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

In this paper we propose a Fault Detection (FD) filter design method for linear parameter varying (LPV) systems. The FD filter is an optimal Hinfin Luenberger observer synthesized by minimizing frequency conditions which ensure guaranteed levels of disturbance rejection and fault detection. Via the Bounded Real Lemma (BRL) and the Separation Principle the design method is formulated as a convex LMI optimization problem. The resulting residual generator is parameter-dependent and uses the plant parameter assumed measurable on-line. Finally, a FD threshold logic is proposed in order to reduce the generation of false alarms. The effectiveness of the design technique is illustrated via a numerical example.

A Fault Detection filter design method for a class of linear time-varying systems

This paper investigates the position-tracking and attitude-tracking control problem of close formation flight with vortex effects under simultaneous actuator and sensor faults. On the basis of the estimated state and fault information from unknown input observers and relative output information from neighbors, an integration of decentralized fault estimation and distributed fault-tolerant control is developed to deal with bidirectional interactions and to guarantee the asymptotic stability and $H_\infty$ performance of close formations.

/pdf/integrated-fault-tolerant-control-for-close-formation-flight-2tbjb353av.pdf

Integrated Fault-Tolerant Control for Close Formation Flight

This paper presents a new strategy for wind turbine fault tolerant control (FTC) to optimise the wind energy captured by a wind turbine operating at low wind speeds. The FTC strategy uses Takagi-Sugeno (T-S) fuzzy observers with state feedback control to maintain nominal wind turbine control without changes in both the fault and fault-free cases. The proposed strategy obviates the need for sensor fault residual evaluation and observer switching by using a fuzzy proportional multiple integral observer (PMIO) to mask i.e. ‘implicitly compensate’ the sensor fault(s) from the controller input and provide good estimation over a wide range of sensor fault scenarios. The proposed FTC method is applied to a 5 MW offshore wind turbine (OWT) benchmark model.

/pdf/wind-turbine-sensor-fault-tolerant-control-via-a-multiple-1ftwrczorg.pdf

Wind turbine sensor fault tolerant control via a multiple-model approach

A Two-Level Approach to Fault-Tolerant Control of Distributed Systems based on the Sliding Mode

Presents a robust model-based technique for the detection and isolation of sensor faults in a chemical process. The diagnosis system is based on the robust estimation of process outputs. A dynamic non-linear model of the process under investigation is obtained by a procedure exploiting Takagi-Sugeno (T-S) multiple-model fuzzy identification. The combined identification and residual generation schemes have robustness properties with respect to modelling uncertainty, disturbance and measurement noise, providing good sensitivity properties for fault detection and fault isolation. The identified system consists of a fuzzy combination of T-S models to detect changing plant operating conditions. Residual analysis and geometrical tests are then sufficient for fault detection and isolation, respectively. The procedure presented is applied to the problem of detecting and isolating faults in a benchmark simulation of a tank reactor chemical process.

Ron J. Patton

Papers

A Fault Detection filter design method for a class of linear time-varying systems

Integrated Fault-Tolerant Control for Close Formation Flight

Wind turbine sensor fault tolerant control via a multiple-model approach

A Two-Level Approach to Fault-Tolerant Control of Distributed Systems based on the Sliding Mode

Robust fault diagnosis in a chemical process using multiple-model approach