Since the concept of industry 4.0 was proposed in 2011, the trend of industry 4.0 has been surging around the world. Intelligent factory is one of the main research points in the industry 4.0 era. In order to improve the intelligent level of the factory, the connection-and-cognition ability has to be established for the factory and its equipment. Connection builds data pipes among equipment and systems while cognition automatically turns the data into knowledge. In an intelligent factory, industrial robot plays a leading role. Hence, the aim of this paper is to synthetically study connection and cognition of industrial robots in intelligent factories. To be specific, open platform communications unified architecture (OPC UA) is applied to establish heterogeneous connection of industrial robots with factory management software. A long short-term memory (LSTM) joint auto encoder method is proposed to establish the unsupervised anomaly detection cognition ability for industrial robot process (e.g. grinding, welding and assembling). In summary, this study puts OPC UA and LSTM auto encoder technology together to study heterogeneous connection and process anomaly detection of industrial robots in intelligent factory. The experimental results showed that the proposed method successfully realized heterogeneous connection of an industrial robot and detected process anomaly from the robot built-in sensors’ data.

中文翻译：

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智能工厂工业机器人异构连接与过程异常检测

自2011年工业4.0概念提出以来，工业4.0的潮流在全球范围内风起云涌。智能工厂是工业4.0时代的主要研究点之一。为了提高工厂的智能化水平，必须建立工厂及其设备的连接和认知能力。连接在设备和系统之间建立数据管道，而认知自动将数据转化为知识。在智能工厂中，工业机器人起着主导作用。因此，本文的目的是综合研究工业机器人在智能工厂中的连接和认知。具体而言，应用开放平台通信统一架构（OPC UA）建立工业机器人与工厂管理软件的异构连接。提出了一种长短期记忆（LSTM）联合自动编码器方法，以建立工业机器人过程（如磨削、焊接和装配）的无监督异常检测认知能力。综上所述，本研究将 OPC UA 和 LSTM 自动编码器技术结合起来，研究智能工厂中工业机器人的异构连接和过程异常检测。实验结果表明，该方法成功地实现了工业机器人的异构连接，并从机器人内置传感器的数据中检测到过程异常。本研究将OPC UA和LSTM自动编码器技术结合起来，研究智能工厂中工业机器人的异构连接和过程异常检测。实验结果表明，该方法成功地实现了工业机器人的异构连接，并从机器人内置传感器的数据中检测到过程异常。本研究将OPC UA和LSTM自动编码器技术结合起来，研究智能工厂中工业机器人的异构连接和过程异常检测。实验结果表明，该方法成功地实现了工业机器人的异构连接，并从机器人内置传感器的数据中检测到过程异常。