process model and measurements. The GPOF allows unbiased estimates of states to be obtained in the presence of unknown inputs or parameters by appropriately choosing the gain matrix during the state update step. We additionally prove the stability and convergence properties of proposed GPOF for linear time invariant systems with correlated process and measurement noises. We also propose a parameter estimation approach which utilizes the biased innovations generated by GPOF. We then extend the results of GPOF to nonlinear stochastic direct feed-through systems with nonadditive correlated process and measurement noises. In particular we introduce an extended generalized parametrized optimal filter (EGPOF) for state and parameter estimation. ...

Robust State Estimation and Parameter Estimation for Linear and Nonlinear Direct Feed-through Systems with Correlated Disturbances

Optimization Based Constrained Gaussian Sum Unscented Kalman Filter

In-core flux measurement is critical for monitoring and power regulation of a large core nuclear reactor. Self Powered Neutron Detectors (SPNDs) are used for measuring the neutron flux in a nuclear reactor. In this paper we propose an online method for SPND gross error detection, identification and estimation. The method uses linear models which are extracted from data and which adapt continuously in time. This adaption is made possible by use of recursive PCA. However, unlike existing recursive PCA approaches which make approximations to achieve recursion, our proposed approach does not make any approximation. We term our technique as ‘Recursive Principal Component Analysis: Exact Computation’ (RPCA-EC). Use of recursion ensures that the computational requirements of RPCA-EC are low thereby facilitating online implementation. Continuous adaption ensures that the model evolves to adequately capture the time varying relationships amongst the SPNDs. The relationships amongst the SPNDs vary with time due to significant variations in the neutron flux profiles in the reactor with varying operating power levels in the reactor. We apply our proposed method on data taken from an operating nuclear reactor in India and compare results with alternate implementations. Results show that the false alarm rate of our implementation is reasonable thereby indicating that the model adapts to time varying relationships. Performance in presence of gross errors is also satisfactory.

Mani Bhushan

Papers

Audit of sensor networks for efficient fault diagnosis

Robust State Estimation and Parameter Estimation for Linear and Nonlinear Direct Feed-through Systems with Correlated Disturbances

Optimization Based Constrained Gaussian Sum Unscented Kalman Filter

Adaptive, online models to detect and estimate gross error in SPNDs

MHE Based State and Parameter Estimation for Systems subjected to Non-Gaussian Disturbances