A Practical Example to Apply Semantic Technology in Remote Condition Monitoring

Transcription

1 A Practical Example to Apply Semantic Technology in Remote Condition Monitoring YUAN, Yong, Senior Key Expert, Corporate Technology, Siemens Ltd. China Unrestricted Siemens AG 2016 All rights reserved.

2 Self Introduction YUAN Yong Education Background 1) Major in Electronics Engineering for Bachelor ( ) and Master degree ( ) in Yunnan University; 2) Electronics Engineering for PhD degree ( ) in Huazhong University of Science and Technology; Work Experience Have been working in Siemens for 10 years from 2005 with focus on industrial wireless communication technology and plant cloud service relevant topics; Research Interests Semantic technology; Plant data service; SOA related technology; Industrial wireless communication technology; Past Project Highlights 1) Domain ontology setup for car assembly line; 2) Application of semantic technology for remote condition monitoring in some real use case; Page 2

3 Basic Vision for Siemens Plant Cloud Services: open cloud platform Open cloud platform Open standard (OPC) for connectivity of Siemens and third-party products Plug and play connection of Siemens products (engineering in the TIA Portal) Cloud for industry with open application interface for individual customer applications Siemens industry apps: asset analytics, energy analytics, industrial network analytics, etc. Picture from On the way to Industry 4.0- The Digital Enterprise, Klaus Helmrich, Hannover Messe, 2015 Page 3

4 Real use case: Chinese SMEs see plant data service as essential for their business development Pilot partner: OEM Partner for Car Assembly Line Facts & Figures Automotive production line equipment provider with big market share About 30% of the orders have service contract attached (1 year service after installation) Page 4 Pilot Background/Objective 1) New business model, Production Line as a Service: Transparent pay-per-use pricing model Opportunities for completely new business models (e. g. selling machine hours) 2) Technical needs: Reliability guarantee; Reduction of maintenance cost; Reduction of the spare part stock for maintenance;

5 Requirements and Opportunities for Chinese SME Requirements: 1) Besides reliable product/system, remote data driven service, e. g. preventive maintenance, has been identified as a key business driver and differentiator by many Chinese SMEs. Increase OEE Increase maintenance quality Decrease spare parts Decrease maintenance cost Professional image Reducing downtime, Less number of emergence repair, Historical report Fast fault location Optimize planning Positive service Less spare parts stock Avoid redundant replacement Fewer engineers Less dependent on experts Apply government bonus on technology update & innovation Opportunities: 1) New business model: Production line as a Service, Pay-per-use pricing model Page 5

6 Step 1: to Identify the Critical Components Page 6

7 Step 2: to Mount Sensors and Carry out Project Specific Commissioning Work Manual dashboard and report generation Manual configuration for data acquisition: trigger, preprocessing Manual site description by Visio drawing Page 7

8 Key Challenges in Real Project Deployment Lack of digital site description: have to rely on Visio, photos and talk Manual configuration for data acquisition Tricky trigger definition: Need to capture right data at the right time Manual data analysis: Dashboard and report are manually done Vibration data size comparison(one cycle,50s) Raw Data size Data point quantity Filtered data size Total Saved 9MB 2 110KB 17.89MB Modeling Tool High threshold and efforts for onsite engineering makes solution not easily repeatable Page 8

9 Modeling Tool: Basic Idea Domain specific templates are prepared as semantic model through the demo case; Templates and generated model can be imported and exported as XML/RDF files; Next step: Import standard templates model, for example through PLM software Page 9

10 Modeling Tool to Annotate Data Content with Context Information Events Processing Engine Events with semantic tag by correlating raw data from different sources Modeling tool Physical setup with raw data Page 10 Information model defined by semantic keywords

11 Key Challenges in Real Project Deployment Lack of digital site description: have to rely on Visio, photos and talk Manual configuration for data acquisition Tricky trigger definition: Need to capture right data at the right time Manual data analysis: Dashboard and report are manually done Vibration data size comparison(one cycle,50s) Raw Data size Data point quantity Filtered data size Total Saved 9MB 2 110KB 17.89MB Modeling Tool Smart Data Logger with App Container Page 11

12 Smart Data Logger with App Container OPC UA Interface Industrial App Store Smart Data Logger Embedded Linux Based OPC UA Adapter App Container Driver Sensors Interface Sensor Page 12

13 Key Challenges in Real Project Deployment Lack of digital site description: have to rely on Visio, photos and talk Manual configuration for data acquisition Tricky trigger definition: Need to capture right data at the right time Manual data analysis: Dashboard and report are manually done Vibration data size comparison(one cycle,50s) Raw Data size Data point quantity Filtered data size Total Saved 9MB 2 110KB 17.89MB Modeling Tool Context Broker Page 13

14 Context Broker with Knowledge Base Layout Engine Diagnosis Engine Report Generator Modelling Tool Context Broker Knowledge Base Common Knowledge Domain Knowledge Site Setup e.g. definition of drive e.g. conveyor line driving unit e.g. MotorA drives GearboxB Page 14

15 Example to Setup Domain Knowledge PROV SSN Provenance Sensors description EDDL FONM Vibration at motor is transmitted to the gearbox via shaft connection Vibration at motor is transmitted to the foundation by a linkage functionality and a connection. Vibration in the foundation is transmitted to the gearbox by a linkage functionality and a connection Device description QUDT Quantities, units Objects, devices, functionality ecl@ss Functional taxonomy Page 15 Core ontologies used

16 Knowledge Based Automatic Analytics and Report Generation Use of knowledge base: Automatic analytics for the motor health status by the knowledge of force transmission; Automatic diagnostic report generation with root cause identification; Page 16

17 Conclusion Big market from Chinese SMEs for plant data service Threshold and efforts for project specific engineering works must be reduced for repeatable solution Semantic technology plays an important role for modeling & analytics to simplify the project specific engineering works Page 17