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 paper investigates the development of a new type of recurrent wavelet neural network and its application to fault detection and isolation (FDI) of a dynamic process. Hybrid learning based on c-means fuzzy clustering algorithm and the steepest-descent method, is used to train the proposed neural network. The experimental case study concerns the sensor and actuator fault diagnosis of a sub-system from the evaporation station of a sugar factory, namely the evaporator. A neural generalised observer scheme is used to generate the residuals (symptoms) in the form of one step-ahead prediction errors. These are then processed by a neural classifier in order to take the appropriate decision regarding the type of the behaviour of the process (normal or abnormal).

Recurrent wavelet neural networks applied to fault diagnosis

Fuzzy Observers for FDI: Application to Bilinear Systems

Vehicle platoon systems are considered as automatous vehicles in a platoon-based driving pattern in which a following vehicle follows the preceding vehicle and maintains the desired vehicle spacing. This article investigates the leader-following tracking issue of vehicle platoon systems under cyber-physical threats with the distributed antiattack fault-tolerant tracking control strategy. In this study, vehicle platoon systems, complicated actuator faults in physical layer, and connectivity-mixed attacks in the cyber layer are modeled, respectively. Decentralized fault-estimation unknown input observer and distributed antiattack fault-tolerant tracking control designs are developed in an integrated control framework to guarantee the robust and resilient tracking property of estimation errors and platoon tracking errors as well as the reliable intervehicle spacing by virtue of attack activation rate and attack frequency metrics. Simulations validate the proposed distributed antiattack fault-tolerant tracking control algorithm in pernicious cyber-physical threatened scenarios.

Distributed Antittack Fault-Tolerant Tracking Control for Vehicle Platoon Systems Under Cyber-Physical Threats

This paper presents a novel approach to integrating quantitative and qualitative information in fault-diagnosis. This paper investigates the development of a supervisory control scheme for a non-linear system with qualitative tasks at the upper level and a lower level comprising quantitative model based non-linear control. A new quantitative approach for the stability of non-linear fuzzy inference systems using Takagi-Sugeno (T-S) fuzzy models is presented. The necessary conditions for the assignability of eigenvalues to a region in the s-plane and the necessary conditions to guarantee the joint stability of fuzzy models for combined T-S fuzzy observers and T-S fuzzy controllers together are derived.

Ron J. Patton

Papers

Recurrent wavelet neural networks applied to fault diagnosis

Fuzzy Observers for FDI: Application to Bilinear Systems

Distributed Antittack Fault-Tolerant Tracking Control for Vehicle Platoon Systems Under Cyber-Physical Threats

Supervisory fault tolerant system using fuzzy multiple inference modelling

Fault-Tolerant Traction System Control Using Fuzzy Inference Modelling