Sebastian Bader | Semantic Technologies for Assisted Decision-Making in Industrial Maintenance

KIT – University of the State of Baden-Württemberg and
National Laboratory of the Helmholtz Association
KARLSRUHE SERVICE RESEARCH INSTITUTE (KSRI)
www.kit.edu
Semantic Technologies for Assisted Decision-Making in
Industrial Maintenance
Sebastian Bader
Research Associate

Institut KSRI9/29/20162 Sebastian Bader
sebastian.bader@kit.edu
Predictive Maintenance
• Forecasting break-down probabilities
Condition-Based Maintenance
• Discover failure patterns
Preventive Maintenance
• Specified service intervals
Reactive Maintenance
• Run to failure
Industrial Maintenance Process
!
Amount of unplanned downtimes

Institut KSRI
Improvement Areas
9/29/20163 Sebastian Bader
Dispatcher
Client
Technician
Machine
Remote support
Schedule
Tour
Local/global
planning
Real-time tour
optimization
Predictive
Maintenance
Information
provision
Semi-automated
decision making

Institut KSRI
Next Generation of Maintenance
 Reduction of unplanned downtimes
 Less travel time for field technicians by tour optimization
 Improved planning of resources and capacities
 Automated/Supported decision making where possible
 Automatic data exchange with customers/suppliers
 Integrating external services and competences
 Provisioning of contextualized information

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Challenges
 How can advanced data insights be used to create
business value?
 How can available data contribute to a more efficient
maintenance process?
 What are the current limitations and how can we solve
them?

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 Predictive Analytics provides flexibility…
 … to prepare resources
 … to organize technicians
 … to adjust capacities and demands
 Data-driven approaches reduce complexity…
 … by regarding all side effects
 … by suggesting appropriate actions
 … by supplying related information
Transforming Predictive Analytics into
Business Value
Dispatcher
SchedulePredictive
Maintenance
Capacity
Demand
Predictions at its own are not sufficient,
only the ability to react provides value!
Reducing uncertainty increases efficiency:
Therefore, an integrated support system
for the whole process is necessary.

Institut KSRI
System Integration via Semantic Web
Technologies
 Current systems already solve some challenges
 forecasting machine downtimes
 optimized scheduling of technicians
 real-time tour planning
 Need for addressing constantly added/removed resources
 New machine instances, types, technologies
 New customers, departments, partners
 Disconnected machines, expiring contracts
 Need for system integration across departments, organizations, and
countries
 Need for flexible, modularized and decentralized integration approach
Tour
SchedulePredictive
Maintenance

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Data Model: the Maintenance Ontology

Institut KSRI
System Integration via Semantic Web
Technologies
How to enable the integration of external services
with potentially unknown requirements, heterogeneous
access methods and varying data formats into a
decentralized network?
 Smart Web Services1 (SmartWS)
 Encapsulate context-based decision logic
 Lifting and lowering to agreed data format according to Linked Data Principles
 Access via HTTP and REST
 Self-describing and therefore automatically
controllable
 Consumer and producer at the same time
(=Prosumers)
Tour
SchedulePredictive
Maintenance
System
1
System
2
HTTP REST
RDF
Wrapper
library
Wrapper
library
Lifting
Lowering
JSON
mapping mapping
Output Functionality
Input
Provenance
2 Maleshkova, Maria, et al. "Smart Web Services (SmartWS)–The Future of
Services on the Web." The IPSI BgD Transactions on Advanced Research: 15.

Reusable SmartWS
Data Sources, Devices, Sensors, Wearables, Algorithms, etc.
Composite
Applications
SmartWS
Devices
SmartWS
Sensors
SmartWS
Algorithms
SmartWSSmartWS SmartWS
Execution
Engine
Reference SmartWS Architecture

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Web Services and Linked Data Platform
 Access to data
 Stored, managed and published through DBs
 Linked Data Platform2 for reading/writing RDF
 RESTful methods for data requesting and manipulation
 SmartWS provide Linked APIs with semantic descriptions
 Requesting Web services
 WSDL/SOAP or RESTful communication
Consistent handling of data and services
2 Speicher, Steve, John Arwe, and Ashok Malhotra. "Linked data platform 1.0." W3C Recommendation, February 26 (2015).

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Provision of Contextualized Information
 Identify topics and context
 Reports, manuals, posts
 Understand the current situation
 Dynamic information from heterogeneous
input channels
 Static knowledge on processes and
resources
 Modeling information objects as
resources, enhanced with meta data, in
a common manner
Technician
Machine
History
Task
Situation

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Social Maintenance Network
 “There must be someone who knows the solution
to my problem.
How can I find him? How can I access his expertise?”
 Implicit knowledge not queryable
 Segregation by organizational unit, language, region, …
1. Connect people depending on qualification, experience,
task, and availability
2. Supply available information where needed
 Solution:
Social network for fast and reliable communication and
adaptive information provision
Dispatcher Technician

 Platform for information and knowledge exchange based
on Linked Data representations
Semantic Media Wiki
Semantic MediaWiki = 𝑾𝒊𝒌𝒊𝒑𝒆𝒅𝒊𝒂 + 𝑺𝒆𝒎𝒂𝒏𝒕𝒊𝒄 𝑴𝒆𝒕𝒉𝒐𝒅𝒔
• Collaborative work
• Sharing knowledge
• Easy syntax
• Browser-based (stationary and mobile)
• Perfect integration with semantic
technologies
• Access on data views (near real-time)
• OLAP functionality
• Extendable platform

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Semantic Text Analysis and Similarity Matching
From Semantic Media Wiki to Social Platform
Task
Route
Chat
Help
Tools
Mobile application
Task X Machine Y
Task-related information views
Activity 1
Activity 2
Task A
Problem P
ID: 0053A435-ZD
Changing air filter of AC unit
Type: Cutter
Installed: 2011
Color: green
Location: Tech Inc.
Configuration: DFR-24
Mario Rossi
John Doe
Max MustermannJean Untel
Community support
Chat functionality
Procedure:
1.Open shell
2.Check power supply
3.Change fuse
4.Start test sequence
5.Check power LED
6.Detach wires
7.Lift filter
8.Insert new filter
9.Attach wires
10.Restart test sequence
11.Fill report
12.Let customer sign
13.Close shell
14.Start machine
History:
Oil pressure error
Vibrations
Regular maintenance
Installation
Client:
Name: Tech Inc.
Contact: Peter Müller
Tel. no.: 01234 555
Time: 9:00 to 11:30
Address: IoT Road 1
Smallville

MAINTENANCE SCENARIO
BUSINESS MODELS
CUSTOMER (LEASING)
Leasing inclusive repair commitment
MANUFACTURER
F
CUSTOMER (MACHINE OWNER)
Full-Service-Contract
MANUFACTURER/MAINTAINER
PLATFORM
@
SENSOR DATA
(periodic intervals)
BREAKDOWNPREDECTION
BREAKDOWNPREDECTION
;ANALYTIC RESULTS
component breakdown probability etc.
SENSOR DATA
measurements, conditions etc.
2
PREDICTIVE ANALYTICS
measurements, conditions etc.
IMPROVEMENTS
INCREASING EFFICIENCY
Shorter maintenance and travel times
INCREASING AVAILABILITY
Minimizing unexpected breakdowns
MINIMIZING MAINTENANCE COSTS
Reduced investigation time
MAXIMIZING TOTAL LIFETIME
Optimized maintenance
3
@

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Future Business Cases
 Full-Service Contracts
 Automated maintenance organization allows efficient risk
management
 Machine-as-a-Service instead of single sales event
 Strategic skill management
 Integrated modules enable the detection of missing/required skills
of work force
 Combination of operational planning with strategic simulations lead
to fact-based decisions
 Externalization of low profit tasks
 Marketplace for external maintenance provider
 Gradual access to sensitive technical information

Institut KSRI
Conclusion
 Semantic Web Technologies enable a flexible and decentralized
integration of heterogeneous resources.
 Consistent data modeling with RDF for a system-wide information
access
 Smart Web Services encapsulate automated decision logic in
order to reduce complexity and increase processing speed
 Semantic annotations of documents, situations, and employees
allow context-related information provision
 Semantic Technologies enable more efficient industrial
maintenance processes with new business models

Institut KSRI
Acknowledgements
This work is partially supported by the German Federal
Ministry for Economic Affairs and Energy (BMWi) as part of
the “Smart Service Welt” program under grant number 01
MD16015 B (STEP)

Sebastian Bader | Semantic Technologies for Assisted Decision-Making in Industrial Maintenance

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Sebastian Bader | Semantic Technologies for Assisted Decision-Making in Industrial Maintenance