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

Supervisory Takagi-Sugeno Fuzzy Fault-Tolerant Control of a Rail Traction System

Linear Matrix Inequality Formulation Of Fault-Tolerant Control Systems Design

The interconnection of dynamical systems gives rise to interesting challenges for control in terms of stability, robustness and the overall performance of the global interconnected systems as well as the fault tolerance of the individual subsystems. Interconnected systems can be developed either from a standpoint of centrality of control based on the construction and design of a global system that satisfies the above requirements. Alternatively, the interconnected system can be decentralized which means that the stability, performance, etc., requirements are achieved at the local (subsystem) levels. To develop a good “fault-tolerant control” strategy for decentralized systems it is necessary to take account of various faults or uncertainties that may occur throughout all local levels of the system. A powerful way to achieve this is to use robust state and fault estimation methods accounting for the model–reality mismatch that is inevitable when (a) systems are linearized and (b) when faults occur in subsystem components such as actuators, sensors, etc. The chapter develops a strategy for decentralized state and fault estimation based on the Walcott–Żak form of sliding mode observer (SMO) with linear matrix inequality (LMI) formulation. This strategy is shown to be advantageous when considering the estimation problem for a large number of interconnected subsystems. After developing the design procedure a tutorial example of two interconnected linear systems with nonlinear interconnection functions shows that the states as well as actuator and sensor faults can be robustly estimated. Finally, an application-oriented example of a three-machine power system is given which has actuator faults as well as nonlinear machine interconnections.

Sliding Mode State and Fault Estimation for Decentralized Systems

We propose a HOSVD (Higher Order Singular Value Decomposition) based approach to LMI based control design and stability analysis techniques. The proposed method deals with the analytic differential equations of dynamic models and automatically finds their TS model representation. As a subsequent advantage, it optimizes the complexity of the resulted TS model with respect to approximation accuracy. As an example a detailed control design is given.

Automatic TS-model representation of analytic differential equations for Tanaka's LMI based control design

An Integral Sliding Mode approach to hierarchical control of interconnected systems with uncertainties arising from unknown interconnection states and unknown parametric variation is proposed. The work is based on a recent study using constrained LQR showing that a two-level hierarchical control system with global coordination control provides a suitable architecture for control of uncertain interconnected systems, in which the control coordinator seeks to balance the effects of the interaction uncertainties. The new method is viewed as an extension to the constrained LQR approach and a nonlinear tutorial example problem is used to illustrate the comparison.

Ron J. Patton

Papers

Supervisory Takagi-Sugeno Fuzzy Fault-Tolerant Control of a Rail Traction System

Linear Matrix Inequality Formulation Of Fault-Tolerant Control Systems Design

Sliding Mode State and Fault Estimation for Decentralized Systems

Automatic TS-model representation of analytic differential equations for Tanaka's LMI based control design

Integral hierarchical SMC of uncertain interconnected systems