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Make Data Work
July 12-13, 2017: Training
July 13-15, 2017: Tutorials & Conference
Beijing, China

机器人的预测性维护实战:解读实时、可扩展的分析管道 (Robot predictive maintenance in action: Real-time, scalable pipelines explained)

This will be presented in English.

Mathieu Dumoulin (McKinsey & Company), Mateusz Dymczyk (H2O.ai)
14:50–15:30 Friday, 2017-07-14
物联网&实时计算 (IoT & real-time), 英文讲话 (Presented in English)
Location: 多功能厅6A+B(Function Room 6A+B) 观众水平 (Level): Intermediate

必要预备知识 (Prerequisite Knowledge)

A general understanding of big data technologies and machine learning

您将学到什么 (What you'll learn)

Explore a fully working pipeline from sensor to visualization explained step by step, learn how to apply anomaly detection on real-time streaming sensor data, and see a real application of modern big data streaming architecture in action

描述 (Description)

各种工业4.0的物联网应用承诺通过降低停机时间、提升产品质量和提高生产效率来获取很高的产能回报。现代化的工业机器人集成了数百个各种类型的传感器,并产生了蕴含丰富价值的海量数据。然而,现实却是即使是一些世界最尖端的工业企业也仅仅只是开始使用相对简陋原始的、定制化且造价高昂的监控系统来利用这些数据。

我们相信,现在已经是可以在月级的时间周期里,使用大浪淘沙后胜出的企业级大数据产品和开源项目来成功地部署工业4.0实验性使用案例,而且花费也仅是全球领先高科技企业里相同项目花费的一小部分。我们会展示一个这种系统的可用原型,并一定程度上解释怎么构建它。

这个可用系统是一个预测性维护案例的实现。只有聪明地使用现代化的基于微服务的流式架构才让这一切成为可能。这个系统利用了MapR聚合数据平台(MapR Converged Data Platform)的独特特征来进行操作分析、消息系统和存储。机器学习的建模和部署则是使用H2O.ai来实现的。

我的演讲描述了一个可用的实时、基于机器学习的异常检查系统。我们展示了一个安装了无线移动传感器的工业机器人模拟器。我们的系统通过一个云端集群进行数据计算。为了增加现实性,我们现场展示的系统会包括一个增强现实(AR)头盔来展示机器人的实时的负载状态。


Industry 4.0 IoT applications promise vast gains in productivity from reduced downtime, higher product quality, and higher efficiency. Modern industrial robots integrate hundreds of sensors of all kinds, generating tremendous volumes of data rich in valuable information. However, the reality is that some of the most advanced industrial makers in the world are barely getting started making use of this data, with relatively rudimentary, bespoke monitoring systems built at tremendous cost.

It is now possible to successfully deploy Industry 4.0 pilot use cases in a matter of months, at a small fraction of the cost of equivalent projects at leading high-tech makers, using a well-chosen selection of big data enterprise products and open source projects. Mathieu Dumoulin and Mateusz Dymczyk walk you step by step through building a working real-time ML-based anomaly detection system on a working industrial robot-analog installed with a wireless movement sensor. The working system is only made possible by a clever use of modern, microservices-based streaming architecture. You’ll learn how to gather data from a wireless movement sensor, process it with H2O on a MapR cluster, and visualize the output through an AR headset by an operator.

Photo of Mathieu Dumoulin

Mathieu Dumoulin

McKinsey & Company

Mathieu Dumoulin is a Digital Expert at McKinsey & Company’s Tokyo office, where he advises large enterprises for big data, enterprise architecture and advanced analytics solutions.
Current areas of interest are creating production systems which optimize industrial processes on operational data and real-time IoT sensor data.

Mateusz Dymczyk

H2O.ai

Mateusz Dymczyk is a Tokyo-based software engineer at H2O.ai, the company behind H2O, the leading open source machine learning platform for smarter applications and data products. He works on distributed machine learning projects including the core H2O platform and Sparkling Water, which integrates H2O and Apache Spark. Previously, he worked at Fujitsu Laboratories on natural language processing and utilization of machine learning techniques for investments and at Infoscience on a highly distributed log data collection and analysis platform. Mateusz loves all things distributed and machine learning and hates buzzwords. In his spare time, he participates in the IT community by organizing, attending, and speaking at conferences and meetups. Mateusz holds an MSc in computer science from AGH UST in Krakow.

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