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 presents a new method for the design of constant gain digital controllers for multirate systems with a multiple input/fixed output sampling rate configuration The technique is based on full-state feedback pole placement Two main problems are addressed; the adverse effects of intersample cross-coupling and the high amplitude control signals that normally result from a multirate feedback structure The trade-off between these two factors is outlined and a method for minimising each as a design parameter is considered An example demonstrates the conflicting design objectives of the multirate feedback control problem

A Robust Approach to Multirate Controller Design using Eigenstructure Assignment

This paper proposes an approach to fault tolerant control (FTC) of a wind turbine subject to sensor faults. Both analytical and hardware redundancies are utilized in this approach. A residual generator based on a Takagi-Sugeno (T-S) fuzzy observer is proposed as the fault detection and isolation (FDI) unit. A T-S fuzzy observer design method via online eigenvalue assignment is proposed. It is shown that single residual can be utilized to identify different sensor faults by analyzing the characteristics of the residual. Model predictive control (MPC) based on T-S fuzzy modeling is proposed as the wind turbine controller to take into account the turbine system nonlinearity and physical constraints of the turbine actuators.

Sensor fault tolerant control of a wind turbine via Takagi-Sugeno fuzzy observer and model predictive control

Offshore wind turbines suffer from asymmetrical loading (blades, tower, etc), leading to enhanced structural fatigue. As well as asymmetrical loading different faults (pitch system faults etc.) can occur simultaneously, causing degradation of load mitigation performance. Individual pitch control (IPC) can achieve rotor asymmetric loads mitigation, but this is accompanied by an enhancement of pitch movements leading to the increased possibility of pitch system faults, which exerts negative effects on the IPC performance. The combined effects of asymmetrical blade and tower bending together with pitch sensor faults are considered as a “co‐design” problem to minimize performance deterioration and enhance wind turbine sustainability. The essential concept is to attempt to account for all the “fault effects” in the rotor and tower systems, which can weaken the load reduction performance through IPC. Pitch sensor faults are compensated by the proposed fault‐tolerant control (FTC) strategy to attenuate the fault effects acting in the control system. The work thus constitutes a combination of IPC‐based load mitigation and FTC acting at the pitch system level. A linear quadratic regulator (LQR)‐based IPC strategy for simultaneous blade and tower loading mitigation is proposed in which the robust fault estimation is achieved using an unknown input observer (UIO), considering four different pitch sensor faults. The analysis of the combined UIO‐based FTC scheme with the LQR‐based IPC is shown to verify the robustness and effectiveness of these two systems acting together and separately.

/pdf/wind-turbine-asymmetrical-load-reduction-with-pitch-sensor-qild91zq58.pdf

Wind turbine asymmetrical load reduction with pitch sensor fault compensation

The robust control design of descriptor systems using eigenstructure assignment is presented in this paper. It considers both the desired closed-loop eigenvalue assignment in the time domain and the minimization of a robustness index in the frequency domain so that the time- and frequency-domain performance specifications are combined in the controller design. An explicit parametric solution for eigenstructure assignment for multivariable descriptor systems is derived by considering both analytical expression and numerical computation. It provides a very useful parametric formulation for optimal robust control design. The robustness index is minimized by making full use of the freedom provided by explicit parametric eigenstructure assignment.

Ron J. Patton

Papers

A Robust Approach to Multirate Controller Design using Eigenstructure Assignment

Sensor fault tolerant control of a wind turbine via Takagi-Sugeno fuzzy observer and model predictive control

Wind turbine asymmetrical load reduction with pitch sensor fault compensation

Robust control design of descriptor systems using eigenstructure assignment

Comparison of Two Techniques of I.F.D. based on a Non-Linear Stochastic Model of an Aircraft