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

A method, called the Eigensystem Realization Algorithm (ERA), is developed for modal parameter identification and model reduction of dynamic systems from test data. A new approach is introduced in conjunction with the singular value decomposition technique to derive the basic formulation of minimum order realization which is an extended version of the Ho-Kalman algorithm. The basic formulation is then transformed into modal space for modal parameter identification. Two accuracy indicators are developed to quantitatively identify the system modes and noise modes. For illustration of the algorithm, examples are shown using simulation data and experimental data for a rectangular grid structure.

/pdf/an-eigensystem-realization-algorithm-for-modal-parameter-1d3nimncg6.pdf

An eigensystem realization algorithm for modal parameter identification and model reduction

This article reviews the development of the original modal assurance criterion (MAC) together with other related assurance criteria that have been proposed over the last twenty years. Some of the other assurance criteria that will be discussed include the coordinate modal assurance criterion (COMAC), the frequency response assurance criterion (FRAC), coordinate orthogonality check (CORTHOG), frequency scaled modal assurance criterion (FMAC), partial modal assurance criterion (PMAC), scaled modal assurance criterion (SMAC), and modal assurance criterion using reciprocal modal vectors (MACRV). In particular, the thought process that relates the original MAC development to ordinary coherence and to orthogonality computations will be explained. Several uses of MAC that may not be obvious to the casual observer (modal parameter estimation consistency diagrams and model updating are two examples) will be identified. The common problems with the implementation and use of modal assurance criterion computations will also be identified. The development of the modal assurance criterion 1-2 over twenty years ago has led to a number of similar assurance criteria used in the area of experimental and analytical structural dynamics. It is important to recognize the mathematical similarity of these varied criteria in order to be certain that conclusions be correctly drawn from what is essentially a squared, linear regression correlation coefficient. The modal assurance criterion is a statistical indicator, just like ordinary coherence, which can be very powerful when used correctly but very misleading when used incorrectly. This article will first review the historical development of the modal assurance criterion. Other similar assurance criteria will then be identified although the list is not intended to be comprehensive. Typical uses of the modal assurance criterion will be discussed and finally, typical abuses will be identified.

The modal assurance criterion–twenty years of use and abuse

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

A carbon nanotube polymer material was used to form a piezoresistive strain sensor for structural health monitoring applications. The polymer improves the interfacial bonding between the nanotubes. Previous single walled carbon nanotube buckypaper sensors produced distorted strain measurements because the van der Waals attraction force allowed axial slipping of the smooth surfaces of the nanotubes. The polymer sensor uses larger multi-walled carbon nanotubes which improve the strain transfer, repeatability and linearity of the sensor. An electrical model of the nanotube strain sensor was derived based on electrochemical impedance spectroscopy and strain testing. The model is useful for designing nanotube sensor systems. A biomimetic artificial neuron was developed by extending the length of the sensor. The neuron is a long continuous strain sensor that has a low cost, is simple to install and is lightweight. The neuron has a low bandwidth and adequate strain sensitivity. The neuron sensor is particularly useful for detecting large strains and cracking, and can reduce the number of channels of data acquisition needed for the health monitoring of large structures.

https://iopscience.iop.org/article/10.1088/0964-1726/15/3/009/pdf

A carbon nanotube strain sensor for structural health monitoring

Parameter Estimation Techniques for Modal Analysis

Complex Mode Indication Function and its Applications to Spatial Domain Parameter Estimation

A unified matrix polynomial approach to modal identification

Structural frequency response testing also known as “modal analysis” is becoming an integral part of the development and testing of a wide range of industrial and consumer products. It is an essential tool for the definition and solution of many types of structural dynamics problems, such as fatigue, vibration, and noise. This paper discusses one of the most useful techniques for experimental structural frequency response testing—one based upon excitation of the structure with an impulsive force. In many situations, this is the simplest and fastest of the various techniques commonly used today. However, the nature of the excitation and response signals in the impulse technique requires special signal processing techniques if accurate frequency response measurements are to be obtained. This paper discusses the application of the impulse technique and reviews the special problems encountered in practice and the techniques that have been developed for dealing with these problems.

Randall J. Allemang

Papers

The modal assurance criterion–twenty years of use and abuse

Parameter Estimation Techniques for Modal Analysis

Complex Mode Indication Function and its Applications to Spatial Domain Parameter Estimation

A unified matrix polynomial approach to modal identification

Impulse technique for structural frequency response testing