Modern manufacturing is now deeply integrating new technologies such as 5G, Internet-of-things (IoT), and cloud/edge computing to shape manufacturing to a new level -- Smart Factory. Autonomic anomaly detection (e.g., malfunctioning machines and hazard situations) in a factory hall is on the list and expects to be realized with massive IoT sensor deployments. In this paper, we consider acoustic data-based anomaly detection, which is widely used in factories because sound information reflects richer internal states while videos cannot; besides, the capital investment of an audio system is more economically friendly. However, a unique challenge of using audio data is that sounds are mixed when collecting thus source data separation is inevitable. A traditional way transfers audio data all to a centralized point for separation. Nevertheless, such a centralized manner (i.e., data transferring and then analyzing) may delay prompt reactions to critical anomalies. We demonstrate that this job can be transformed into an in-network processing scheme and thus further accelerated. Specifically, we propose a progressive processing scheme where data separation jobs are distributed as microservices on intermediate nodes in parallel with data forwarding. Therefore, collected audio data can be separated 43.75% faster with even less total computing resources. This solution is comprehensively evaluated with numerical simulations, compared with benchmark solutions, and results justify its advantages.
翻译:现代制造业目前正在深入整合新技术,如5G、英特网(IoT)和云层/尖端计算等新技术,以将制造形成新水平 -- -- 智能工厂。工厂大厅中的自动异常探测(例如机器故障和危险情况)已被列入清单,预计将通过大规模IoT传感器部署实现。在本文中,我们考虑基于声学的数据异常探测,这种探测在工厂中广泛使用,因为健全的信息反映较富裕的内部状态,而视频则不能;此外,音频系统的资本投资在经济上更加友好。然而,使用音频数据的独特挑战是,在收集源数据分离时声音是混合的。传统方式是将音频数据全部传输到集中的分离点。然而,这种集中的方式(即数据传输和随后分析)可能会推迟对关键异常现象的迅速反应。我们证明,这项工作可以转化成一个网络内处理机制,从而进一步加速。我们提议一个渐进的处理机制,即数据分离工作作为中间节点的微观服务在数据传输中进行分配。因此,一个独特的挑战是不可避免的。一种传统方式将音频数据数据数据数据传输到集中的数据数据数据数据传送到集中点,因此,可以将其与数字资源加以全面分离,比比作比较,比较,比较,比较其基数率是较快的方法可以比较,比的计算结果比较,比比数据比数据比的优势,比的计算结果可以更快。