Plastic gear remaining useful life prediction using artificial neural network
Plastic gear remaining useful life prediction using artificial neural network
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DOI：
10.1007/s10010-021-00557-9
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发表时间：
2021-10-21
影响因子：
1.1
通讯作者：
Moriwaki, I.
中科院分区：
文献类型：
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作者：
Kien, B. H.;Iba, D.;Moriwaki, I.
塑料齿轮的预测和健康管理（PHM）由于塑料齿轮性能的提高而引起了人们的关注，揭示了塑料齿轮在工业中的潜在应用，特别是在车辆变速器中。同时，健康指标（HI）的建设和剩余使用寿命（RUL）的估计是有效地执行PHM的两个关键要素。在本文中，健康指标发生器（HIG）的基础上，人工神经网络（ANN）的构造。HIG是从训练数据中学习的，训练数据是从塑料齿轮的原始振动数据中提取的，使用傅里叶分解方法（FDM）在敏感频带（SFB）中，并使用变点检测算法（CDA）标记。采用线性回归（LR）、威布尔分布参数估计（EWD）和HI组合平均RUL（HI-ARUL）三种预测策略，利用HIG生成的HI对塑料齿轮的RUL进行预测。结果表明，所生成的HI对塑料齿轮的早期失效具有敏感性，能够在塑料齿轮的整个工作时间内进行有效而精确的诊断，预测误差小于7%。
Prognostic and health management (PHM) of plastic gears has attracted attention due to an increasing performance of plastic gears, uncovering potential applications in the industry, especially in vehicle transmissions. Meanwhile, health indicator (HI) construction and remaining useful life (RUL) estimation are two key elements to efficiently perform PHM. In this paper, a health indicator generator (HIG) based on an artificial neural network (ANN) is constructed. The HIG is learned from training data extracted from plastic gears' raw vibration data using the Fourier decomposition method (FDM) in a sensitive frequency band (SFB) and labeled using a change-point detection algorithm (CDA). Three prediction strategies, including linear regression (LR), estimation of parameters for Weibull distribution (EWD), HI combined average RUL (HI-ARUL), are deployed using HI generated from HIG to predict the RUL of the plastic gear. The results show that the generated HI is sensitive to the early failure of plastic gears and is capable of applying an efficient and precise diagnosis method, which can be performed during the whole working time of plastic gear with prediction errors smaller than 7%.