Optimal Detection of New Classes of Faults by an Invasive Weed Optimization Method

International audienc

An integrated imputation-prediction scheme for prognostics of battery data with missing observations

International audienc

Dynamic Weighting Ensembles for Incremental Learning and Diagnosing New Concept Class Faults in Nuclear Power Systems

International audienceKey requirements for the practical implementation of empirical diagnostic systems are the capabilities of incremental learning of new information that becomes available, detecting novel concept classes and diagnosing unknown faults in dynamic applications. In this paper, a dynamic weighting ensembles algorithm, called Learn++.NC, is adopted for fault diagnosis. The algorithm is specially designed for efficient incremental learning of multiple new concept classes and is based on the dynamically weighted consult and vote (DW-CAV) mechanism to combine the classifiers of the ensemble. The detection of unseen classes in subsequent data is based on thresholding the normalized weighted average of outputs (NWAO) of the base classifiers in the ensemble. The detected unknown classes are classified as unlabeled until their correct labels can be assigned. The proposed diagnostic system is applied to the identification of simulated faults in the feedwater system of a boiling water reactor (BWR)

ENSEMBLE OF NEURAL NETWORKS FOR DETECTION AND CLASSIFICATION OF FAULTS IN NUCLEAR POWER SYSTEMS

In this work, an ensemble of neural networks is built by an algorithm called Learn++.NC and applied for fault detection and diagnosis. The algorithm is capable of incrementally learning new classes of faults, while retaining the previously acquired knowledge. The detection of new classes in subsequent data is achieved by thresholding the normalized weighted average of outputs (NWAO) of the neural networks in the ensemble. The unknown classes detected remain unlabeled until their correct labels can be assigned. The proposed method is applied to the identification of simulated faults in the feedwater system of a Boiling Water Reactor (BWR)

A Method for Estimating the Confidence in the Identification of Nuclear Transients by a Bagged Ensemble of FCM Classifiers

International audienceThe performance of diagnostic systems based on empirical models may vary in different zones of the training space. It is, thus, important to a-priori verify whether the model is working in a zone where the performance is expected to be satisfactory. In this respect, the objective of this work is to estimate the degree of confidence in the identification of nuclear transients by a diagnostic system based on a bagged ensemble of Supervised Fuzzy C-Means (FCM) classifiers. The method has been applied for classifying simulated transients in the feedwater system of a nuclear Boiling Water Reactor (BWR). The obtained results indicate that the bagging ensemble permits to achieve satisfactory performance, with a reliable estimation of the degree of confidence in the classification

Classifier-Ensemble Incremental-Learning Procedure for Nuclear Transient Identification at Different Operational Conditions

International audienceAn important requirement for the practical implementation of empirical diagnostic systems is the capability of classifying transients in all plant operational conditions. The present paper proposes an approach based on an ensemble of classifiers for incrementally learning transients under different operational conditions. New classifiers are added to the ensemble where transients occurring in new operational conditions are not satisfactorily classified. The construction of the ensemble is made by bagging; the base classifier is a supervised Fuzzy C Means (FCM) classifier whose outcomes are combined by majority voting. The incremental learning procedure is applied to the identification of simulated transients in the feedwater system of a Boiling Water Reactor (BWR) under different reactor power levels

Genetic Algorithm-Based Sliding Mode Control of a Human Arm Model

Spinal cord injured patients cannot move their segments by their intact muscles. A suitable controller can be used to help them move their arm. In this study, the kinematics and dynamics of right-hand movement are modeled considering planar three links. A genetic algorithm-based sliding mode (GASM) controller is designed to move the human arm model for tracking a desired trajectory in the sagittal plane. The GA is used to tune the convergence rate of the sliding mode controller for having an appropriate tracking performance. The summation of errors is considered as a cost function and GA is proposed to find the controller gains to minimize the difference between the outputs of the model and nominal trajectories. To the best of the author\u27s knowledge, it is for the first time that the GA-sliding mode controller has been used for controlling the human hand so as to have a particular movement. Simulation results are evaluated in upward and downward movements of the human arm to affirm the effectiveness of the proposed controller

Uncertainty-Aware Management of Smart Grids Using Cloud-Based LSTM-Prediction Interval

This article introduces an uncertainty-aware cloud-fog-based framework for power management of smart grids using a multiagent-based system. The power management is a social welfare optimization problem. A multiagent-based algorithm is suggested to solve this problem, in which agents are defined as volunteering consumers and dispatchable generators. In the proposed method, every consumer can voluntarily put a price on its power demand at each interval of operation to benefit from the equal opportunity of contributing to the power management process provided for all generation and consumption units. In addition, the uncertainty analysis using a deep learning method is also applied in a distributive way with the local calculation of prediction intervals for sources with stochastic nature in the system, such as loads, small wind turbines (WTs), and rooftop photovoltaics (PVs). Using the predicted ranges of load demand and stochastic generation outputs, a range for power consumption/generation is also provided for each agent called ``preparation range\u27\u27 to demonstrate the predicted boundary, where the accepted power consumption/generation of an agent might occur, considering the uncertain sources. Besides, fog computing is deployed as a critical infrastructure for fast calculation and providing local storage for reasonable pricing. Cloud services are also proposed for virtual applications as efficient databases and computation units. The performance of the proposed framework is examined on two smart grid test systems and compared with other well-known methods. The results prove the capability of the proposed method to obtain the optimal outcomes in a short time for any scale of grid

Quadrotor Attitude and Altitude Tracking Control Using Finite Discrete-Time Linear Quadratic Tracking Controller

In this paper, an optimal finite discrete-time linear quadratic tracking (LQT) control method is proposed to control the altitude and attitude of a quadrotor. First, the dynamic model of the quadrotor is derived using Newton-Euler equations. Next, non-linear equations of the quadrotor are written in the state space form and linearized around an equilibrium point. Then, continuous-time linear state-space equations are converted into discrete-time equations considering a specific sampling time. Moreover, the controller design process is completed by determining the performance index and the weighting matrices, and the optimal control input is acquired for the closed-loop system. In the end, the simulation results are shown to demonstrate the robustness of the controller against parameter uncertainties and show its performance in attenuating the external disturbance effect