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

Bone remodelling is a vital process which enables bone to repair, renew and optimize itself. Disorders in the bone remodelling process are inevitably manifested in bone-related diseases, such as hypothyroidism, primary hyperparathyroidism and osteoporosis. In our previous work, a predator–prey based mathematical model was developed to simulate bone remodelling cycles under normal and two pathological conditions, hypothyroidism and primary hyperparathyroidism, for trabecular bone at a fixed point. However, the biochemical meanings of the model parameters were not fully explored. This article first extends the previous work by proposing relationships between the model parameters and biochemical factors involved in the bone remodelling process and by examining whether those relationships do predict the behaviours observed in vivo. The model is then applied to the simulation and investigation of bone remodelling of postmenopausal osteoporosis. The proposed connections are supported by good agreement between t...

A predator-prey based mathematical model of the bone remodelling cycle: exploring the relationship between the model parameters and biochemical factors.

Monte-Carlo Reliability and Performance Analysis of Satellite FDI System

This paper is concerned with generic parametric controller design via output-feedback eigenstructure assignment for linear multivariable systems. A new parametric expression of closed-loop eigenvectors and generalized eigenvectors is developed for both real eigenvalue and complex-conjugate eigenvalue cases. It also considers the case where the closed-loop eigenvalues are multiple and/or the same as the open-loop ones so that the system to be designed can be uncontrollable and/or unobservable. The controller designed via output-feedback eigenstructure assignment is expressed by new proposed parameter vectors. The freedom provided by output-feedback eigenstructure assignment is used to optimize a performance function which is used to measure the sensitivity of the closed-loop system matrix.

Generic parametric controller design using output feedback eigenstructure assignment

Robust Fault Detection Observer Design using H∞/µ Techniques for Uncertain Flight Control Systems

This work is motivated by the challenge to develop an adaptive strategy for systems that are complex, have actuator faults and are difficult to control using linear methods. The novelty lies in combined use of LPV fault estimation and LPV fault compensation to meet active FTC performance requirements. The paper proposes a new design approach for systems which can be characterized via sets of LMIs and can be obtained using efficient interior-point algorithms. A polytopic LPV estimator is synthesized for generating actuator fault estimates used in an active FTC scheme to schedule the nominal system state feedback gain as a function of fault effect factors , thereby maintaining the system performance over a wide operating range within a proposed polytopic model. The method is demonstrated through a nonlinear two-link manipulator system with torque input faults at each joint.

/pdf/an-lpv-approach-to-active-ftc-of-a-two-link-manipulator-piqdiognqv.pdf

Ron J. Patton

Papers

A predator-prey based mathematical model of the bone remodelling cycle: exploring the relationship between the model parameters and biochemical factors.

Monte-Carlo Reliability and Performance Analysis of Satellite FDI System

Generic parametric controller design using output feedback eigenstructure assignment

Robust Fault Detection Observer Design using H∞/µ Techniques for Uncertain Flight Control Systems

An LPV Approach to Active FTC of a Two-Link Manipulator