https://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf

Deep learning in neural networks

A review on machinery diagnostics and prognostics implementing condition-based maintenance

This book is downloadable from http://www.irisa.fr/sisthem/kniga/. Many monitoring problems can be stated as the problem of detecting a change in the parameters of a static or dynamic stochastic system. The main goal of this book is to describe a unified framework for the design and the performance analysis of the algorithms for solving these change detection problems. Also the book contains the key mathematical background necessary for this purpose. Finally links with the analytical redundancy approach to fault detection in linear systems are established. We call abrupt change any change in the parameters of the system that occurs either instantaneously or at least very fast with respect to the sampling period of the measurements. Abrupt changes by no means refer to changes with large magnitude; on the contrary, in most applications the main problem is to detect small changes. Moreover, in some applications, the early warning of small - and not necessarily fast - changes is of crucial interest in order to avoid the economic or even catastrophic consequences that can result from an accumulation of such small changes. For example, small faults arising in the sensors of a navigation system can result, through the underlying integration, in serious errors in the estimated position of the plane. Another example is the early warning of small deviations from the normal operating conditions of an industrial process. The early detection of slight changes in the state of the process allows to plan in a more adequate manner the periods during which the process should be inspected and possibly repaired, and thus to reduce the exploitation costs.

Detection of abrupt changes: theory and application

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

Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy—a survey and some new results

A new complete parametric approach for eigenstructure assignment in controllable linear descriptor systems, E[xdot] = Ax + Bu via the static state feedback control u = Kx, is proposed. General complete parametric expressions in direct closed forms for the closed-loop eigenvectors associated with the finite closed-loop eigenvalues are presented, based on a recently proposed complete parametric solution to the generalized Sylvester matrix equation AV + BW = EVF. These expressions are linear in a group of parameter vectors which represent the degree of the design freedom. A very simple complete parametric solution for the feedback gain matrix K is also established with the help of a proposed ‘completing the square’ technique. The approach guarantees arbitrary assignment of rank(E) number finite closed-loop eigenvalues with arbitrary given algebraic and geometric multiplicities, and realizes elimination of all possible initial time impulsive responses. It also guarantees the closed-loop regularity when two si...

Eigenstructure assignment in descriptor systems via state feedback-a new complete parametric approach

Optimal Selection of Unknown Input Distribution Matrix in the Design Of Robust Observers for Fault Diagnosis

An explicit parametric expression of the controller gain matrix in linear multi-input multi-output state-variable feedback system design using eigenstructure assignment is studied. A novel method of using singular value decomposition is presented for the case where the sets of closed-loop and open-loop real and complex-conjugate eigenvalues intersect.

Parametric state feedback controller design of multivariable systems

Modeling uncertainty can be described as an additional term in the dynamic equation of a system that has a certain structure. Based on this structure, a robust fault diagnosis scheme can be designed. The authors present new methods for determining the structure of modeling uncertainties based on deconvolution of unknown inputs. Several theoretical conditions are discussed, and a jet engine system is used to illustrate the method. Simulation results show the power of this method for modeling uncertainties for the purpose of robust fault diagnosis. >

Modelling of uncertainties for robust fault diagnosis

This work is motivated by the challenge to develop an adaptive strategy for systems that are complex, have actuator faults and are difficult to control using linear methods. The novelty lies in combined use of LPV fault estimation and LPV fault compensation to meet active FTC performance requirements. The paper proposes a new design approach for systems which can be characterized via sets of LMIs and can be obtained using efficient interior-point algorithms. A polytopic LPV estimator is synthesized for generating actuator fault estimates used in an FTC scheme to schedule the nominal system state feedback gain, thereby maintaining the system performance over a wide operating range within a proposed polytopic model. The active FTC controller is a function of fault effect factors derived on-line. The effectiveness of the proposed method is demonstrated through a nonlinear two-link manipulator system with torque input faults at each joint.

Ron J. Patton

Papers

Eigenstructure assignment in descriptor systems via state feedback-a new complete parametric approach

Optimal Selection of Unknown Input Distribution Matrix in the Design Of Robust Observers for Fault Diagnosis

Parametric state feedback controller design of multivariable systems

Modelling of uncertainties for robust fault diagnosis

LPV fault estimation and FTC of a two-link manipulator