collaboration to realise iot solutions...2019/05/02 · success through collaboration delivering...

Success through collaboration

Delivering value by combining knowledge and technology that

we wouldn’t be able to achieve on our own.

Collaboration to realise IoT Solutions

PC-Control: Unified Universal Automation Platform

Runtime

Engineering Tools

Communi-

cation

Storage

HMI IEC

Safety

Motion

Library

PLC Open

Motion

Robotics

Motion

C, C++CCAT

CCATModules

Simulink®

Module

MATLAB®

Simulink®

Scientific

Automation

Measure-

ment &

Analysis

Vision

Runtime

Vision

Beckhoff Automation IO System

▪ Increase robustness and reliability

− Protect machinery as well as staff on-site

▪ Save the environment

− Don‘t replace intact parts

▪ Save your money

− Planned and fewer downtimes

Introduction to 4.0 IIOT

l costs

minimum

total costs

preventive reactivepredictive

▪ Time domain

− Root-mean-square (RMS)

− Excess Kurtosis

− Crest-Factor

− Signal envelope

− Analytic signal

▪ Frequency domain

− Magnitude- and Power Spectrum

− Envelope Spectrum

− Integrated- and Multi(frequency)band RMS

▪ Time-Frequency domain

− Instantaneous frequency/phase

▪ Quefrency domain

− Power Cepstrum

Condition Monitoring Toolbox | TwinCAT 3 Library

Time domain signal features

Frequency domain signal features

Time-Frequency domain signal features

Quefrencydomain signal features

Statistical evaluation

- Quantiles

- Central moments

Classification

110/240 VAC

4G data

EtherCAT P

EtherCAT and power in a single cable

IEPE Vibration sensorThermocouple

IP67 signal conditioning for temp and vibration sensors

IoT Vibration and Temp Monitoring

Controller and Gateway

▪ Frequencies to look at when aiming bearing diagnostics

− Ballpass frequency, outer race

− Ballpass frequency, inner race

− Ball roller spin frequency

− Fundamental train frequency

• (+) fixed inner ring

• (-) fixed outer ring

Shaft speed [Hz]

Number of roling elements

angle of load

▪ Time domain

− Excess kurtosis

− Crest-Factor

bearing

Defect on outer

bearing surface

Defect on inner

bearing surface Defect on

rolling element

Time domain signal features

Frequency domain signal features

Time-Frequency domain signal features

Quefrencydomain signal features

Statistical evaluation

- Quantiles

- Central moments

Classification

▪ Frequency domain

− Envelope Spectrum

Knowledge on geometry

of bearing and rotating

→ frequencies to observe

are known

frequency

(FFT of a signal‘s envelope)

1) Measure and visualise raw data (e.g. scope view)

2) Calculate signal features (CM Lib. | signal processing

and statistics)

3) Mapping to a maintenance state (CM Lib. | classif.)

4) ISO 10816-3:2009

Application example

Traffic light

classification

(thresholds)

• There are lots of drilled holes on a plane (Boeing 777 wing -> 35 000

fasteners)

• Current process monitoring is to prove the process on coupons then

manual inspection

• Drilling tools are improving, monitoring systems are appearing but

often proprietary so no real opportunity to use the data

• Always looking for ‘one way assembly’

POC Use case Aerospace wing drilling MTC

Drilling Process Monitoring

Features of Interest

1. Part Thickness

The thickness of composite panels is quite variable and form tolerances mean that

shim is added to fill the gap between panels on assembly. As a consequence the

thickness of the drilled part may vary. The current process is that the depth of each

drilled hole is checked and a fastener selected long enough to fasten the parts but

not too long as to add extra weight to the wing.

2. Burr Height

Typically a burr forms on the exit side of aluminium components on drilling whose

height is quite variable. Currently the wing panel must be dismantled to inspect each

hole and deburr if necessary because there is no way of knowing the burr height in

advance. We believe that the burr formation may again be detected from the spindle

torque/power curve and alert the operator when a burr forms.

3. Tool Wear

In general we think we will see an increase in spindle power and defects as the tool

wears and becomes blunt. Currently the tool is replaced either after a set number of

holes or when the operator notices that the tool is worn. We would like to generate

an automatic alert when the tool has worn and possibly estimate the number of holes

remaining in a particular tool.

4. Good/Bad Hole

More generally we would like to explore the possibility of using machine learning to

flag bad holes to the operator so that they do not have to inspect each hole but are

directed to the holes in need of attention.

POC Use case Aerospace wing drilling MTC

Data Capture

Analogue output +/- 10V

• Spindle power (8ms)

• ~1200 data points per hole

Beckhoff IPC IOT Gateway

OPC UA

• Timestamp (8ms)

• Filename

• Timestamp (8ms)

• Filename

Spreadsheet

• Hole position

• Part thickness

• Hole quality

• Gap

• Exit Burr

Manual Measurements

Data Capture – IoT Gateway

Remotely

accessed IoT

Gateway

ENABLING GLOBAL IOT CONNECTIVITY

Secure Private Networks using Cellular Networks

SECURITY

• The Arkessa Core Network creates a SECURE PRIVATE NETWORK between device and

cloud.

• Redundancy in the Arkessa Core Network ensures HIGH-AVAILABILITY.

• Public Internet access is RESTRICTED unless required by the application.

Enterprise Cloud

Public Internet

SECURE PRIVATE NETWORK

SECURE PRIVATE NETWORKING

OVER CELLULAR NETWORKS

GGSNSGSN RADIUS DATA

ARKESSA NETWORK

Arkessa AAA Services for fixed IP

allocation and 2nd level authentication

ENTERPRISE NETWORK

ResilientEncrypted

Internet

Network to Network

Interconnect

Private APN

Radius

SIM APN

GEA/UEA/UIA2 GTP VRF

VRF or Access Control List

IPSec / VLAN

Private IP Address LOCAL AND ROAMING

MOBILE NETWORKS(~600 networks, ~200 countries)

MASSIVE BROADBAND AUTOMATION & SAFETY

Metering Smart Cities

Agriculture &

Environment

Wearables &

Healthcare

Transportation

Digital

Surveillance

Drones

Retail &

Hospitality

Robotics &

Machine Vision

Smart GridAV & EV

Process and Factory

Automation

Low-cost & Low-Power

Low Data volumes

Massive deployment density

High Data volumes

Data streaming

Real-time

Ultra-reliability and low-latency

Wireless Time Sensitive Networking

Positioning

ONE NETWORK FOR ALL INDUSTRIES

< Commercial deployment and scaling Early pilots >

CELLULAR

4G LTE

LTE Cat-NB1

LTE Cat-M1

INTRODUCING INVMA AND ASSETMINDER

Rob Dinsmore – Sales Manager

April 2019

Need resized image

Fundamentally the PHYSICAL and DIGITAL worlds have converged

ASSETMINDER

Receive instructions

and updates from you

• Firmware updates

• Control services remedy

Tell you when the asset isn’t

being used properly• Warranty claims

• Health and safety

Tell you when there

maybe a failure• Predict failure

• Reduce risk of further damage

Tell you when they need servicing

• Revenue

• Minimise downtime

Tell you their location

• Theft

• Violations

• Audit

To provide adequate service to your customers or to make sure your assets are working for

you, they need to be able to:

ASSETMINDER

• Telematics monitoring the location and status of vehicles, equipment and people is common

• Using data from assets/products in the field to diagnose problems remotely is a reality

• Companies are getting value from this data and are changing the way they work.

• BUT with many solutions:-

Narrow

Closed

They answer one question such as where your asset is or what it is doing. They work well with one type of asset such as a vehicle, a generator or a building but never all of your assets. They might just work with one manufacturer.

You can’t easily connect them to your current processes through your existing Help Desk, Service Management, Product Lifecycle Management, Customer Relationship Management, Enterprise Resource Planning, or other systems.

They are built to solve one problem and can’t grow with your needs and ideas for business improvement. Small changes require customisation rather than configuration.

The hardware and software used is relatively expensive and a one-size fits all with a reliance on one technology to satisfy all of your needs.

Traditional System Landscape

Logistics SuppliersExternal

Production Quality Inventory

Business Systems

(ERP, CAD, PLM)

Operational Control

(MES, MOM)

Process Monitoring

(HMI, SCADA, Test Stands)

Control Level & Data Acquisition

(PLC, CNC, Autoclave)

An Industrial Innovation Platform

complements and brings

• Unified visibility across systems, assets, devices and people

• Wrap and extend existing systems

• Actionable and comprehensive operational intelligence

• Real-time, social, mobile

• Rapid application developmentMainten. Energy

USE NEW TECHNOLOGY TO WRAP AND EXTEND

ASSETMINDER

The Broad, Open and Flexible Solution

• Set up multiple Geo-fence Triggers• Locate your asset and its velocity• Use the location to trigger process

• Set your parameters• Configure your dashboard• Configure rules on asset and

system

• Alarms and configurable workflow• Analytics and Machine Learning• Email, text, vmail, tweet

• Job assignment• Time to next service counters• Knowledge base and service blog• Auditable compliance trail

Built on the secure, robust and flexible ThingWorx Platform from PTC

• Remote access and triggers• Software updates• Remote diagnosis

LOCATE

MONITORCONTROL

SERVICE INFORM

INTEGRATE

CAFM, ERP, CRM, PLM,

CAD, SLM, CPM, BI

ASSETMINDER

The Broad, Open and Flexible Solution

ASSETMINDER

Deliver Value and Scalability With Low Risk

Project Hurdles

Integration

Business Logic

User Interface

Database

Security

Protocol Translation

Hardware

Process Change

People Change

InVMA & AssetMinder

Out of the Box

Drag and drop

Pre-built “Mash-Ups”

Out of the Box

Approved Suppliers

Design Method

Change Method

Outcome

Faster

Lower Cost

Lower Risk

More Robust

Thank you

Start trace

2mm above

surface

Stop trace at

end of

countersink

Drill tip cutting

composite

Drill tip cutting

aluminium

Drill tip in air Start forming

countersink

Drilling Process

Part thickness

Burr height

Tool wear

Good / bad hole

Setting up ThingWorx

ThingTemplate

ThingShape

Property definition

• ‘payload’ property used to listen for the MQTT payload

• On change, code snippet is run (next slide)

• ‘spindlePower’ is persisted (saved to db) as the result of the code snippet.

• Defining this property declares it as a property to be stored in db

• MQTT connection from Template

• Data management functionality (parsing and posting) comes from Shape

• Properties also from Shape

• Very easy to hook up/initialize new drill Things, may just need to change MQTT topic

...this part is TBC

There is still the question of how to associate the post-processing measurements

Analysis

During Processing

Start trace

2mm above

surface

Stop trace at

end of

countersink

Drill tip cutting

composite

Drill tip cutting

aluminium

countersink

Analysis

Post Processing

• After drilling we need to check the quality of the hole, therefore we take post-drill-time measurements

• Measurements taken manually…

• This is important so that we have “ground truth” data to use for Machine Learning

Analysis

Start trace

2mm above

surface

Stop trace at

end of

countersink

Drill tip cutting

composite

Drill tip cutting

aluminium

countersinkAnalysis Approach:

• Extract features from torque

• Attempt:

• pattern based recognition

approaches to predict key

measurements

• Simpler “deterministic”

measurements (e.g. time

where torque is below ‘x’

for gap test and

thicknesses)

• Will include ‘environment’

features, such as drill bit age

• Finally predict: Part thickness,

burr heights, gaps + thickness,

countersink height

Thank you

1. Identify business problem that causes pain. (unreliability, poor efficiency, variable quality)

2. Identify simple project to prove value that can be scaled if successful

3. Select sensors and interfaces required to collect data

4. Select method of communication to cloud (wired network, 4G…)

5. Choose application software that provides value

6. Choose cloud provider that will host application software

Conclusion

collaboration to realise iot solutions...2019/05/02 · success through collaboration delivering...

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