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

To date, one of the main challenges in the wave energy field is to achieve energy-maximizing control in order to reduce the levelized cost of energy (LCOE). This paper presents a model predictive velocity tracking control method based on a hierarchical structure for a Wavestar-like deivce in the WEC-SIM benchmark. The first part of the system structure aims to estimate the wave excitation moment (WEM) by using a Kalman filter. Then, an extended Kalman filter (EKF) is chosen to obtain the amplitude and angular frequency of the WEM in order to compute the reference velocity. Following this, a low-level model predictive control (MPC) method is designed to ensure the wave energy converter (WEC) tracks the optimal reference velocity for maximum energy extraction from irregular waves. Two Gaussian Process (GP) models are considered to predict the future wave excitation moment and future reference velocity, which are needed in MPC design. The proposed strategy can give a new vision for energy-maximizing tracking control based on MPC.

/pdf/model-predictive-energy-maximising-tracking-control-for-a-15kjm9vh.pdf

Model Predictive Energy-Maximising Tracking Control for a Wavestar-Prototype Wave Energy Converter

B-Spline Neuro-Fuzzy Identification and Fault Diagnosis Applied to a Sugar Evaporization Plant

Variable Structure Control of a Static Var Compensator for System Dynamic Stability

A new dynamic neuro-fuzzy system applied to fault diagnosis of an evaporation station

This paper is concerned with multiobjective robust controller design using eigenstructure assignment for multi-variable control systems. It considers various performance indices (or cost functions) in the objectives, which are individual eigenvalue sensitivity functions, and the sensitivity and the complementary sensitivity functions in the frequency domain, instead of a single performance index for a control system. Based on these performance indices, the robustness criteria are expressed by a set of inequalities. The paper will make full use of the freedom provided by eigenstructure assignment to find a controller to satisfy the robustness criteria. A numerical algorithm for multi-objective optimization is developed.

http://ieeexplore.ieee.org/iel7/7055173/7081895/07082152.pdf

Ron J. Patton

Papers

Model Predictive Energy-Maximising Tracking Control for a Wavestar-Prototype Wave Energy Converter

B-Spline Neuro-Fuzzy Identification and Fault Diagnosis Applied to a Sugar Evaporization Plant

Variable Structure Control of a Static Var Compensator for System Dynamic Stability

A new dynamic neuro-fuzzy system applied to fault diagnosis of an evaporation station

Multiobjective robust eigenstructure assignment for multivariable systems