the road ahead of iot

HOWARD CHENG

VICE PRESIDENT ASIA PACIFIC

ANALOG DEVICES

The Road Ahead of IoT

– ADI Perspective

50 Years of Leading Measurement and

Interpretation

Transforming industries with our

customers

► www.analog.com/IoT

http://www.analog.com/IoT

Ahead of What’s Possible®: The Foundation of Success in IoT

©2016 Analog Devices, Inc. All rights reserved.

Partnering with Customers

Going Beyond Silicon

Innovating with ImpactWe are solving the toughest problems in IoT

not only with our expertise and leadership in

real world signal processing but also by

working closely with customers and

leveraging algorithms, platforms, software

and cloud computing.

3

ADI Well-Positioned for Value Creation

Sensors,

Signal

Processing,

Power, MCU,

Connectivity

Algorithms,

Domain

Knowledge

Data History,

Data Analytics

Predictive

Analytics

Transformations Interpretations Connections Pattern

Recognition

Decisions

IOT

CLOUD

Gateway

DATA INFORMATION

KNOWLEDGE WISDOM

CONNECTMEASURE INTERPRETSENSE ANALYZEAnalog Devices Highly Confidential Information.


The Connected World – Opportunities for IoT

Analog Devices Highly Confidential Information.

©2016 Analog Devices, Inc. All rights reserved.3

ADI Shaping the Future of IoT

Smart Factory Smart Consumer

Smart Healthcare Smart City/Agriculture

Cloud Network

Security Analytics

Technologies Markets



Information Journey – Analytics

Data

Ingestion

Real-Time

Streaming

Analytics

Historical

AnalyticsActions

“Connected”

“Aware”

“Reactive”

Predictive

Analytics

Prescriptive

Analytics

“Predictive”

“Proactive”

Incre

asin

g B

usin

ess Im

pa

ct



The Answer - Hardware as the Root of Trust

From the Sensors to the CloudLOWEST

SE

CU

RE

FR

OM

SE

NS

OR

TO

CL

OU

D

HIGHEST

SENSORS

CONVERTOR PROCESSINGANALOG DIGITAL

POINT OF HIGHEST SECURITY

PROCESSING

END POINT

REQUIRES IDENTITY AND INTEGRITY

CLOUD

ANALOG

GATEWAYS

SERVERS

SERVERS

NODES

GATEWAYS

NODES

SENSOR

FOG

EDGE

DATASMART

SENSOR

CLUSTERSMART

SENSOR

CLUSTER

CLOUD

Re-enforcing Intelligent Loops in Industrial Automation

Howard Cheng

© 1995 - 2016 Analog Devices, Inc. All Rights Reserved

Example of IoT Application

Smart Machine

Maintenance is just the “tip of the iceberg”


Motors consume 45% of all global electricity

From a segment perspective

Industrial : 78% of all electrical energy used in industrial systems

Building Technology : 43% of all electrical energy used in commercial buildings and 37% in the home

A predictive maintenance system can be shown to reduce energy consumption by up to 14%

(US Department of Energy Office of Industrial Technologies)

Smart Factory – Example of ADI Manufacturing Plant

► Overview

8” CMOS, IPD, PROBE Cleanroom

Heavy Cleanroom Plant (air filtering systems, power units etc)

Precision Equipment (used to process the wafers)

>50 Process Tool Types

>300 Individual Tools of Every Vintage

>20 Years Experience

Well Developed Process, Equipment & Plant Control Structure

► Objectives

To move from a traditional approach of Preventative & Reactive Mix

To add Prediction to Maintenance Methodology

To Reduce Scrap

To Maintain & Improve Equipment Performance

To use as an IoT Test bed to trial ADI sensors and technology

11



Monitoring Plant Equipment

Imbalance

Turning moment

monitoring

Field ErrorAlignment ErrorComponent Resonance

Pump / Fan Gear Box Drive

Bearing ErrorGear Error

Signature Analysis Indicative of impending Fault based on Vibration Analysis

Warning based on

Coupling

Misalignment

Diagnosis

Beginning of

Roller Bearing Damage



The Need for Electrical Signature Analysis (ESA) in Smart Machines

13

Bearings41%

Stator37%

Rotor10%

Other12%

Problems in 3-Phase Induction Motors

~47% of faults

are electrical

Source: Predictive Maintenance by Electrical Signature Analysis to Induction Motors, Bonaldi, et. al

Fault Types Analysis Methods

Opportunity for edge node processing

and cloud analytic differentiation and IP

generation

Electrical Signature Analysis (ESA) Methods

► Power Quality

► MCSA (Motor Current Signature Analysis)

► EPVA (Extended Parks Vector Approach)

► IPSA (Instantaneous Power Sig Analysis)

► Partial Discharge

More Modalities = More Context

14

Improper Contact

Pressure

Thermography

Acoustic

Summary of Machine Health Data Processing Modalities

Sensor Performance

Pro

cessin

g C

om

ple

xity

CONDITION MONITORING• Thresholds set by ISO-10816

DIAGNOSTIC• Full data analysis, model development

• Can be done now with handheld instruments

• Pin point fault detail, limited RUL prediction

PROGNOSTIC• Sophisticated processes, algorithms & know-how

• Can be implemented from a single vibration sensor

• Requires development of Machine models

As much sensor data as possible, Historian (EAM), Maintenance records

High quality XL data

Applied Domain Expertise

Machine size, RPM

High quality XL data

Machine Learning / AI• S/W installed on the plant server or cloud

• Resolution improves with # of measurands

• Questionable link to quality of sensors

Plant Server /Historian

Local

processing

‘Cloud’

processing

15

IoT Evolving to More Intelligence at the Node

MEASURE

INTERPRET

SENSE

CONNECT ANALYZE &PREDICT

INTERPRETSENSE CONNECT MEASURE

ANALYZE

• Intelligent “Smart” Sensing: node turns data into informationo Lowers overall power consumption, lowers

latency, reduces bandwidth waste

o Enables move from reactive IoT predictive & real-time IoT

o Important in Smart Healthcare, Smart City, and Smart Factory

• Data stays data: never generate wisdom and knowledge at the nodeo Power hungry and bandwidth

intensive to convert and send all data

TODAY

TOMORROW



Thank You!

50 Years of Leading Measurement and

Interpretation

Transforming industries with our

customers

the road ahead of iot

Small Business & Entrepreneurship