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- W4387005418 abstract "Massive process measurement message brings great computational complexity and modeling complexity to the traditional breakdown diagnosis algorithm, and the traditional diagnosis algorithm has the disadvantage that it is difficult to make online estimation by using high-order quantities. Modern industrial systems have been developing towards largescale and complexity, which makes the breakdown diagnosis means for industrial systems encounter a series of technical questions. In view of the powerful data representation learning and analysis ability of deep learning technique, breakdown diagnosis based on deep learning has attracted wide attention from industrial and academic circles, and has made intelligent process control more automatic and resultful. The means adopted in this paper is deep learning, which is carried out alternately by convolution and sub-sampling. At last, the algorithm close to the output layer adopts the common multilayer neural network. As a breakthrough in the domain of latter-day artificial intelligence, deep learning can voluntarily learn valuable traits from the original trait set or even the original data. The research in this paper shows that the algorithm in this paper is resultful in breakdown prediction, and it is suitable to be widely applied in practical applications." @default.
- W4387005418 created "2023-09-26" @default.
- W4387005418 creator A5076689598 @default.
- W4387005418 date "2023-06-01" @default.
- W4387005418 modified "2023-09-26" @default.
- W4387005418 title "Computer System Breakdown Intelligent Analysis Simulation Model Based on Deep Learning" @default.
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- W4387005418 doi "https://doi.org/10.1109/icmiii58949.2023.00114" @default.
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