Residual life prediction method of complex equipment based on two-layer long-short-term memory network

Abstract

The invention discloses a method for predicting the residual life of complex equipment based on a double-layer long-short-term memory network. The method includes by adopting a depth learn algorithm,preprocessing the historical data of complex equipment, and building a two-tier long-term and short-term (LSTM) network, wherein the number of LSTM cells in a two-layer long-short-term (LSTM) networkis determined by a continuous time period. The current data are preprocessed and transferred to the trained two-layer LSTM network, and the output of the two-layer LSTM network is set as the predictedvalue of the residual life of complex equipment. The residual life prediction model of the complex equipment based on the double-layer LSTM provided by the invention can improve the prediction accuracy of the residual life of the complex equipment. Complex equipment can thus be maintained in a timely and effective manner, while reducing the occurrence of accidents. It is of great significance toensure the safety of complex equipment operation and reduce unnecessary maintenance at the same time.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of complex equipment, and in particular relates to a method for predicting the remaining life of complex equipment based on a double-layer long-short-term memory network (LSTM). Background technique [0002] In practical engineering applications, mechanical equipment is inevitably damaged. With the accumulation of service time, the accumulation of damage will eventually lead to equipment failure. Taking maintenance measures after equipment failure often interrupts production and leads to a decrease in efficiency, and replacing equipment too early will cause waste of resources and increase additional costs; therefore, accurate prediction of the remaining service life of equipment can effectively improve production efficiency , reducing additional costs. However, as equipment design tends to become more complex, it becomes increasingly difficult to accurately predict the remaining service l...

AI Technical Summary

Problems solved by technology

According to relevant literature, a large number of algorithms have been applied to the prediction of the remaining life of complex equipment, such as support vector machines, hidden Markov models, and deep learning, among which deep learning has shown excellent results. However, problems related to complex equipment The amount of data is large and there are various types of data. It is difficult for traditional algorithms to establish a mapping between data and the remaining life of complex equipment, and the prediction effect is poor.

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