SMART MANUFACTURING: THE NEXT INDUSTRIAL REVOLUTION Caroline Hargrove & Rob Bowyer Cambridge, 3rd February 2017

PresentedatConsor,umforthe4thRevolu,on|Execu,veBriefingDay(#C4IR)Cambridge,UK2-3February2017|www.cir-strategy.com/events

BACKGROUND

3

The world of manufacturing is evolving: •  Rapid technological development •  Ever increasing computational power have sent

ripples of digital disruption throughout the industry •  Rise of the Industrial Internet of Things (IIoT) is

paving the way for the 4th industrial revolution

BACKGROUND

4

The IIoT combines real and virtual production environments into a single view of the factory Smart manufacturing describes the application of these systems to real production lines

–  using embedded systems, –  communication platforms and –  process intelligence

Decision support tools enable manufacturers to understand the impact of individual line productivity on the overall factory performance

BACKGROUND

5

The motivation to harness the IIoT is usually to achieve improvements across a range of manufacturing KPIs including: •  Increased yield •  Increased throughput •  Increased productivity •  Reduced costs •  Reduced downtime

This can be difficult to achieve due to unique characteristics of each production process

CONDITION INSIGHT

PRODUCTIVITY MIRAGE

7

Typically, manufacturing plants prioritise their efforts on reducing downtime, of which maintenance programmes are a key driver

Condition-based monitoring (CBM) or Pre-emptive maintenance (PEM) are seen as the natural solutions but •  It is difficult to develop robust solutions •  Ignores link to quality - usually separate

functions within the business

Hence the ‘mirage’ effect – one never really gets the expected benefit from CBM/PEM

MAT IIoT FOCUS

8

Our approach is different – rather than starting from •  Condition Monitoring / PEM focus: fault

diagnostics –  Challenging problem; may not be unique solution –  Needs cloud to join sources of data –  Needs ground-truth from NLP platforms etc. –  Long term validation required

… we start with a •  Quality focus: anomaly detection

–  Needs high rate sensing at edge of network –  Appropriate choice of sensors –  Bespoke key features –  Rapid deployment –  Informs CBM

SYMPTOMS & LEAD TIMES

9

Time

Conditions start to change

Vibrations Noise

Heat

Smoke

Emergency Stop

3 month 2 weeks 2 days

10 min

Mac

hine

con

ditio

n Anomaly detection works early on (lead indicator) Traditional CBM/PEM

<10ms

CLOUD vs EDGE of NETWORK

Developing solutions for the edge of the network requires application specific engineering for the end-to-end solution to work including •  Domain knowledge •  Agile feature development •  Secure over-the-air download •  Configuration management

Our integrated data acquisition, workflow & algorithm development environment inspired from F1 racing allows us to do this efficiently

10

GENERIC SYSTEM OVERVIEW

11

Smart Sensor

Analytics Engine

PLC

Analytics Engine Execution of local analytics for: −  Real-time decisions for

machine operation. −  Reducing data volume for

upstream analysis through various strategies.

Gateway

Data storage

ATLAS / MIDAS

Market App

Market App

Data & Analytics Platform

EDGE LAN

Smart Sensor Feature extraction from very high frequency data.

CLOUD

Market Application Dashboards containing performance KPIs, and decision support.

Data Analysis & Model Design Tools Workflow & tools to undertake diagnosis of issues, and to design & improve models for execution line-side.

CASE STUDY: MDI CRIMPING

CRIMP OPERATION - PHYSICAL

13

CRIMP OPERATION SEQUENCE

14

FEATURE EXTRACTION

15

Reasonably consistent force traces: •  Most variation between

heads

•  Stochastic noise evident on ‘shoulder’

FEATURE EXTRACTION

16

Extracting key features from within a force trace: •  Contact force •  Max force •  Number of Stiction/friction

peaks •  Contact force multiplet •  Crimping force multiplet •  Force duration

Use features in thresholding/clustering for identifying anomalies

NUMBER OF OUTLIERS

17

For the contact force 0.0425% of cans considered outliers (in bins with very low numbers of samples <1000) out of 1.34 million cans For 75,000 cans this would be ~30 cans Example: Contact force – force at initial contact of crimp head and can

Cloud platform

HADOOP

Real-time gateway

SYSTEM OVERVIEW - PHYSICAL

18

Analytics Engine PLC

Analytics Engine Feature detection and thresholding

HMI

LINE-SIDE FACTORY CLOUD

Data Store

SPARK

DECISION INSIGHT

DECISION INSIGHT Production is not only about uptime and quality but also operating the facility with competing KPI’s (cost, quality, schedule, shelf-life, inventory) •  Real-time decision support tools help operators

avoid ‘local’ optimisations and miss the bigger picture

•  Best results come from being forewarned => predictive modelling supported by what-if scenario planning

20

DECISION INSIGHT

21

Decision Insight – a framework for prescriptive analytics

DECISION INSIGHT

22

We use simulation to aid understanding in a complex world •  Performance (KPIs,

outcomes) •  Levers (interventions)

& constraints (infeasibility, safe operating area)

•  Decision logic

(robustness frequency)