Pronostic de défaillances : Maîtrise de l'erreur de prédiction.

International audienceLe travail rapporté ici traite globalement de la spécification et du développement d'un système de pronostic de défaillances. De ce point de vue, beaucoup de développements visant la proposition de méthodes de prévision existent dans la littérature. La majorité d'entre elles portent sur la construction de modèles capables de minimiser l'erreur de prédiction d'une situation future. Cependant, peu traitent de la maitrise de cette erreur. C'est ce qui fait l'objet de ce papier et pour lequel nous proposons d'exploiter le système ANFIS (système d'inférence floue paramétré par apprentissage neuronal). Après avoir positionné l'activité de pronostic dans le cadre de la maintenance industrielle, nous présentons le réseau ANFIS. Nous étudions les pistes permettant de maîtriser l'erreur de prédiction d'un tel système, notamment lors de la phase d'apprentissage (optimisation des paramètres du réseau). Les éléments théoriques nécessaires à cette analyse sont décrits, une nouvelle fonction de coût est proposée et l'influence de celle-ci sur les performances du réseau est discutée. Nous illustrons l'ensemble sur un benchmark. La modification proposée permet de réduire la phase d'apprentissage du système de pronostic

Semi-Active Adaptive Control of Coupled Structures for Seismic Hazard Mitigation

The research presented in this dissertation examines innovative structures connected with smart control devices driven by adaptive control methods. The research focuses on understanding the dynamics of coupled structures and evaluating the merits of adaptive control in enhancing the seismic performance of these structures and dealing with uncertainties. Coupled structures is recognized as an effective strategy to protect civil structures from seismic excitations. Coupling of adjacent structures has proved to offer functional benefits such as the potential for shifting the buildings’ natural frequencies, likely leading to a reduction in the natural period of vibration. Structural performance is further enhanced by implementing energy-dissipative devices to connect adjacent buildings to minimize the seismic structural responses. One of the main challenges to control civil structures is the high uncertainty involved throughout their lifetimes. Adaptive control promises to deal with changes in structures’ characteristics, such as seismic-induced damage. The simple adaptive control method, which is a reference-model following scheme, is used in the current research to improve the seismic behavior of adjacent buildings connected by structural links where control devices are implemented. The philosophy of the simple adaptive control method is that an actual system (often called plant) can be forced to track the behavior of pre-determined trajectories through adjustable adaptive gains. The effectiveness of the simple adaptive controller in reducing the seismic responses is compared with other adaptive and non-adaptive control methods. The results reveal that the simple adaptive controller is effective in alleviating the structural responses and dealing with uncertainties of coupled structures with both linear and nonlinear behavior. The results also show that the coupling strategy is viable for reducing the structural responses under seismic excitations

A comprehensive comparison of the performance of metaheuristic algorithms in neural network training for nonlinear system identification

Many problems in daily life exhibit nonlinear behavior. Therefore, it is important to solve nonlinear problems. These problems are complex and difficult due to their nonlinear nature. It is seen in the literature that different artificial intelligence techniques are used to solve these problems. One of the most important of these techniques is artificial neural networks. Obtaining successful results with an artificial neural network depends on its training process. In other words, it should be trained with a good training algorithm. Especially, metaheuristic algorithms are frequently used in artificial neural network training due to their advantages. In this study, for the first time, the performance of sixteen metaheuristic algorithms in artificial neural network training for the identification of nonlinear systems is analyzed. It is aimed to determine the most effective metaheuristic neural network training algorithms. The metaheuristic algorithms are examined in terms of solution quality and convergence speed. In the applications, six nonlinear systems are used. The mean-squared error (MSE) is utilized as the error metric. The best mean training error values obtained for six nonlinear systems were 3.5×10−4, 4.7×10−4, 5.6×10−5, 4.8×10−4, 5.2×10−4, and 2.4×10−3, respectively. In addition, the best mean test error values found for all systems were successful. When the results were examined, it was observed that biogeography-based optimization, moth–flame optimization, the artificial bee colony algorithm, teaching–learning-based optimization, and the multi-verse optimizer were generally more effective than other metaheuristic algorithms in the identification of nonlinear systems

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM

Navigational Strategies for Control of Underwater Robot using AI based Algorithms

Autonomous underwater robots have become indispensable marine tools to perform various tedious and risky oceanic tasks of military, scientific, civil as well as commercial purposes. To execute hazardous naval tasks successfully, underwater robot needs an intelligent controller to manoeuver from one point to another within unknown or partially known three-dimensional environment. This dissertation has proposed and implemented various AI based control strategies for underwater robot navigation. Adaptive versions of neuro-fuzzy network and several stochastic evolutionary algorithms have been employed here to avoid obstacles or to escape from dead end situations while tracing near optimal path from initial point to destination of an impulsive underwater scenario. A proper balance between path optimization and collision avoidance has been considered as major aspects for evaluating performances of proposed navigational strategies of underwater robot. Online sensory information about position and orientation of both target and nearest obstacles with respect to the robot’s current position have been considered as inputs for path planners. To validate the feasibility of proposed control algorithms, numerous simulations have been executed within MATLAB based simulation environment where obstacles of different shapes and sizes are distributed in a chaotic manner. Simulation results have been verified by performing real time experiments of robot in underwater environment. Comparisons with other available underwater navigation approaches have also been accomplished for authentication purpose. Extensive simulation and experimental studies have ensured the obstacle avoidance and path optimization abilities of proposed AI based navigational strategies during motion of underwater robot. Moreover, a comparative study has been performed on navigational performances of proposed path planning approaches regarding path length and travel time to find out most efficient technique for navigation within an impulsive underwater environment

Intelligente Regelung von nichtlinearen elektromechanischen Systemen

Magdeburg, Univ., Fak. für Elektrotechnik und Informationstechnik, Diss., 2011von Yuriy Tsepkovski

Development of new methodologies for the weight estimation of aircraft structures

The problem of weight estimation in the aerospace industry has been acquiring considerably greater importance in recent years, due to the numerous challenges frequently encountered in the preliminary phases of the design of a new aircraft. This is the stage where it is possible to make design changes without incurring into excessive cost penalties. On the other hand, the knowledge of the design, of the relationships existing between the different variables and their subsequent impact on the final weight of the structure is very limited. As a result, the designer is unable to understand the true effect that individual design decisions will produce on the weight of the structure. In addition to this, new aircraft concepts end up being too conservative, due to the high dependency of current weight estimation methods to historical data and off-the-shelf design solutions. This thesis aims at providing an alternative framework for the weight estimation of aircraft structures at preliminary design stages. By conducting a thorough assessment of current state-of-the-art approaches and tools used in the field, fuzzy logic is presented as an appropriate foundation on which to build an innovative approach to the problem. Different adaptive fuzzy approaches have been used in the development of a methodology which is able to combine an analytical base to the structural design of selected trailing edge components, with substantial knowledge acquisition capabilities for the computation of robust and reliable weight estimates. The final framework allows considerable flexibility in the level of detail of the estimate consistent with the granularity of the input data used. This, combined with an extensive uncertainty analysis through the use of Interval Type-2 fuzzy logic, will provide the designer with the capabilities to understand the impact of error propagation within the model and increase the confidence in the final estimat