Abstract
Abstract The aim of this study was to improve the low accuracy of equipment spare parts requirement predicting, which affects the quality and efficiency of maintenance support, based on the summary and analysis of the existing spare parts requirement predicting research. This article introduces the current latest popular long short-term memory (LSTM) algorithm which has the best effect on time series data processing to equipment spare parts requirement predicting, according to the time series characteristics of spare parts consumption data. A method for predicting the requirement for maintenance spare parts based on the LSTM recurrent neural network is proposed, and the network structure is designed in detail, the realization of network training and network prediction is given. The advantages of particle swarm algorithm are introduced to optimize the network parameters, and actual data of three types of equipment spare parts consumption are used for experiments. The performance comparison of predictive models such as BP neural network, generalized regression neural network, wavelet neural network, and squeeze-and-excitation network prove that the new method is effective and provides an effective method for scientifically predicting the requirement for maintenance spare parts and improving the quality of equipment maintenance.
Highlights
The aim of this study was to improve the low accuracy of equipment spare parts requirement predicting, which affects the quality and efficiency of maintenance support, based on the summary and analysis of the existing spare parts requirement predicting research
According to the characteristics of the time series data with equipment spare parts consumption, this article proposes to use long short-term memory (LSTM) recurrent neural network to predict the requirement of equipment spare parts, in which the technical advantages of LSTM recurrent network will be fully performed
In order to solve the problem that the LSTM recurrent neural network prediction model involves many parameters, which will make the performance of prediction unstable if set artificially, with the goal of minimizing the prediction error, an algorithm of equipment spare part requirement prediction with LSTM recurrent neural network whose parameters are optimized by particle swarm optimization is further proposed
Summary
Abstract: The aim of this study was to improve the low accuracy of equipment spare parts requirement predicting, which affects the quality and efficiency of maintenance support, based on the summary and analysis of the existing spare parts requirement predicting research. A method for predicting the requirement for maintenance spare parts based on the LSTM recurrent neural network is proposed, and the network structure is designed in detail, the realization of network training and network prediction is given. The performance comparison of predictive models such as BP neural network, generalized regression neural network, wavelet neural network, and squeeze-andexcitation network prove that the new method is effective and provides an effective method for scientifically predicting the requirement for maintenance spare parts and improving the quality of equipment maintenance
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