Industrial IoT: Architecture Framework Use Cases. Artur Borycki Teradata Labs

Transcription

1 Industrial IoT: Architecture Framework Use Cases Artur Borycki Teradata Labs

2 IoT represents more than just things : It must represent systems (and systems of systems)

3 The Internet of Things: It s About The Sensor Data, Stupid! 1 Sensors sometimes lie 2 Even when they are not lying, sensors may not tell the whole truth 3 Extracting useful signal requires multi-genre Analytics and additional data 4 Sensors typically don t measure the quantity of interest directly 5 By itself, sensor data is frequently not actionable Don t assume that sensor data is accurate, complete and consistent; do apply Information Management bestpractices. Capture raw sensor data wherever possible; otherwise, understand how sensor data has been summarised. Plan to support a wide variety of Analytics path / pattern / graph / time-series / text - just to prepare an ADS from time-series data. Some model scoring may take place on the smart device itself; build models centrally, but be prepared to deploy them widely. Integrate your Data Lake and Data Warehouse environments, so that observation and transaction data can be joined.

4 Real value requires a system (and system of systems) view: INVESTMENT FOCUS VALUE FOCUS Data Store Sensors Edge Integrate Analytics System of Systems Systems Science or Theory ACTING THINKING OPTIMIZING 4

5 IoT: Enables lots of metrics to be passed around to add value : Analytics at scale Movement Heart rate Sleep Metric phy

6 IoT: Enables lots of metrics to be passed around to add value : Analytics at scale Movement Heart rate Sleep Metric phy

7 IIoT: Raw data is too siloes to allow analytics at scale No analytics at scale

8 Teradata IIoT Architecture:

9 Teradata Enterprise IoT Architecture: Public Data (Open API) Distributed Systems. ( Global / Local (Regional) ) Centralise Data Warehouse Devices Devices Control System Control System Buffer Server Execution Cold Standby Streaming Framework Persistent Landing Zone Integrated data Reports / Enterprise Applications Devices Control System Buffer Analytical Objects Dashboards Data R&D Analytical Objects Data Labs HPC Public API Applications Visualization Consumer BI

10 Lets start talking details: Dashboard Service Reporting Service Bulk Push Service Plugins: Access Framework Spark sqoop NoSQL Solr Teradata Aster RestAPI Config Service Pipeline Registry Dependency Management Scheduling Service Config Service Plugins: Processing Framework Advanced Analytics & ML STEM Aster Map Reduce MQ SQL (Hadoop) Stream Spark Storm Teradata Table Operators MLlib R/h2o Aster Python R Fortran Config Service Validation Module Metadata Module Packaging Module Upload Service Bulk Collection Service Stream Collection Service Plugins: Collection Framework fs SAN NFS S/FTP scp S3 JMS *MQ Kafka Listener Provenance Service Logging Service Authorization Orchestration Service HDFS S3 NoSQL In-memory Teradata Data Governance Framework Data Storage

11 Teradata IIoT: Use cases

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13 What the hell!!!???

14 Understanding the challenge Technology Challenge Business Challenge Highly skilled engineers spending too much time loading / manipulating / analyzing data Unable to look at all the data need to split up into smaller sub-sets Product Complexity Time to Insight Data Volumes Failure Number of Variable IT Cost & Complexity Multi-Petabyte Data Volume Million+ Variables Constantly Changing Structure Data Storage (cost effective) Scalable Analytical Engine Visualization / Front End Analytical Challenge Process to identify Key Variables so Action can be Taken Large scale multi-linear regression High amount of dimensionality Need to operationalize analytics Y i e l d Action Teradata

15 Insight every day / hr / sec without any code changes 1. Take Sensor data that is highly variable and very wide 2. Convert to JSON format for flexible schema and load into Teradata 3. Un-pivot data from columns to row in Teradata RDBMS 4. Use standard Teradata SQL to access wide sensor data and execute applied mathematics at scale Real optimization comes from changing the right processes and inputs not from changing the data structures or code

16 UNPIVOT

17 Wafer maps

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19 Oil Exploration Survey: Tens of Thousands of Seismic Traces, which must then be processed Sources: Air Gun Array Receivers Each Receiver collects one 6-8 Second Trace, each containing several thousand time-points Example: 8 seconds of collected 4ms sampling rate yields a 2000 entry trace Vessel can tow 10K++ receivers meaning 10K++ traces collected per shot

20 Case Study: Caterpillar Large Power Systems Division Sensor Data Qualification Challenge Producing qualified data sets for data engineers to minimize wasted effort on data wrangling of sensor data Solution A rules-based engine to set up and execute data management rules to deal with: Light analytics and visualization that potentially gets pushed to the edge computing device Results Actual (and Expected) Productivity improvements: Reduction in data engineer effort through standardization of data wrangling; data payload available 6 days earlier! Promise of reduction in central compute platform resources and network bandwidth

21 Siemens Digital Services powered by Sinalytics Example: Predictive maintenance of trains and locomotives Results Improved asset availability Rail Transport Market drivers Rail operator challenges Rail user demands Trains/Locomotives Rail vehicle engineering Mechanical vibrations Sensor properties Maintenance operations Data Science Pattern identification Machine learning Automated alert generation Avoidance of unnecessary maintenance Reduction of maintenance costs Domain know-how Context know-how Analytics know-how Customer value Siemens

22 Maintenance: Build a predictive model for engine failures on trains: Business Objective Avoid unplanned train downtime through prediction model Repair trains before failure occurs, secure uninterrupted process Data Challenge Sensor data from engines and data from maintenance management system Understand patterns to failure (pattern analysis) Understand correlation of errors / sensor readings around a failure Based on this insight, build a predictive model Example: daily pattern of temperature readings mid low mid often occurs 3 days prior to an engine problem Solution Unified Data Architecture which includes Teradata Integrated data warehouse, Aster discovery platform and Hadoop Leverage Aster to perform discovery analytics Sensor data stored in Hadoop Scoring of predictive model performed in Teradata Warehouse Opportunity to Impact Pre-Dispatch required spare parts in time Avoid unplanned downtime, penality, process interruption Save time on failure analysis

23 Smart Cities Weather Data & analytics Smart Government Smart utility services Smart buildings The Smart City Electric vehicles Enhanced safety & security Smart healthcare Smart homes Geolocation services Citizens, as customers, communities, prosumers Integrated, personalised retail Integrated travel & transportation Social interaction The Internet of Things (IoT) Smarter communications services

24 Why Smart Cities Need Integrated Data: Independent Investigations Intersecting Correlations

25 Car systems (today): Various technologies work in conjunction with each other to make a car autonomous. LIDAR Sensors RADAR Detectors GPS INS Electromechanical Systems Software and Algorithm

26 Avatars (virtual car): Decouple physical world from digital world Model the function independently from the device Not all data needs to be stored Devices can change, but function remains Keep track of devices Similarities with the Party Persona construct in Retail LDM Physical Digital Device Shadows ( Avatars ( Virtual Actor Model (

27 What is system of systems in this case: GPS... Cars (n) Computation (model build, AI algorithms, Global Neural Network and Genetic Algorithms,...) Radar Static maps Radar Train data (AI) Traffic system Historian (analytics at scale) Emergency data (Accidents, construction,...) Control Center Route data and history Emergency data

28 How far can I go on the remaining charge? What are the road conditions ahead? Traffic? What happened on the last hard brake? I want access to my . I want to schedule service. What else should I have checked while my car s being serviced? I want my car to be a wi-fi spot. What remote services do I use most? Does my car need an upgrade? I want to stream my music. I want to find my [stolen] car. I want my car to remember me. How risky is my teen s driving?

29 THANK YOU: