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

Component fault diagnosis using wavelet neural networks with local recurrent structure

This paper investigates the development of a Supervisory Control System with qualitative tasks at the upper level and simple quantitative models at the lower level to control complex non-linear systems. A new quantitative approach for the stability of non-linear fuzzy inference systems using Takagi-Sugeno (T-S) fuzzy models is presented. The necessary conditions for the assignability of eigenvalues to a region in the s-plane and the necessary conditions to guarantee the stability of fuzzy models for both T-S fuzzy observers and T-S fuzzy controllers together are derived. The problem is solved via the Linear Matrix Inequality (LMI) method. The paper demonstrates the feasibility of this system architecture through application to a fault-tolerant design for a railway traction system using DSP in a hardware test-rig.

A multiple model approach to fault-tolerant control using Takagi-Sugeno fuzzy modelling: Real application to an induction motor drive system

Information Fusion in Fault Diagnosis Based on B-Spline Networks

Using a Bilinear Motor Model for a Sensor Fault Tolerant Rail Traction Drive

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The last decade has seen a rapid increase and convergence in the use of (a) digital network technology, (b) embedded systems and (c) fault analysis and on-line diagnosis. This has resulted in an increase in complexity of the resultant system which must not only ensure performance but also monitor and compensate for faults and hence avoid total failure. The advances made have also facilitated a plug-and-play (PnP) characteristic of networked embedded systems. This paper provides (i) a suitable architecture for the fault tolerant operation and (ii) enables the above PnP feature.

Ron J. Patton

Papers

Component fault diagnosis using wavelet neural networks with local recurrent structure

A multiple model approach to fault-tolerant control using Takagi-Sugeno fuzzy modelling: Real application to an induction motor drive system

Information Fusion in Fault Diagnosis Based on B-Spline Networks

Using a Bilinear Motor Model for a Sensor Fault Tolerant Rail Traction Drive

Reconfiguration in Networked Control Systems: Fault Tolerant Control and Plug-and-Play