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

This paper considers robust eigenstructure assignment for multivariable systems. The time-domain performance specifications provided by eigenstructure assignment and robust performance specifications in the frequency domain considered by H infinity control are combined to realize joint optimal robust control design. A parametric expression for state-feedback eigenstructure assignment is introduced on the basis of a set of free parameters. The mixed performance index used as the cost function consists of two parts: robustness in the frequency domain; and system eigenstructure constraints in the time domain. The analytical gradient calculation of the cost function with respect to the free parameters is derived for optimal robust eigenstructure assignment using gradientbased optimization. The mixed time- and frequency-domain robust eigenstructure assignment is demonstrated in the design of a mode-decoupled roll-yaw autopilot.

Mixed time- and frequency-domain robust eigenstructure assignment

This paper illustrates some of the capabilities of previously proposed network control system (NCS) architectures to carry on functioning in the event of faults, without recourse to system reconfiguration. The principle of interaction prediction is used to set up a coordination strategy that encapsulates an ability to withstand or tolerate certain faults, thereby allowing the system to continue functioning. It is also shown that the coordination strategy can be made more effective if a learning agent is allowed to learn the coordination functions. This facilitates the use of different types of agent at the local level, together with recurrent networks and genetic algorithms (GAs) at the coordination level. The experimental test-bed system is a benchmark three-tank system that has some of the main features of an industrial process control system.

An interaction predictive approach to fault-tolerant control in network control systems:

A bounded-error approach to designing unknown input observers

A monte carlo analysis and design for fdi of a satellite attitude control system

This paper outlines the design and initial testing of a quantitative model-based fault detection and isolation (FDI) scheme for a water pumping system The work has been motivated by the desire to develop a fault diagnosis and condition monitoring scheme for an industrial cooling water pumping system in operation at the 'Heysham 2' power station (British Energy) Methods of residual (fault indicator) generation using observers and parameter estimation are appraised as to their suitability for industrial systems, where sensor availability and limited sampling rates must be taken into considera tion A detailed model of the industrial pumping system has been developed and the performance of the methods considered is illustrated using this model Due to the industrial motivation the task of analysing the information provided by complementary methods is described and a practical scheme for the cooling system is proposed which considers the currently available methods and the particular challenges presented by

Ron J. Patton

Papers

Mixed time- and frequency-domain robust eigenstructure assignment

An interaction predictive approach to fault-tolerant control in network control systems:

A bounded-error approach to designing unknown input observers

A monte carlo analysis and design for fdi of a satellite attitude control system

Model-based fault diagnosis of a two-pump system