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Through introducing the whole operation of the electric locomotives by system monitoring software, a graphical intuitive understanding developed by configuration software. Preparation of the entire electric locomotive operating system monitoring software locomotive operations recorded by the whole locomotive of the acquisition, the top operations Video(More)
For the better analysis of failure diagnosis problems, firstly, using MATLAB software to simulate failure problems such as the emergence of regular power, short circuit and broken circuit. Combining the failure practical example, neural network analysis is introduced for diagnosis. Failure diagnosis using two kinds of neural networks: the BP network and(More)
When the locomotive system breaks down, it is hard to control the quality of the locomotive overhaul process using traditional network diagram, owing to the complexity of fault phenomena and fault reasons. Moreover, conventional method may lead to damage to some fault-free components easily. Ant colony algorithm (ACA) was adopted to improve the quality in(More)
Combining the failure practical example, neural network analysis is used for diagnosis. Using the neural network in MATLAB to simulate the circuit fault system, this is using two different kinds of training functions. For the better analysis of failure diagnosis problems, firstly, using MATLAB software to simulate failure problems such as the emergence of(More)
Based on 66 capacitance values by 12-electrode capacitance sensor, combining Fast Independent Component Analysis (FastICA) with Least Squares Support Vector Machine (LS-SVM) algorithms, a new method for phase concentration measurement of two-phase flow was proposed. FastICA was used to extract the independent components from capacitance values. LS-SVM was(More)
An improved least squares support vector machine (LS-SVM) approach was proposed to overcome the drawback of ldquolosing sparsityrdquo in original LS-SVM. At the same time, real-coded genetic algorithm (RC-GA) was introduced to solve the difficult problem of parameters selection in LS-SVM. By discarding most data points with too large or too small training(More)
Based on the characteristic that the Empirical Mode Decomposition (EMD) can decompose signal adaptively, a flow pattern identification method based on EMD multi-scale information entropy was put forward. Firstly, the acquired pressure-difference fluctuation signals are decomposed through EMD, and the decomposed signals within different frequency bands are(More)
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