Recently, the development of the Industrial Internet of Things (IIoT) has led enterprises to re-examine the research of the equipment-state-prediction models and intelligent manufacturing applications. Take industrial robots as typical example. Under the effect of scale, robot maintenance decision seriously affects the cost of spare parts and labor deployment. In this paper, an evaluation method is proposed to predict the state of robot lubricating oil based on support vector regression (SVR). It would be the proper model to avoid the structural risks and minimize the effect of small sample volume. IIoT technology is used to collect and store the valuable robot running data. The key features of the running state of the robot are extracted, and the machine learning model is applied according to the measured element contents of the lubricating oil. As a result, the cost of spare parts consumption can be saved for more than two million CNY per year.

中文翻译：

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基于支持向量回归的机器人润滑油状态评价方法

近期，工业物联网（IIoT）的发展促使企业重新审视设备状态预测模型和智能制造应用的研究。以工业机器人为例。在规模效应下，机器人维修决策严重影响备件成本和劳动力调配。本文提出了一种基于支持向量回归（SVR）的机器人润滑油状态预测评价方法。这将是避免结构风险和最小化小样本量影响的合适模型。工业物联网技术用于收集和存储有价值的机器人运行数据。提取机器人运行状态的关键特征，并根据测得的润滑油元素含量应用机器学习模型。