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

The effect of osteocyte apoptosis on signalling in the osteocyte and bone lining cell network: a computer simulation.

Industrial Actuator Benchmark for Fault Detection and Isolation

This paper studies the robust fault diagnosis of stochastic systems with unknown disturbances based on a full order observer. This observer can give disturbance decoupling minimum variance state estimation for time-varying systems with both noise and unknown disturbances. The existence condition and the design procedure are presented in the paper. The output estimation error with disturbance decoupling and minimum variance properties is used as a residual signal to diagnose faults. The developed method is applied to an illustrative example and simulation results show that the approach taken is able to detect faults reliably in the presence of both modelling errors and noise. >

Robust fault diagnosis of stochastic systems with unknown disturbances

This paper proposes an active fault-tolerant control scheme based on a gain-scheduled H∞ design strategy. Under the assumption that the effects of faults on the system can be of affine parameter dependence, a reconfigurable robust H∞ controller is developed. The resulting controller is a function of the fault effect factors, which can be derived online from the residual vector of the fault detection and isolation (FDI) mechanism. To demonstrate the effectiveness of the proposed method, a non-linear double inverted pendulum system with a fault in the motor tachometer loop is considered. The adaptive fault-tolerant controller recovers well from the unstable system with loop failure.

Active fault-tolerant control of a double inverted pendulum

This paper presents a novel fault detection and isolation scheme for non-linear dynamic systems. This scheme utilizes a fuzzy observer to generate the diagnostic residual signal for fault detection and isolation. This fuzzy observer, based upon the idea of Takagi-Sugeno fuzzy models, comprises a number of locally linear observers and the final state estimate is a fuzzy fusion of all local observer outputs. To ensure good estimation performance, the eigenvalues of the fuzzy observer are assigned in a pre-defined region in the complex plane. The stability as well as eigenvalue constraint conditions for the fuzzy observer design are presented and solved in the linear matrix inequality framework in this paper. Finally, the paper demonstrates the application of fuzzy observers in detecting and isolating intermittent faults in the induction motor of a railway traction system using a real-time test-rig implementation.

Ron J. Patton

Papers

The effect of osteocyte apoptosis on signalling in the osteocyte and bone lining cell network: a computer simulation.

Industrial Actuator Benchmark for Fault Detection and Isolation

Robust fault diagnosis of stochastic systems with unknown disturbances

Active fault-tolerant control of a double inverted pendulum

Non-linear dynamic systems fault detection and isolation using fuzzy observers: