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Support vector machine has obtained more and more attentions as a new method of machine learning based on the statistic learning theory. At the same time, there are increasing concerns about the fault diagnosis for practical engineering systems. Firstly, many kinds of SVM algorithms will be introduced, such as LS-SVM, LSVM and PSVM and so on. Besides, the advantages and disadvantage of those methods...
The PI algorithm has proven to be a popular and widely used control method, due to its relative simplicity and robustness. Despite this, the linear nature of the algorithm means it doesn't provide optimal control to non-linear systems. This paper presents a novel method of improving the performance of the PI controller using an ANFIS network to provide gain scheduling. This control scheme is applied...
This paper presents a self-learning strategy for an artificial cognitive control based on a reinforcement learning strategy, in particular, an on-line version of a Q-learning algorithm. One architecture for artificial cognitive control was initially reported in [1], but without an effective self-learning strategy in order to deal with nonlinear and time variant behavior. The anticipation mode (i.e...
The implementation of the Extended Prediction Self-Adaptive Controller is presented in this paper. It employs LabVIEWTM graphical programming of industrial equipment and it is suitable for controlling fast processes. Three different systems are used for implementing the control algorithm. The research regarding the controller design using graphical programming demonstrates that a single advanced control...
This paper deals with the event-triggered distributed H∞ filtering for a class of networked systems with sensor networks. The topology of the sensor network is supposed to be time-varying. Whether or not a sampled data packet of a sensor node should be transmitted to its neighbors is determined by a predefined event-triggering condition, which closely depends on the variation of the sensor network...
In this paper, an innovative unknown input observer (UIO) is developed to estimate the faults of the system subjected to faults and process disturbances. By representing the concerned faults as auxiliary states, an augmented system is constructed. By designing an unknown input observer for the augmented system, the simultaneous estimations of the system states and concerned faults can be obtained...
This paper presents an approach for automatic reconstruction of automation logic from execution scenarios using a metaheuristic algorithm. The IEC 61499 basic function blocks is chosen as implementation language and reconstruction of Execution Control Charts for basic function blocks is addressed. The synthesis method is based on a metaheuristic algorithm most closely related to ant colony optimization...
Early detection of abnormalities for electrical motors is a key point to reduce economic losses caused by unscheduled maintenance and shutdown time. In this context, health monitoring and fault diagnosis are crucial tasks to be performed. We introduce a novel Linear Discriminant Analysis (LDA) based algorithm to deal with fault data dimension reduction and fault detection issues. In particular the...
Models describing the material flow of discrete manufacturing systems are important documentation artefacts and the basis for a comprehensive understanding of the underlying processes. The analysis of such models allows deriving important key performance indicators enabling the assessment of the current system implementation. However, manual modeling as well as up-to-date model maintenance is an error-prone...
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