機器人的預測性維護實戰：解讀實時、可擴充套件的分析管道 [session]

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機器人的預測性維護實戰：解讀實時、可擴充套件的分析管道

講師：Mathieu Dumoulin (MapR Technologies), Mateusz Dymczyk (H2O.ai)

14:50–15:30 Friday, 2017-07-14

物聯網&實時計算 (IoT & real-time), 英文講話 (Presented in English)

地點： 多功能廳6A+B（Function Room 6A+B)

觀眾水平: Intermediate

必要預備知識

A general understanding of big data technologies and machine learning

您將學到什麼

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

各種工業4.0的物聯網應用承諾通過降低停機時間、提升產品質量和提高生產效率來獲取很高的產能回報。現代化的工業機器人整合了數百個各種型別的感測器，併產生了蘊含豐富價值的海量資料。然而，現實卻是即使是一些世界最尖端的工業企業也僅僅只是開始使用相對簡陋原始的、定製化且造價高昂的監控系統來利用這些資料。

我們相信，現在已經是可以在月級的時間週期裡，使用大浪淘沙後勝出的企業級大資料產品和開源專案來成功地部署工業4.0實驗性使用案例，而且花費也僅是全球領先高科技企業裡相同專案花費的一小部分。我們會展示一個這種系統的可用原型，並一定程度上解釋怎麼構建它。

這個可用系統是一個預測性維護案例的實現。只有聰明地使用現代化的基於微服務的流式架構才讓這一切成為可能。這個系統利用了MapR聚合資料平臺（MapR Converged Data Platform）的獨特特徵來進行操作分析、訊息系統和儲存。機器學習的建模和部署則是使用H2O.ai來實現的。

我的演講描述了一個可用的實時、基於機器學習的異常檢查系統。我們展示了一個安裝了無線移動感測器的工業機器人模擬器。我們的系統通過一個雲端叢集進行資料計算。為了增加現實性，我們現場展示的系統會包括一個擴增實境（AR）頭盔來展示機器人的實時的負載狀態。

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講師介紹：

Mathieu Dumoulin (MapR Technologies)

Mathieu Dumoulin is a data scientist in MapR Technologies’s Tokyo office, where he combines his passion for machine learning and big data with the Hadoop ecosystem. Mathieu started using Hadoop from the deep end, building a full unstructured data classification prototype for Fujitsu Canada’s Innovation Labs, a project that eventually earned him the 2013 Young Innovator award from the Natural Sciences and Engineering Research Council of Canada. Afterward, he moved to Tokyo with his family where he worked as a search engineer at a startup and a managing data scientist for a large Japanese HR company, before coming to MapR.

Mateusz Dymczyk (H2O.ai)

Mateusz is a Tokyo-based software engineer at H2O.ai, the maker 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. After Fujitsu he moved to Infoscience to work 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.