Mechanical equipment key part residual life prediction method combining AE and bi-LSTM

A technology for life prediction and mechanical equipment, applied in neural learning methods, geometric CAD, biological neural network models, etc., can solve the problems of difficult feature extraction and low accuracy of remaining life prediction
CN110737952APending Publication Date: 2020-01-31TAIYUAN UNIV OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
TAIYUAN UNIV OF TECH
Publication Date
2020-01-31

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Abstract

The invention belongs to the technical field of mechanical equipment key part service life, and discloses a mechanical equipment key part residual life prediction method combining AE and bi-LSTM, andthe method comprises the following steps: carrying out the feature extraction of input data through an auto-encoder; dividing the data after feature extraction to obtain a training set and a test set;constructing a bidirectional LSTM prediction model, wherein in the bidirectional LSTM prediction model, an LSTM network hidden layer comprises a forward layer and a backward layer; carrying out training through training set data and test set data until an evaluation index is close to the optimal, and storing the bidirectional LSTM prediction model and parameters thereof; and inputting the to-be-predicted data into the bidirectional LSTM prediction model, and outputting the predicted life. The method improves the prediction result, and can be applied to the field of mechanical part life prediction.
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Description

technical field

[0001] The invention belongs to the technical field of service life of key components of mechanical equipment, and in particular relates to a method for predicting the remaining life of key components of mechanical equipment combined with AE and bi-LSTM. Background technique

[0002] Bi-directional long short-term memory (bi-LSTM) is widely used in natural language recognition, text analysis, etc., but it is rarely used in the prediction of the remaining life of key parts of mechanical equipment. The existing feature extraction methods for key parts of mechanical equipment need to be improved, and the accuracy of the remaining life prediction method needs to be further improved. The existing methods for predicting the remaining life of mechanical equipment use one-way learning of historical data (based on time series and time-progressive relationship for remaining life prediction model training), and only perform feature learning and predict future remaining ...

Claims

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