The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In view of the support vector machine (SVM) model applied in vibrant fault diagnosis for hydro-turbine generating unit, it exists problems of parameter settings and classification-plane incline due to unequal sample, which leads to lower diagnosis accuracy. As a new bionic intelligent optimization algorithm for glowworm swarm optimization(GSO), it has the characteristics of strong versatility and...
In order to solve the problems such as availability of data extraction, better local optimum, gradient to dissipate more efficiently, this paper presents a new method of power transformer fault diagnosis based on deep learning and Softmax classifier. Power transformer fault diagnosis model is established based on stacked auto-encoders and softmax regression, then each restricted boltzmann machine...
This paper studies the simultaneous fault diagnosis of the main reducer in the automobile transmission system assembly based on vibration signals. A simultaneous fault diagnosis model based on Paired Relevance Vector Machine (Paired-RVM) is proposed for the simultaneous fault of the main reducer, and each binary sub-classifier is trained with single fault samples and then fused by a pairing strategy...
This paper proposes a regrouping particle swarm optimization-based neural network (RegPSONN) for rolling bearing fault diagnosis. The proposed method applied neural network for rolling bearing conditions classification, and regrouping particle swarm optimization (RegPSO) is utilized for network training, and ten time-domain feature parameters are selected to establish the input vector. To evaluate...
Aiming at some characteristics of servo valves that the complex influence between the failure modes and the high order nonlinearity of the fault datum, this paper presents a fault diagnosis model-Deep Intelligent Generalized Regression Neural Network (DGN). The DGN is a supervised deep learning model. In order to fully learning the fault datum, this paper proposed a logistic mapping and dynamic step...
Due to the growth prospect of analog circuit fault diagnosis, this paper tends to introduce a novel arithmetic model based on least squares support vector machine (LSSVM) and the semi-supervised learning (SSL) scheme which is adept at cost-saving. The proposed method contains two steps. Firstly, the fact that large deviation may emerge as a result of the empirical risk inspires the idea of an improved...
This paper is an attempt to develop a new technology, which is an advancement of the previously published paper [1] for fault diagnosis of multilevel inverter adopting the machine learning and optimization techniques. The advanced machine-learning algorithm called the Optimized Radial Basis Neural Network (ORBNN) method is developed in which the Neural Network uses Radial Basis function as the activation...
Based on VC dimension theory and structural risk minimization principle of statistical learning theory, Support vector machine (SVM) has a prominent advantage in solving classification and fault prediction problems, specifically suitable for small sample, nonlinear and high dimensional pattern recognition problems. However, SVM is originally created for solving binary classification problems. The...
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...
Extreme Learning Machine has the quality of fast learning speed, good generalization performance, and high diagnostic accuracy. For analog circuit fault diagnosis and health management (PHM) applications, this paper presents the method of online sequential learning machine with differential evolution algorithm to optimize Extreme Learning Machine and improve the diagnostic accuracy and generalization...
Classification strategy is an important issue of support vector machine application. Aiming at the defects of common used classification methods, an improved minimum spanning tree support vector machine (MST-SVM) is proposed. MST-SVM has the advantages of simple structure and high classification efficiency. The classification process is further optimized by introduction of Fisher separability measure...
Using the counter propagation artificial neural network (CPANN) to diagnose the DGA fault, network structural parameters should be set, such as the training epochs, network size etc. When user to set, it would be affect by the artificial subjective factors. If we use the traversal search way, it would be the consumption of computing and time. So this article employed parallel genetic algorithm to...
Support vector machine (SVM) is a machine learning algorithm which has been applied to fault diagnosis of analog circuits. Invasive weed optimization (IWO) is a novel numerical optimization algorithm inspired from weed colonization. An approach that combines IWO and SVM (IWO-SVM) is proposed to fault diagnosis of analog circuits in this paper. The process of fault diagnosis of analog circuits using...
This paper presents a BP network model based on improved PSO for bearing fault diagnosis. Combining PSO algorithm for global optimization ability with BP neural network advantages of local search, the model effectively prevents the network from a local minimum, and at the same time guarantees the accuracy of diagnosis. Simulation results show that the locomotive bearings have been effectively diagnosed...
Aiming at the defects of BP neural network, analyzed the disadvantages of the genetic algorithm and the common quantum genetic algorithm. Used the diversity of population and the rapidity of convergence of the real-number coded double-chain quantum genetic algorithm which was combined with BP neural network to modify the weights and thresholds of the neural network, and the modified neural network...
In order to solve the fault diagnosis problem of vibration Parameter, this dissertation proposes the application of adaptive neural network-based fuzzy inference system to engine error diagnosis. Different from the fuzzy inference system, the membership function adopted in this method is no longer a fixed entity but an optimal one achieved by the practice of neural network, which adopts the method...
Adaptive neural network-based fuzzy inference system (ANFIS) was applied to build a fault diagnosis model of automobile engine, the thesis, with the construction of ANFIS, by using gradient descent genetic algorithm and optimization of system parameters of neutral network learning algorithm, inputs the fusion data into ANFIS, the ANFIS fault diagnosis model adopts the method of information fusion...
This paper, in order to reduce fault and improve ratio of recognition, build adaptive neural network-based fuzzy inference system (ANFIS), which was applied to build a fault diagnosis model of automobile engine, adopts the method of information fusion in entropy method to optimize the input interface. To reduce the impact of excessive parameters on classification accuracy and cost, it also raises...
Low convergence accuracy and the acceleration coefficient setting problem have always been the difficult and hot research points of particle swarm optimization algorithm. This paper introduces a composite particle swarm optimization CPSO based on the adaptive PSO and adaptive GA and applies CPSO in the BP network training of turbo-pump fault diagnosis. In addition, the classical test function Rastrigrin...
Flight Parameters stage classification is the premise of the fault diagnosis and trend forecast based on flight parameters. Stage classification belongs to the classification optimization problem of multi-attribute data through analysis the flight data. This paper carried out the research for the two-class classification based on the semi-supervised learning methods PTSVM (Progressive Transductive...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.