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Electronic equipment fault prediction method based on LSTM enhancement model

A technology for electronic equipment and fault prediction, which is applied in the field of neural network improvement, can solve problems such as decreased accuracy, and achieve the effect of improving memory ability

Pending Publication Date: 2020-09-22
BEIJING INFORMATION SCI & TECH UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the problem that the existing LSTM model is used to predict equipment failures, and the accuracy will gradually decrease with the length of the sequence, and a method for predicting electronic equipment failures based on LSTM enhanced models is provided.

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  • Electronic equipment fault prediction method based on LSTM enhancement model
  • Electronic equipment fault prediction method based on LSTM enhancement model
  • Electronic equipment fault prediction method based on LSTM enhancement model

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Embodiment Construction

[0032] 1. Implementation principle of the existing LSTM model:

[0033] RNN is a neural network with a hidden layer with a self-connection relationship. Each node calculates the output at the current moment through the state at the current moment, and the state at the current moment is determined by the state at the previous moment and the input at the current moment, so as to realize Memory of time series data. LSTM continues the chain conduction structure of RNN, and adds four kinds of interaction gates on the basis of RNN. Such as figure 1 As shown, input (input gate), forget (forget gate), output (output gate) and cell (cell state) to solve the gradient explosion problem. Each LSTM node contains three inputs, namely the node state C at the last moment t-1 , the node output h at the last moment t-1 and the input x at the current moment t . The unique gate structure of LSTM contains a non-linear activation function sigmoid, which determines the amount of information pa...

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Abstract

The invention discloses an electronic equipment fault prediction method based on an LSTM enhancement model, and relates to a method for performing memory enhancement on a long-short-term neural network. In order to overcome the problem that the precision is gradually reduced along with the time sequence length when an existing LSTM model is adopted to predict an equipment fault, the method specifically comprises the following steps: step 1, collecting monitoring data in the operation process of electronic equipment, and preprocessing the monitoring data; 2, building an LSTM enhancement model,and inputting the preprocessed monitoring data into the LSTM enhancement model for training to obtain a trained LSTM enhancement model; and step 3, according to the trained LSTM enhancement model andthe monitoring data in a period of time, predicting the fault of the electronic equipment.

Description

technical field [0001] The invention relates to a neural network improvement method, in particular to a memory enhancement method for long and short-term neural networks. Background technique [0002] Time series prediction technology has important application value in many fields such as economy, meteorology, geology, hydrology, science, military affairs, and medicine. In industrial control, the uninterrupted operation of machines leads to failures. Through the processing and prediction of industrial data, the automated production process can be effectively supervised and hidden risks can be prevented. In this field, the application problems in practical scenarios have complex nonlinear characteristics, and the linear models used for time series analysis in the early days have limitations. Traditional machine learning methods such as matrix decomposition, support vector machine SVM (Support Vector Machine), and Bayesian network largely rely on the effectiveness of artifici...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/049G06N3/084G06N3/044G06N3/045G06F18/2411G06F18/2433
Inventor 王超吴明慧
Owner BEIJING INFORMATION SCI & TECH UNIV
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