Transformer fault diagnosis using continuous sparse autoencoder
نویسندگان
چکیده
منابع مشابه
Transformer fault diagnosis using continuous sparse autoencoder.
This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the con...
متن کاملStudy on Transformer Fault Diagnosis Based on Dynamic Fault Tree
In this paper, according to theoretical diagnosis of fault tree, the author builds a diagnosis model based on dynamic fault tree and illustrates the model’s construction method and diagnosis logic in detail. According to case analysis, compared with conventional fault tree diagnosis, the above-mentioned method is advanced in fault-tolerant ability. Plus, the diagnosis results record some interm...
متن کاملUsing PCA with LVQ, RBF, MLP, SOM and Continuous Wavelet Transform for Fault Diagnosis of Gearboxes
A new method based on principal component analysis (PCA) and artificial neural networks (ANN) is proposed for fault diagnosis of gearboxes. Firstly the six different base wavelets are considered, in which three are from real valued and other three from complex valued. Two wavelet selection criteria Maximum Energy to Shannon Entropy ratio and Maximum Relative Wavelet Energy are used and compared...
متن کاملStructured Sparse Convolutional Autoencoder
This paper aims to improve the feature learning in Convolutional Networks (Convnet) by capturing the structure of objects. A new sparsity function is imposed on the extracted featuremap to capture the structure and shape of the learned object, extracting interpretable features to improve the prediction performance. The proposed algorithm is based on organizing the activation within and across f...
متن کاملParallelizing the Sparse Autoencoder
The objective of this project is to take the Spase Autoencoder algorithm, as presented at: http://www.stanford.edu/class/archive/cs/cs294a/cs294a.1104/handouts.html , and develop methods for parallelizing, and possibly distributing the computation. In addition, the language Go, was chosen as the language to implement the algorithm, because it is a language designed with parallel computation as ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SpringerPlus
سال: 2016
ISSN: 2193-1801
DOI: 10.1186/s40064-016-2107-7