Advanced sensing and its Decisive Role for Digitization · Digitally enhanced systems. Operation and system optimization ... Starting as monitoring / redundancy applications 2. Creation

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SOCIETAL AUTOMATION 2019, KRAKOW

Advanced sensing and its Decisive Role for Digitization

Jörg Gebhardt, Ulf Ahrend, Markus Aleksy, Matthias Berning, Francisco Mendoza, Dirk Schulz, Thomas Gamer, Stephan WildermuthABB Corporate Research Center Germany, Ladenburg

2019

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Sensing in an Industrie 4.0 context: Starting point or target of many good ideas

Non-invasive temperature measurement: Why interesting, and paradigmatic?

Decisive sensor properties in the future

Autonomy, a vision

Use cases for 5G: TACNET4.0

Conclusions

Agenda

September 21, 2019 Slide 2

—Sensing in Industrie 4.0What is it all about?

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Increased productivity: operating time, speed, yield

Digitalization opens up new opportunities

September 21, 2019 Underlined : Example areas where sensing can be essential4Slide

Real-time monitoring

Greater reliability

Maintenance optimization

Faster installation, more configurability

Power quality

Better integration of renewable energies

Management of volatile supply and demand

Real-time control and monitoring

Smart asset management

Planning optimization

Differentiation via intelligent products, communication and software

Digital equipment Digitally enhanced systemsOperation and system optimization software

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Users and providers must note these

Requirements for seamless introduction

September 21, 2019 5Slide

Investment protection

Stability of the production system

Controlling data flows

Standardization

Cybersecurity

Ensure step-by-step introduction of Industry 4.0

Industry 4.0 services must not put current production at risk

Plant upgrades must be transparent and controllable

Deploy the full potential of Industry 4.0 by using standards

Ensure data protection and data integrity

—The important role of sensors in Industrie 4.0

September 21, 2019

References: a) VDI/VDE-GMA: Technologie-Roadmap “Prozesssensoren 4.0“ (2015)b) Namur Open ArchitectureSlide 6

Agile, short-term roadmap for conservative industries

Enterprise resourceplanning

-------------------------Manufacturing execution

systems--------------------------

Automation level: control, optimization, maintenance

---------------------------Field level

Secure

Open

Concept

– Industrie 4.0 starts as an additive extension of automation pyramid

– Significant improvements of cost per sensor: Open and integrative approaches

– No risk created for installed base

Non-invasive ideas

1. Starting as monitoring / redundancy applications

2. Creation of new optimization / maintenance use cases

3. Perspective on independent use cases in control

State-of-the-artautomationpyramid

Industrie 4.0Extensions andadditions

Area for newsensors andsmart automation

1

32

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September 21, 2019 7Slide

A first example: Intelligent motors for digitalization

Easy to deploy as smart sensor is a small

wireless package and simple to use

Reduces motor downtime by up to 70%

Extends motor lifetime by as much as 30%

Reduces power consumption by up to 10%

ABB’s smart sensor accurately measures key motor parameters at regular intervals. Using a built-in wireless communication interface, it transmits the data via a smartphone or gateway to a cloud-based secure server.

The smart sensor enables remote condition monitoring for LV motors through the Internet of Things, driving productivity and efficiency.

Technology description Customer benefits

ABB solution

ABB AbilityTM Smart Sensor

—Non-invasive measurement


Synergies :

– Embedded intelligence

– System-level AI / machine learning

– Autonomous devices

• Reliable wireless communication

• Energy harvesting

References e.g.:

1. VDI/VDE-GMA: Technologie-Roadmap “Prozesssensoren 4.0“ (2015)

2. ABB Review 04 | 2015: “Absolut zero invasion“

3. Sensoren und Messsysteme 2018 “Improved measurement of surface temperature: an enabler for widespread non-invasive T-measurement in industry”

Drivers:

– Digitalization and Industrie 4.0: Quest for high-quality data

– Quest for performance optimization

– Efficient use of workforce

Benefits:

– Flexibility in measurement, greenfield or retrofit

– Installation time reduction: sometimes from months to hours

– Often significant reduction of effort per measurement point (e.g. no process interruption, no pipe emptying)

– Process remains undisturbed

Why, and why now?

Non-invasive sensing can prove to be a game changer in industries like: chemical, food & beverage, oil & gas, power

—Non-invasive temperature measurementTechnology reasoning

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Easy to install / to change / to maintain

Safe against harsh process conditions

– High flow

– Abrasion

– High pressure

Less disturbance for the process

– No pressure loss

– Flow profile unperturbed

Ideally combined with energy harvesting no-cabling - option

And more, as learnt in practice

– Surface measurement often faster than invasive/thermowell

– Reduced variety, easier engineering

– Synergy with Industrie 4.0 via temporary measurement, flexibility and low cost per measurement point

Non-invasive T- measurement


Appealing features in general

—Specific ABB-Concept for Non-invasive Temperature


Device as such– Non-invasive concept: Strong reduction of installation cost and

time – Performance close to invasive: accuracy + response time– ABB technology: specific sensor tip, model-based correction– User applications´ development ongoing

Synergies for customers – Industrie 4.0 – Machine learning / AI on component or plant level

Enabling activities for Non-Invasive Technology– Large potential to replace / complement brownfield

installations(e.g. large opportunity as redundancy sensors)

– Non-inv. Temperature is first example of serious, flexible, pervasive sensing

Device Features, Achievements, and resulting Opportunities

ABB´s specific sensor technology

Model-based Sensing

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From welding to clamp onFigure 1: Noninvasive Temperature at a glance


Traditional invasive

Tm

TSP 341 –N, Non-invasive temperature sensor

Tmedium

No process penetration

Accurate real time model based measurement of Tsurface

Model based inference of Tmedium

No explicit need for insulation

Use of standard Insets

Unique double sensor tip design

mediumwall

Primary sensor in contact

Offset referencesensor

—Figure 2: Double-sensor design and reasoning


Reduction of response time from ≈ 1000 sec to 20 sec

Response to an ideal T-step from 20 °C to 80°C at t=0 (see ref. [3]):Model-based simulation of the signal´s approach to equilibrium, i.e. here to 80°C.Curves from left to right correspond to increasing reference sensor distance d from the primary sensor. The primary sensor is assumed to be at position x1 = 0.01m, and the reference sensor at x2 = x1 + d. Note the logarithmic time scale.Conclusion: The device responds considerably faster if the two sensors are located close to each other.

d

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Comparing ABB Non-invasive sensor to traditional temperature measurements – Step response test

Non-invasive Temperature Measurement


Test – Pumping 77°C fluid instantaneously into pipe with fluid at 22°CPipe – Stainless Steel, DN80, 3 mm wall thicknessFlow – 14 m3/Hr (Re>10000)

ABB Research Facility, Germany

Traditional Thermowell/inset

Industrial Surface Temperature

Sensor

ABB Non-invasive Sensor

Laboratory Surface Sensor

Glued PT100

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Comparing ABB Non-invasive sensor to traditional temperature measurements – Step response test

Non-invasive Temperature Measurement


ABB Non-invasive sensor matches thermowell, outperforms traditional surface measurements!

ABB Non-invasive temperature sensor

ABB Non-invasive Sensor

Open valve

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Invasive TT

ABB Non-invasive TT

Example - Comparing ABB Non-invasive sensor to traditional temperature measurements

Why should I use non-invasive approach? Keep your measurement quality

September 21, 2019 Conditions:Slide 16

ABB Non-invasive Temperature sensor matches thermowell performance for process control applications!

Flow

Invasive Temperature Transmitter (TT)

ABB Non-invasive Temperaturesensor (Test Series)

Dev

iati

on fr

omin

vasi

ve T

T

PerformanceABB Non-invasive - Invasive TT

Average difference -0.68°C

Standard deviation 0.14°C

—Figure 5: Models 1 and 2


Infer Tsurface and Tmedium , respectively

Flow ( velocity, viscosity, heat conductivity etc.)

Tprimary

Treference

12

Tmedium

TsurfaceProcess pipe

Basic observation: Tsurface is almost equal to Tmedium in many relevant situations,such as aqueous solutions with turbulent flow.

Model 1 : Used to calculate Tsurface from Tprimary and Treference

Model 2 : Used to judge if Tsurface ≈ Tmedium , (based on application parameters)and, if not, to calculate a compensation

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Governing equationsHeat equation in solidsNavier stokesThermal convection in fluidsTranslated into thermal resistances : Rbl , Rw , RF

– Boundary layer

– Wall

– Insulation + convective resistance

Procedure to find the mean fluid temperature– Te is measured

– Twa is calculated by a Model No 1

– Tm is calculated by a Model No 2

Complex thermo-hydro-dynamical situation

Concept for temperature difference calculation


For many use cases: Surface and medium temperatures are almost equal. Model No 1 already sufficient.

12

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Example for water, as a typical low-viscosity fluid

Model 1 alone: Calculated relative deviation (Tfluid – Tsurface) / (Tfluid – Tambient)


Quite difficult to create laminar flow e.g. in a DN80-pipe

For small pipe diameters (< 3 cm), good results are expected for all velocities.

Already for quite low velocities of > 10 cm/s errors will be small for all diameters

Here, Model No 2 is not necessary, i.e. no process parameters are needed in planning

Deviations Tfluid – Tsurface are very small for most water-like applications

Results from empirical formulaeallowed region(in log-log-scale)

Relative T-deviation

—ABB Noninvasive Temperature measurement

Cf. ABB business unit „Measurement and Analytics“ September 21, 2019 Slide 20

Traditional DCS Use Case Monitoring & Optimization Use Case

Connected to Cloud Applications for monitoring, data mining, asset optimization

Possible integration scenarios

Connected to a DCS for providing process values

AbilityCloud

AbilityEdge

HARTMultiplexer

—Use Cases for non-invasive temperatureWhat is the benefit?

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Detection of e.g.:

1. Plugged equipment

2. Fouling of process containment, e.g. pipe walls

3. Degradation of process efficiency: Pumping, heating, reaction rate etc

4. Degradation of existing instrumentation: Cross-checking sensors´ and actuators´performance

5. And many others ….

Non-invasive temperature use cases


Typical features of process optimization potential

Redundancy installations and independent installations both generate very interesting use cases.

—Non-invasive instruments – Customer cost reduction potential


Example: Adding 4 temperature instruments for heat exchanger monitoring

Many relevant use cases involve multiple installations. Unfeasible without low-effort sensing.

Shutdown required

Considerable installation Cost + Time

Shutdown not required

Strongly reduced installation Cost (↘ by approx 25 %) andTime (↘ from months to hours/days)

TT TT

TTTT

—Further example: Monitoring of electrical switchgearAdded value of low cost infrared array sensors

—Monitoring & diagnostic of electrical assets


Motivation and overview

Benefit of continuous diagnostics: Avoidance of catastrophic failures, reduced downtime of assets and predictive maintenance.

Typical M&D approach Medium voltage switchgear

M&

D c

hain

sensing

lifetime modelling

physical quantities

raw signals

failure prediction

conditioned data

data for lifetime

conditioning

feature extraction

domain knowledge

device know-how

failure modes

data analytics

big data

artificial intelligence

Thermal monitoring

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Technology demonstratorDevelop a technology demonstrator for MV switchgear combining:

– Infrared camera for thermal imaging

– Additional sensor functionality in the same housing

Accurate temperature monitoring of critical points

Monitoring system for MV switchgear


Low-cost thermography for advanced temperature supervision

Overlay of IR & visible image

IR sensor mounted in SWGHot-spot extrapolation

—Decisive sensor properties in the future

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Installation

– Wireless

– Non-invasive

Commissioning

Building upon existing technology:

– Communication infrastructure

– Economy of scale from consumer domain, components off the shelf

Maintenance

– Large battery lifetimes via low-energy consumption

– Clear-cut calibration concepts

Low cost


Minimize costs via specific features

—Competing design targets


Technical detail

Energy consumption is often the main design driver.

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Globally unique identification

Standardized communication using IP-based protocols (OPC-UA, TSN, 5G …)

Machine-readable semantics avoids manual engineering effort

Virtual description, digital twin

Security and authentication concept

Non-functional sensor requirementsCommunication and signal processing

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Resources´ scarcity and commoditization of production

Digitalization

Variability of inputs require quick process adaptions

Variety of output

Demography, efficient use of workforce, know-how

Regulations, societal expectations

Smart Sensing: Major trends

—AutonomyA vision of the future in various domains

—The transition to autonomous systems in industry

April 2, 2019https://new.abb.com/news/detail/15115/abb-leads-the-way-to-the-autonomous-industrial-future, https://youtu.be/_yx82bDSTzE)Picture source: ABB YouTube channel (

Slide 33

The next development step

Autonomous system:– has learning-based capabilities – adapting to changing conditions– that are not pre-programmed or anticipated in

the design.

Automation system:– needs little human operator involvement – using well-defined tasks – that have predetermined rule-based responses– in reasonably well-known and structured

environments.

Artificial Intelligence as

key driver

Thomas Gamer, Mario Hoernicke, Benjamin Kloepper, Reinhard Bauer, Alf J. Isaksson: The Autonomous Industrial Plant -Future of Process Engineering, Operations and Maintenance, Proceedings DYCOPS 2019

https://youtu.be/_yx82bDSTzE

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Handle increasing complexity of Industrie 4.0

systemsLot size one production

Improved worker health & amplify human potential

Bring out and accelerating new innovations

April 2, 2019 Slide 34

Value proposition of autonomy

Higher productivity / yield and increased quality

Lower cost and energy consumption

Enable new business models and value

propositions

Opportunities currently not imagined at all

The transition to autonomous systems in industry

—Taxonomy of autonomy levels in industry

—Autonomous drivingDefinition based on SAE & NHTSA standards

Sep 13, 2018 Autonomous driving definition is based on SAE & NHTSA standards.* Picture source: Link, By Ian Maddox [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)], from Wikimedia CommonsSlide 36

0

1

2

3

4

5

no autonomy, no assistance

driver assistance: speed or lane position

occasional self-driving: speed and lane position

limited self-driving

full self-driving in certain situations

full self-driving in all situations

Driving in traffic with Tesla's autopilot controlling distance from the lead car and centering the vehicle in the lane*.

Tesla Autopilot system is considered to be an SAE level 2 system.

https://commons.wikimedia.org/wiki/File:Tesla_Autopilot_Engaged_in_Model_X.jpg

—Taxonomy of autonomy levels in industry

April 2, 2019 Gamer, T. and Isaksson, A., “Autonomous Systems”, ABB Review 8/2018, p.8-11, 2018Slide 37

Autonomy levels in industry

Core dimensions:− Role(s) of human− Scope and complexity of automated

tasks (i.e., system boundary)− System capabilities and intelligence

No autonomy, humans are in complete control.Level 0

Assistance with or control of subtasks. Humans always responsible, specifying set points.

Level 1

Limited autonomy in certain situations. System alerts to issues. Humans confirm proposed solutions or act as a fallback.

Level 3

System in full control in certain situations. Humans might supervise.

Level 4

Full autonomous operation in all situations. Humans may be completely absent.

Level 5

Prerequisite: Automation system monitors the environment.

Occasional autonomy in certain situations. Humans always responsible, specifying intent.

Level 2

—5G use cases

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Motivation and valueThe increasing demand for highly customized products, as well as flexible production lines, can be seen as trigger for the “fourth industrial revolution”, referred to as “Industrie 4.0”. Current systems usually rely on wire-line technologies to connect sensors and actuators. To enable a higher flexibility such as moving robots or drones, these connections need to be replaced by wireless technologies in the future. Furthermore, this facilitates the renewal of brownfield deployments to address Industrie 4.0 requirements.

5G in Industrial Settings

Motivation


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International Telecommunication Union (ITU)

5G Use Case Classifications


Ultra-reliable and low latency communications

– This category is characterized by stringent requirements regarding latency, throughput, and availability

Enhanced mobile broadband

– Use cases covering human-centric application scenarios

Massive machine type communications

– This category is addressing the application of a very large number of connected devices.

– Most realistic start points for advanced sensing in Industry 4.0

5G ITU Use Case Families

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TACNET 4.0*



Mobile robotics

– Cooperative transport of goods

– Platooning

– …

Local and time critical control

– Closed loop motion control

– …

Monitoring

– Additive sensing for process automation

– Predictive maintenance for rotating equipment

– …

5G TACNET Use Case Groups

*http://www.tacnet40.de/

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TACNET 4.0



Remote control

– Remote control for process automation

– Remote live support

– …

Shared infrastructure and intra-/ inter-enterprise communication

– Industrial campus

– …

5G TACNET Use Case Groups

*http://www.tacnet40.de/

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Monitoring

TACNET 4.0 Use Cases


– For production automation, there are a number of operational goals with regard to product quality, production uptime, energy and material use etc. To optimize toward these goals, insight into process and equipment conditions is needed beyond the information provided by sensors deployed for closed loop control.

– By deploying additive sensors for process quantities (temperature, flow, etc.) and equipment conditions (vibrations, leakages, etc.), the sensory resolution in a plant can be significantly increased, including temporary installations to address transient but urgent issues.

– Example: process plants, such as chemical plants

Additive sensing for process automation

Field Level

Plant Level(Edge)

Cloud Level(Remote)

Site Level(On-premise)

Plant backbone

Site backbone

Internet

Peripheral connect-

ivity

On-premise data-center

DCS

Rad

io a

cces

sne

two

rkC

ore

net

wo

rk

observation only

monitoring & optimization

monitoring & optimization

edge gateway

—Conclusions

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Special situation for Artificial Intelligence in process industry:

– Often not quite big data

– Make efficient use of physical modeling

Interesting aspects regarding control system setup and safety: e.g. Namur Open Architecture and ASME PTC 19.3 TW-2016Regulations push technology, in this case.

Strong trend from product to application-business

Demand for consulting and service

Small- + medium-sized enterprises: quest for low investment risk

Conclusions: What was it all about, again?


Interesting driving forces behind non-invasive and low-cost sensing

Non-invasive / low cost sensing generates a large potential for process optimization, trouble-shooting, and more!

Documents

Advanced sensing and its Decisive Role for Digitization · Digitally enhanced systems. Operation and system optimization ... Starting as monitoring / redundancy applications 2. Creation