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Remaining useful life prediction based on health index similarity

机译：基于健康指标相似度的剩余使用寿命预测

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摘要

Condition-based maintenance and the prediction of the remaining useful life (RUL) of cutting tools are of crucial importance to reduce unexpected downtime and ensure quality. Our paper proposes an original RUL prediction model based on health index (HI) similarity, where both distance similarity and spatial direction similarity are considered. Data mining is carried out to the large and messy original monitoring data to construct the HIs of the cutting tools, which are then used to predict the RUL. The novelty of our method is that it can make full use of limited historical datasets to achieve more accurate prediction results. The model is applied to the data obtained from a turbine factory's slotting cutter machining process and is compared to one of the most popular prognostic method - least squares support vector regression. Our proposed approach is also applied to two further case studies -a GaAs-based semiconductor laser and simulated data. The comparative results show the effectiveness and practicability of our proposed method, even when the data fluctuate a lot and show distinctive trends.
机译：基于状态的维护和切削工具剩余使用寿命的预测对于减少意外停机时间并确保质量至关重要。本文提出了一种基于健康指数（HI）相似度的原始RUL预测模型，其中同时考虑了距离相似度和空间方向相似度。对大而凌乱的原始监控数据进行数据挖掘，以构建切削工具的HI，然后将其用于预测RUL。我们方法的新颖之处在于它可以充分利用有限的历史数据集来获得更准确的预测结果。该模型应用于从涡轮机工厂的开槽刀具加工过程中获得的数据，并与最流行的预后方法之一-最小二乘支持向量回归进行比较。我们提出的方法还应用于另外两个案例研究-基于GaAs的半导体激光器和模拟数据。比较结果表明，即使数据波动很大并呈现出明显的趋势，我们的方法的有效性和实用性。

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