Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practical applications.  

An unscented Kalman filter in designing dynamic GMDH neural networks for robust fault detection

This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. Moreover, a new training algorithm of the neural network based on the unscented Kalman filter is presented. The final part of the work contains an illustrative example regarding the application of the proposed approach to robust fault detection of a tunnel furnace

Fault-tolerant tracking control for a non-linear twin-rotor system under ellipsoidal bounding

A novel fault-tolerant tracking control scheme based on an adaptive robust observer for non-linear systems is proposed. Additionally, it is presumed that the non-linear system may be faulty, i.e., affected by actuator and sensor faults along with the disturbances, simultaneously. Accordingly, the stability of the robust observer as well as the fault-tolerant tracking controller is achieved by using the ℋ∞ approach. Furthermore, unknown actuator and sensor faults and states are bounded by the uncertainty intervals for estimation quality assessment as well as reliable fault diagnosis. This means that narrow intervals accompany better estimation quality. Thus, to cope with the above difficulty, it is assumed that the disturbances are over-bounded by an ellipsoid. Consequently, the performance and correctness of the proposed fault-tolerant tracking control scheme are verified by using a non-linear twin-rotor aerodynamical laboratory system

Neural network-based robust actuator fault diagnosis for a non-linear multi-tank system

International audienceThe paper is devoted to the problem of the robust actuator fault diagnosis of the dynamic non-linear systems. In the proposed method, it is assumed that the diagnosed system can be modelled by the recurrent neural network, which can be transformed into the linear parameter varying form. Such a system description allows developing the designing scheme of the robust unknown input observer within H∞ framework for a class of non-linear systems. The proposed approach is designed in such a way that a prescribed disturbance attenuation level is achieved with respect to the actuator fault estimation error, while guaranteeing the convergence of the observer. The application of the robust unknown input observer enables actuator fault estimation, which allows applying the developed approach to the fault tolerant control tasks

Towards a health-aware fault tolerant control of complex systems: A vehicle fleet case

The paper deals with the problem of health-aware fault-tolerant control of a vehicle fleet. In particular, the development process starts with providing the description of the process along with a suitable Internet-of-Things platform, which enables appropriate communication within the vehicle fleet. It also indicates the transportation tasks to the designated drivers and makes it possible to measure their realization times. The second stage pertains to the description of the analytical model of the transportation system, which is obtained with the max-plus algebra. Since the vehicle fleet is composed of heavy duty machines, it is crucial to monitor and analyze the degradation of their selected mechanical components. In particular, the components considered are ball bearings, which are employed in almost every mechanical transportation system. Thus, a fuzzy logic Takagi–Sugeno approach capable of assessing their time-to-failure is proposed. This information is utilized in the last stage, which boils down to health-aware and fault-tolerant control of the vehicle fleet. In particular, it aims at balancing the exploitation of the vehicles in such a way as to maximize they average time-to-failure. Moreover, the fault-tolerance is attained by balancing the use of particular vehicles in such a way as to minimize the effect of possible transportation delays within the system. Finally, the effectiveness of the proposed approach is validated using selected simulation scenarios involving vehicle-based transportation tasks

A predictive actuator fault-tolerant control strategy under input and state constraints

International audienceThe paper deals with the design of a robust predictive fault-tolerant control for linear discrete-time systems with an applicationof the quadratic boundedness theory and an associated robust invariant set. The main problem is to maintain the state of thesystem inside the robust invariant set obtained under asymmetric input and state constraints. The proposed strategy relies on athree-stage procedure, which is based on adaptive fault estimation as well as robust and predictive controller. The fault-recovery procedure is initiated with fault estimation and then the fault is compensated with a robust controller. In a case when robust fault compensation fails, i.e. the current state does not belong to the robust invariant set, a suitable predictive action is started. The main goal of this action is to generate control allocation enhancing the robust invariant set. This appealing phenomenon makes it possible to enlarge the domain of attraction of the possibly faulty system. The final part of the paper shows an illustrative example regarding a two-tank system

Procedural Method for Fast Table Mountains Modelling in Virtual Environments

Natural terrains created by long-term erosion processes can sometimes have spectacular forms and shapes. The visible form depends often upon internal geological structure and materials. One of the unique terrain artefacts occur in the form of table mountains and can be observed in the Monument Valley (Colorado Plateau, USA). In the following article a procedural method is considered for terrain modelling of structures, geometrically similar to the mesas and buttes hills. This method is not intended to simulate physically inspired erosion processes, but targets directly the generation of eroded forms. The results can be used as assets by artists and designers. The proposed terrain model is based on a height-field representation extended by materials and its hardness information. The starting point of the technique is the Poisson Faulting algorithm that was originally used to obtain fractional Brownian surfaces. In the modification, the step function as the fault line generator was replaced with a circular one. The obtained geometry was used for materials’ classification and the hardness part of the modelled terrain. The final model was achieved by the erosive modification of geometry according to the materials and its hardness data. The results are similar to the structures observed in nature and are achieved within an acceptable time for real-time interactions

A robust H8 observer design for unknown input nonlinear systems: Application to fault diagnosis of a wind turbine

The paper is devoted to the problem of designing robust unknown input observer (UIO) for fault estimation purpose. The proposed approach is based on the Takagi-Sugeno models which can be effectively applied for modelling of the wide class of non-linear systems. It also revisits the recent results proposed in the literature and provides a less restrictive design procedure of a robust UIO. In particular, the general UIO strategy and the 8 framework are provided to design a robust fault estimation methodology. The resulting design procedure guarantees that a prescribed disturbance attenuation level is achieved with respect to the state estimation error. The main advantage of the proposed approach boils down to its simplicity because it reduces to solving a set of Linear Matrix Inequalities (LMIs). The final part of the paper presents an illustrative example devoted to the fault estimation of a three blade 1 MW variable-speed, variable-pitch wind turbinePostprint (author's final draft

A robust H1 observer design for unknown input nonlinear systems: Application to fault diagnosis of a wind turbine

The paper is devoted to the problem of designing robust unknown input observer (UIO) for fault estimation purpose. The proposed approach is based on the Takagi-Sugeno models which can be effectively applied for modelling of the wide class of non-linear systems. It also revisits the recent results proposed in the literature and provides a less restrictive design procedure of a robust UIO. In particular, the general UIO strategy and the H1 framework are provided to design a robust fault estimation methodology. The resulting design procedure guarantees that a prescribed disturbance attenuation level is achieved with respect to the state estimation error. The main advantage of the proposed approach boils down to its simplicity because it reduces to solving a set of Linear Matrix Inequalities (LMIs). The final part of the paper presents an illustrative example devoted to the fault estimation of a three blade 1 MW variable-speed, variable-pitch wind turbine.Postprint (published version