Software Engineering Integration For Flexible Automation Systems

Stefan BifflChristian Doppler Laboratory SE-Flex-AS

Institute of Software Technology and Interactive Systems (ISIS) Vienna University of Technology

http://cdl.ifs.tuwien.ac.at

Tool Mec.

Tool Elec.

Workflow

Analysis

SCADA

Tool SW

Model Mec.

Model SW

Model Elec.

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(Software+) Engineering for Automation Systems

Increasing flexibility and complexity of software-intensive systems(Software+) engineering: the models of non-software engineers contain requirements and design constraints for software engineers.Surprisingly little work on the flexible, efficient, and robust integration of engineering tools across engineering disciplines.

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Example: End-to-End Test Across Engineering Models

Sensor

V_AS2S3S4S5

V_BV_CV_D

S1

SystemInterface

SoftwareInterface

Software„Behavior“

Wiring Configuration Use of Data

Electrical Engineer Configurator Software Engineer

3

Engineering Research Challenges

Basic research challengesEarly defect detection across engineering discipline and tool boundaries.Engineering process analysis using design- and run-time data sources.

Research applications in the industry partners’ domainsPlatform to build integrated tools for automation systems development & QA.SCADA systems with data analysis for monitoring automation systems.

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Integration Challenges and Requirements

Challenges from weak integration of software tools for engineering1. Engineering process on event level is hard to track and analyze.2. Integration of software tools is often vendor-specific and/or fragile.3. Sharing of data models across software tools is inefficient and risky.4. Run-time defect detection cannot easily access design knowledge.5. Integration of run-time environments is hard to observe for analysis.

Requirements Management

Pipe & Instrumentation

Electrical Plan

Process Engineer

Software Engineer

Project Manager Tool Data

Tool Data

Tool DataElec. Engineer

Flexible Automation System

Software Dev. EnvironmentTool Data

Operator

Maintenance Engineer

SCADATool Data

Anfoderungs-ManagementOnsite Eng. EnvironmentTool Data

Data Analysis/Simulation

Tool Data

4

2

1

Design Time Run Time

5

3

Software Engineering Integration

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Research Approach – Automation Service Bus

Technical Integration: Engineering Service Bus (1), Control Service Bus (2).Semantic Integration: Engineering Knowledge Base (3).Flexible integration of SCADA (4) with data analysis/simulation (5).Defect detection approaches for design time (6) and run time (7).

Goal: Approaches for the integration of software tools in automation engineering.

Aut

omat

ion

Serv

ice

Bus

(Offs

ite)

Engi

neer

ing

Serv

ice

Bus

ASB

(Ons

ite)

Con

trol

Ser

vice

Bus

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Semantic Integration of Engineering KnowledgeIdentification of common concepts across engineering disciplines

„Pump flow“ Real (l/min)0 to 1,200%I20.5.3

InformationAnalog 0 to 10 VX.22.2.1

Software Engineer

Process Engineer

ElectricalEngineer

Tool A Data Model Tool B Data Model

Domain/project data model

Mechanical equipment properties

Transmission linesTerminal points

Data TypesLogical Behavior

RequirementsLocation IDsComponents

Interfaces

Tool C Data ModelSignals (I/O)

Machine vendor catalogue

Model Mapping Tool A – Domain

Derived MappingTool A – Tool B

Models

Common concept

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1

2

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Current Issues on data integration from heterogeneous tools

Semantic Integration of Engineering Tool Data

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Defect Detection Across Engineering Models Use of common concepts in models across engineering disciplines

Defect type examplesMissing, wrong, inconsistent model elements or relationshipsConflicts from changes of overlapping model elementsRun-time violation of model constraints

Defect detection approachesReview of overlapping model partsAutomated check of model assertions (syntactic and semantic)Change conflict detection and resolutionDerivation of run-time assertions

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Practical Example: End-to-End Analysis

SELECT ?Electric_ID, el:desc, el:type, ?Config_ID, ?SW_ID, sw:desc, sw:type

WHERE {el:E_short ekb:mapsTo ?Electric_ID.?dom:Electric dom:Electric_ID ?Electric_ID.?dom:Electric dom:Config_ID ?Config_ID.cfg:C_short ekb:mapsTO ?Config_ID.?dom:Configuration dom:Config_ID ?Config_ID.?dom:Configuration dom:SW_ID ?SW_ID.sw:S_short ekb:mapsTo ?SW_ID.?dom:Software dom:SW_ID ?SW_ID.

}9

Example Query Result(S1, “pressure”, “mbar”, C1, V_A, “pressure”, “mbar”)(S4, “pressure”, “mbar”, C3, V_B, “temperature”, “kelvin”)(S2, “level”, “cm”, C5, V_C, “level”, “m”)

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Combining Engineering Models and Run-Time Data

Challenges of working with run-time dataNo engineering models at run time

data hard to correlate and analyzeNo security concepts for run-time data (e.g., recipes, diagnosis information)

Semantic integration as solutionAdd knowledge on engineering data model to run-time data collectionDefine access rights to run-time data

Practical benefits Locate defects by correlating sensor dataPrioritize local alarms in the plant context

Secure access to sensitive data– Process engineer recipes– Maintenance engineer diagnosis– Plant manufacturer remote maintenance

Tool Mec.

Tool Elec.

Workflow

Analysis

SCADA

Tool SW

Diagnosis

Automat.

Temp. Sens. Mid.

Temp. Sens. Top

Temp. Sens. Bot.

Level Sensor

Fill Pump

Ext. Pump

+ On/Off+ RPM

Pump

+ Type+ Temperature+ Temp. Range+ Operating hours

Temp. Sensor

+ Level+ Level Rate+ Distance+ Signal Strength+ Level Range

Radar Level Gauge

+ Address+ Description+ Position+ Access Level

Process Data Object

+ Flow+ Flow Range

Flow Sensor

ContainsElements

+ Medium Vol.

Tank

Instance of

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Summary – Christian Doppler Laboratory Software Engineering Integration for Flexible Automation Systems

Close fundamental gaps in scientific knowledge on – Defect detection across engineering disciplines– Defect detection using design- and run-time data– Engineering process analysis: find sources of defects.

Empirical studies with industry partners on – Technical and semantic integration of software tools

and data models.– Defect detection, location, and recovery approaches.

Applications– Better integrated development and QA tools for industrial automation

systems engineering with the “Automation Service Bus”.

– SCADA systems with data analysis for monitoring automation systems to detect defects before they occur.

Visit us online at http://cdl.ifs.tuwien.ac.at

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