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.
The existing power system fault analysis method is based on the information of power system relay protection and sensor device to determine the location and type of fault. With the continuous expansion of the power system and the increasing complexity of the structure, excessive alarm information makes it difficult to diagnose the power system faults timely and effectively. A fault diagnosis method...
Actuator plays an important role in the Flight Control System of UAV (Unmanned Aerial Vehicle), so it is necessary to diagnose the fault in real time. This paper analyzed the operating principle of UAVs actuator, on this basis the analytic model is established to operate state monitoring and fault diagnosis, and designed a threshold-improved SPRT joint algorithm to extract the characteristic of fault...
The characteristics of the last stage group of condensing steam turbine are analyzed, and a method based on the synthetic BP (back-propagation) neural network is proposed for determining the normal value of relative internal efficiency of the last stage group. In order to consider the influence of the regenerative system, the influential factors of the relative internal efficiency of the last stage...
Unexpected bearing failures may cause unscheduled downtime and economic losses. It is, therefore, very important to find the faults symptoms of the rolling element bearing components. Vibration signal of fault bearing is nonlinear and non-stationary in nature, which makes the stationary assumed methods not appropriate. In this paper, a biphase randomization wavelet bicoherence method is introduced,...
Wavelet bicoherence is one of the most useful tools for quadratic nonlinear behavior identification of stochastic system, which has been used in many fields. However, current wavelet bicoherence algorithm can neither eliminate the spurious peaks coming from components with long coherence time, nor distinguish the quadratic phase coupling and non quadratic phase coupling signals, which may constraint...
Transformer fault diagnosis based on relevance vector machine (RVM) is proposed. The advantages of the RVM over the support vector machine (SVM) are probabilistic predictions, automatic estimations of parameters, and the possibility of choosing arbitrary kernel functions. Most importantly, RVM is capable of comparable classification accuracy to SVM, but with fewer relevance vectors (RVs) and higher...
Because matter-element theory's shortcoming in ignoring the uncertainty of boundary values when setting up a fault diagnosis model, the diagnosing results are deviated from the actual situation. Considering cloud model theory can be applied to represent the uncertainty of the boundary, we combine cloud model theory with matter-element to propose a new diagnosing model. Meanwhile, by using data such...
Support Vector Machine (SVM) is based on statistical learning theory which developed from the common machine learning. It is an effective tool to deal with limited samples. This paper proposes a model of the dissolved gas analysis (DGA) of transformer based on Multi-class SVM. Firstly, with the combination of SVM multi-class classification methods one-versus-rest (1-v-r) and one-versus-one (1-v-1),...
The mechanical faults of a transformer are predicted with monitoring its vibration, which is a new technology put forward recently for condition based monitoring of transformers. The key technology of vibration method is how to identify the effective features of the vibration signal. Here, a new self-adapting time-frequency analysis method - local wave method which is developed from the empirical...
This paper addresses the application of neural network to air-cooling condenser faults diagnosis. For traditional back propagation (BP) neural network algorithm, the learning rate selection is depended on experience and trial. In this paper, an improved BP neural network algorithm with self adaptive learning rate is proposed using the fundamental equation. Unlike existing algorithm, self adaptive...
It is difficult to determine the normal value for fault diagnosis of steam turbine flow passage, because the flow passage fault or regenerative system fault may result in the decrease of the relative internal efficiency of steam turbine. Based on analyzing the feature of flow passage and regenerative system, the flow passage condition of steam turbine can be evaluated by relative internal efficiency...
A multi-level fault diagnosis model for power transformer fault diagnosis based on Statistical theory is presented The fault information within Dissolved Gas Analysis (DGA) is used to build fault diagnosis model and the fault diagnosis is accomplished according to the concentration distribution of typical fault gases in higher dimensional space. The proposed approach is constructing the most accuracy...
A new method based on Euclidean clustering and support vector machines is presented and constructed in the paper. According to the Euclidean distances between the transformer's state sorts, build the multi-classification model of support vector machines. The diagnosing experiments of different transformer testing scenarios show this method can avoid the blindness when building the multi-classifier,...
Because traditional transformer diagnosing approaches are over-rigidity and need almost complete and accurate testing data, a NB (Naive Bayesian) classifier based model to diagnose transformer faults is presented and constructed in the paper. As the diagnosing performance is depressed by incomplete testing data, SVM regression approach is used to estimate the missing data. Thus a new diagnosis model,...
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.