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

There is an increasing demand for dynamic systems to become safer and more reliable This requirement extends beyond the normally accepted safety-critical systems such as nuclear reactors and aircraft, where safety is of paramount importance, to systems such as autonomous vehicles and process control systems where the system availability is vital It is clear that fault diagnosis is becoming an important subject in modern control theory and practice Robust Model-Based Fault Diagnosis for Dynamic Systems presents the subject of model-based fault diagnosis in a unified framework It contains many important topics and methods; however, total coverage and completeness is not the primary concern The book focuses on fundamental issues such as basic definitions, residual generation methods and the importance of robustness in model-based fault diagnosis approaches In this book, fault diagnosis concepts and methods are illustrated by either simple academic examples or practical applications The first two chapters are of tutorial value and provide a starting point for newcomers to this field The rest of the book presents the state of the art in model-based fault diagnosis by discussing many important robust approaches and their applications This will certainly appeal to experts in this field Robust Model-Based Fault Diagnosis for Dynamic Systems targets both newcomers who want to get into this subject, and experts who are concerned with fundamental issues and are also looking for inspiration for future research The book is useful for both researchers in academia and professional engineers in industry because both theory and applications are discussed Although this is a research monograph, it will be an important text for postgraduate research students world-wide The largest market, however, will be academics, libraries and practicing engineers and scientists throughout the world

Robust Model-Based Fault Diagnosis for Dynamic Systems

Humidity measurement is one of the most significant issues in various areas of applications such as instrumentation, automated systems, agriculture, climatology and GIS. Numerous sorts of humidity sensors fabricated and developed for industrial and laboratory applications are reviewed and presented in this article. The survey frequently concentrates on the RH sensors based upon their organic and inorganic functional materials, e.g., porous ceramics (semiconductors), polymers, ceramic/polymer and electrolytes, as well as conduction mechanism and fabrication technologies. A significant aim of this review is to provide a distinct categorization pursuant to state of the art humidity sensor types, principles of work, sensing substances, transduction mechanisms, and production technologies. Furthermore, performance characteristics of the different humidity sensors such as electrical and statistical data will be detailed and gives an added value to the report. By comparison of overall prospects of the sensors it was revealed that there are still drawbacks as to efficiency of sensing elements and conduction values. The flexibility offered by thick film and thin film processes either in the preparation of materials or in the choice of shape and size of the sensor structure provides advantages over other technologies. These ceramic sensors show faster response than other types.

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Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review

This paper presents an S-Transform based probabilistic neural network (PNN) classifier for recognition of power quality (PQ) disturbances. The proposed method requires less number of features as compared to wavelet based approach for the identification of PQ events. The features extracted through the S-Transform are trained by a PNN for automatic classification of the PQ events. Since the proposed methodology can reduce the features of the disturbance signal to a great extent without losing its original property, less memory space and learning PNN time are required for classification. Eleven types of disturbances are considered for the classification problem. The simulation results reveal that the combination of S-Transform and PNN can effectively detect and classify different PQ events. The classification performance of PNN is compared with a feedforward multilayer (FFML) neural network (NN) and learning vector quantization (LVQ) NN. It is found that the classification performance of PNN is better than both FFML and LVQ.

Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network

In this paper, a prototype wavelet-based neural-network classifier for recognizing power-quality disturbances is implemented and tested under various transient events. The discrete wavelet transform (DWT) technique is integrated with the probabilistic neural-network (PNN) model to construct the classifier. First, the multiresolution-analysis technique of DWT and the Parseval's theorem are employed to extract the energy distribution features of the distorted signal at different resolution levels. Then, the PNN classifies these extracted features to identify the disturbance type according to the transient duration and the energy features. Since the proposed methodology can reduce a great quantity of the distorted signal features without losing its original property, less memory space and computing time are required. Various transient events tested, such as momentary interruption, capacitor switching, voltage sag/swell, harmonic distortion, and flicker show that the classifier can detect and classify different power disturbance types efficiently.

Wavelet-based neural network for power disturbance recognition and classification

The paper presents a measurement method for power quality analysis in electrical power systems. The method is the evolution of an iterative procedure already set up by the authors and allows the most relevant disturbances in electrical power systems to be detected, localized and estimated automatically. The detection of the disturbance and its duration are attained by a proper application, on the sampled signal, of the continuous wavelet transform (CWT). Disturbance amplitude is estimated by decomposing, in an optimized way, the signal in frequency subbands by means of the discrete time wavelet transform (DTWT). The proposed method is characterized by high rejection to noise, introduced by both measurement chain and system under test, and it is designed for an agile disturbance classification. Moreover, it is also conceived for future implementation both in a real-time measurement equipment and in an off-line analysis tool. In the paper firstly the theoretical background is reported and briefly discussed. Then, the proposed method is described in detail. Finally, some case-studies are examined in order to highlight the performance of the method.

A measurement method based on the wavelet transform for power quality analysis

A methodology is presented for developing a digital signal-processing architecture capable of simultaneous and automated detection and classification of transient signals. The basic unit of the aforementioned architecture is the wavelet network, which combines the ability of the wavelet transform of analyzing nonstationary signals with the classification capability of artificial neural networks. By exploiting the modularity as well as original strategies concerning wavelet network implementation and training, the method succeeds in enhancing the classification performance with respect to other available solutions.

Wavelet network-based detection and classification of transients

The possibilities offered by neural networks for overcoming both system identification and fault diagnosis problems in dynamic systems are investigated. In particular, an original neural fault diagnosis procedure is illustrated. Its sensitivity and response time enables it to be used to great advantage in online applications. Some applications are also reported which, although pertaining to a simple linear dynamic system, highlight the general applicability and advantages of a neural approach. >

A neural network approach for identification and fault diagnosis on dynamic systems

The sol-gel synthesis of silicophosphate gels using phosphoryl chloride and tetraethoxysilane as molecular precursors is reported and discussed. Gel-derived glasses and films having the molar compositions 10P2O5 · 90SiO2 and 30P2O5 · 70SiO2 have been obtained. The structure of the dried gels as well as the structural modifications that occurs during the transformations in gel-derived glasses are analyzed by Fourier transform infrared spectroscopy (FTIR). It has been found that the evidence of the P—O—Si linkages begins to appear only on the FTIR spectra of the bulk gels heat treated up to 400°C while they are well resolved on the FTIR spectra of the bulk gel samples heated up to 1000°C indicating that at this temperature the transformation in the corresponding gel-derived glasses occurs. The humidity sensitive properties of the gel-films have been evaluated by electrochemical impedance spectroscopy (EIS). The phosphorous content as well as the temperature of the heat treatments strongly affect the sensitivity to RH of the gel-derived films.

Massimo D'Apuzzo

Papers

A measurement method based on the wavelet transform for power quality analysis

Wavelet network-based detection and classification of transients

A neural network approach for identification and fault diagnosis on dynamic systems

Sol-Gel Synthesis of Humidity-Sensitive P2O5-SiO2 Amorphous Films

A method for the automatic detection and measurement of transients. Part I: the measurement method