Open AccessPredicting Spare Parts Inventory of Hydropower Stations and Substations Based on Combined Model
Author(s) -
Zhenguo Ma,
Bing Tang,
Keqi Zhang,
Yuming Huang,
Danyi Cao,
Jiaohong Luo,
Jianyong Zhang
Publication year - 2022
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2022/1643807
Subject(s) - spare part , autocorrelation , autoregressive model , autoregressive integrated moving average , autoregressive–moving average model , artificial neural network , computer science , process (computing) , reliability engineering , data mining , engineering , procurement , nonlinear system , operations research , time series , artificial intelligence , statistics , machine learning , mathematics , operations management , physics , marketing , quantum mechanics , business , operating system
In this paper, a combined model is proposed to predict spare parts inventory in accordance with equipment characteristics and defect elimination records. Fourier series is employed to process the periodicity of the data, autoregressive moving average (ARMA) is used to deal with the linear autocorrelation of the data, and backpropagation (BP) neural network is used to settle the nonlinearity of the data. The prediction results, comparisons, and error analyses show that the combined model is accurate and meets the practical requirements. The combined model not only fully utilizes the information contained in the data but also provides a reasonable decision basis for the procurement of spare parts, making the inventory in a safe state and saving holding costs.
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