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首頁> 外文期刊>Robotics and Computer-Integrated Manufacturing >A data-driven based decomposition-integration method for remanufacturing cost prediction of end-of-life products
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A data-driven based decomposition-integration method for remanufacturing cost prediction of end-of-life products

機(jī)譯：一種基于數(shù)據(jù)驅(qū)動的分解整合方法，用于報廢產(chǎn)品再制造成本預(yù)測

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摘要

Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition-integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products.
機(jī)譯：再制造成本預(yù)測有助于從經(jīng)濟(jì)角度直觀地判斷報廢（EOL）產(chǎn)品的可再制造性。然而，由于隨機(jī)性，再制造成本的非線性和缺乏足夠的數(shù)據(jù)樣本。預(yù)測EOL產(chǎn)品再制造成本的一般方法的精度非常低。為此，提出了一種基于數(shù)據(jù)驅(qū)動的分解集成方法來預(yù)測EOL產(chǎn)品的再制造成本。該方法基于歷史再制造成本數(shù)據(jù)來建立預(yù)測模型。首先，單個EOL產(chǎn)品的再制造成本按時間順序排列在再加工順序中。改進(jìn)的局部均值分解（ILMD）用于將再制造成本時間序列數(shù)據(jù)分解為具有平滑，周期性波動的多個組件，并將其用作輸入。利用基于粒子群算法（PSO-BP）的BP神經(jīng)網(wǎng)絡(luò)來預(yù)測每個零件的成本。最后，將預(yù)測分量相加以獲得最終預(yù)測結(jié)果。為了說明和驗證該方法的可行性，以DH220挖掘機(jī)的再制造成本為樣本數(shù)據(jù)，實證結(jié)果表明，該模型具有較高的預(yù)測精度和較少的計算時間，在統(tǒng)計上優(yōu)于其他基準(zhǔn)模型。提出的方法可以作為一種有效的工具來分析和預(yù)測EOL產(chǎn)品的再制造成本。

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