Award Winning IIoT Plant Asset Management

| IIoT Plant Asset Management |
© Yokogawa Corporation of America
1
Anu Mahesh
Wireless & IIoT Product Marketing Manager
Award Winning
IIoT Plant Asset Management

© Yokogawa Electric Corporation
Hosts and Presenters
Host Presenter
Anu Mahesh
Anu.mahesh@yokogawa.com
Gerald Hardesty
gerald.hardesty@yokogawa.com

5
Agenda
1. IIoT Overview with APM & CBM
2. LoRaWAN
3. Yokogawa IIoT and Sushi
Sensors
 Vibration examples
4. GA10 & Artificial Intelligence (AI)
5. Open Discussion

6
Introduction to IIoT for Asset
Performance Management
and Condition Based
Maintenance

7
Current State of Industrial Landscape

8
Yokogawa’s IoT Reference Model

9
 What is APM?
 APM (Asset Performance Management) is the whole activity of planning,
implementing measures, and conducting assessments for making full use of
the value offered by assets.
 Mutual information sharing between maintenance and operation is required to
obtain the maximum effectiveness in APM.
Achieving APM
Goal To maximize asset availability
Item to
manage
 Uptime
 Longevity
 Maintenance cost
Method CBM(Condition based maintenance)
Required
Data
Asset condition data：Continuously measure the
health condition of asset
Goal To maximize asset utilization
Item to
manage
 On-Time delivery
 Quality/Quantity control
 Reliability
 Production cost
Method Visualization of use and operation status
Required
Data
Process Data：Continuously measure the utilization
and the operation status of asset
Maintenance Operation
Achieving
APM
Information
sharing
Information
Sharing
Information
sharing

10
Customer Concerns
 Facts of equipment maintenance
 Critical machines are monitored by permanent
sensors using continuous vibration monitoring
system.
 Important machines are monitored by handheld
sensors periodically through operator round.
 Almost field inspections have been outsourced.
Too many assets to monitor
Shortage of field patrol
frequency and inspection skills
 Concerns of field patrol
 All equipment cannot be inspected due to
labor shortages
 Operator skills/ know-how varied resulting in
human errors
 Inspection data is not digitized and cannot be
analyzed
 Miss anomalies/ abnormality trends in assets
Failure
Abnormality
Detection

11
 IIoT(Industrial IoT)
 IIoT (Industrial IoT) made it possible to collect large amounts of data
which was not measurable or indirect estimation at a low cost.
 In operator rounds ,organizing/ accumulating data was unrealistic due
to burden on the operator but introducing
IIoT for maintenance activity can reduce it.
 IIoT Solves Challenge
What IIoT Offers
 Improvements in wireless technology enables
many sensors measurement results can be
received remotely.
 Enormous number of
equipment to monitor
Challenge
 Equipment with sensors shares its condition
through the network without operator rounds.
 Lack of operator rounds
 Measurement by sensors eliminates blanks and
variations in measurement results by human eyes.
 Accuracy of measurement result
Solution

12
LoRaWAN Overview

13
What is LoRaWAN?
30ft Range*
1000 ft Range*100ft Range*
* Approximate range in an ideal setting
FSK
5 Km
Range*
Long range and low power
At +14dbm output power, 915MHz
• In Sub-GHz
• 5km dense urban >13km suburban
• Bitrates < 100k bps
Robust communication
• Not susceptible to interference from
Wi-Fi, Bluetooth, GSM, LTE, etc
High accuracy localization
and ranging
• Modulation format permits high
accuracy localization
• Not RSSI based and accounts for
multi-path and fading
• Permits high volume additional
features
Improved network capacity
• Connect more nodes
• Additional capacity
for features
LoRaWAN – Low Power Long Range Wide Area Network Communication
 LoRa is a radio modulation name for long-range, low-power, low-data-rate
applications
 LoRaAlliance develops the specification and certifies devices.
 The latest version of the specification is V1.1 as of February 2019.
 Long-distance communication of up to around 10 km.
 Wide area connectivity characteristics in the unlicensed Sub-Ghz band
(915MHz)
 Allows customer to create private networks/ use public networks
 Telecommunication companies deploying LoRaWAN networks Senet, The Things
Network, Comcast
 Long battery life: 1 – 10 years

14
Low Power Wide Area (LPWA)
 LPWA is a type of wireless communication
providing coverage that cannot be met with
Bluetooth and other short-range wireless
communication (coverage up to dozens of meters).

15
LoRaWAN Communication Protection
 Device address (DevAddr)
 32-bit ID that is unique within the network.
 Shared between the end device, network server, and application server.
 Network session key (NwkSKey)
 128-bit AES key that is unique to each end device.
 Shared between an end device and network server.
 Used to ensure data integrity and protects the communication between an end
device and network server.
 Application Session Key (AppSKey)
 128-bit AES key that is unique to each end device.
 Shared between an end device and application server.
 Used for the encryption and decryption of application messages and protects the

16
Why Yokogawa Chooses LoRaWAN
 Sensor can be installed even in a pipe jungle  Radio reachability
 Both cloud and on-premises system are supported  System flexibility
 Several years operation without a battery change  Low power
 A large number of sensor must be handled  System scalability
 Initial & operation cost must be affordable Low cost
 Worldwide standard & proven technology  Market adoption
[*] NB-IoT, LTE-M

17
Yokogawa IIoT Concept &
Sushi Sensors

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“Sushi Sensor” concept is “Sushi” itself
Why “Sushi”?
 Easy & Simple
 Installation：M6 screws and magnet mounting, LoRaWAN
 Configuration via smartphone with Near Field Communications
 Plug & Play data collection & monitoring
 Professional
 Environment resistance：IP66/67 and explosion-proof
 Long-life and replaceable battery
 3-axis vibration sensor
 Variation
 Expand sensor lineup for asset health monitoring

19
Sushi Sensor
IIoT wireless sensor for field use
Monitor asset condition via wireless, digitize operator rounds
Sushi Sensor
XS770A
A.I.
Long distance
Low power
LoRaWAN
Gateway
Robustness
for field use
Smart Phone
Ease of start-up
and data monitoring
Digital
trend
Cloud or
On-premises
Height: 3.96 in
Diameter：1.88 in
Weight: 9.2 oz
 1st Vibration and surface temperature
 Monitor equipment across plant
 Approx. 100 ~ 200 sensors per gateway
 Transmission distance： approx. 3,300 ft
(1000m)
 Specs
 4-Year battery life @ 1hour interval
 Frequency range: 10Hz ~ 1kHz (Peak, RMS)
 Temperature range: -4 to 185 °F(-20 to
85 °C)
 Fastest update interval: 1 minute
 On Premise/ Cloud-based Solution
 Configuration, data logging
 Apply AI to enable Condition Based
Monitoring
 Use
 Monitor equipment condition by vibration and
 surface temperature trend data

20
Sushi Wireless Pressure Sensor
 XS530 Pressure Measurement Module
 Gauge Pressure Sensor
 Measured fluid: gases, liquids
 Used with XS110A Communication Module
 Specifications
 Measurement range: -0.1 to 5 MPa (-14.5 to 720 psi)
 Process Temperature Limits: -40 to 120ºC (–40 to 248°F)
 Update time 1 minute to 3 days
 Battery
 Battery life: 10 years (update time: 1 hour*2)
 Can be replaced by removing only the wireless
communication module
 No dismounting the measurement module from the piping.
 Applications
 Online pressure monitoring of gauges
 Leakage monitoring of valves
 Clogging monitoring of piping and filters
XS530 Pressure
Measurement
Module
XS530 + XS110A
Pressure
Measurement
Module

21
Sushi Wireless Temperature Sensor
 XS550 Temperature Measurement Module
 XS550 Temperature Measurement Module
 Supports 2 inputs of IEC standard thermocouples
 Used with XS110A Communication Module
 Specifications
 Measurement data Temperature, 2 points (non-insulated)
 Sensor type: Thermocouple of types B, E, J, K, N, R, S, T,
and C
 Battery
 Battery life: 10 years (update time: 1 hour*2)
 Can be replaced by removing only the wireless
communication module
 No dismounting the measurement module from the piping.
 Applications
 Identifying the failed stage in multistage heat exchangers
 Monitoring energy loss due to steam leakage
 Temperature monitoring of tanks and firebricks
XS550
Temperature
Measurement
Module
XS550 + XS110A
Temperature Measurement
Module

22
Customer Concerns
 Equipment Maintenance Facts
 Critical machines are monitored by permanent sensors using
continuous vibration monitoring systems.
 Important machines are monitored by handheld sensors periodically
via operator inspections.
Every 8 hours
- 1 month
inspection

23
APM Solution: Basic System Architecture
Ethernet
LoRaWAN
Gateway
NFC
（Near Field Communication）
Communication
Sensing
Network Platform
Data Processing/Storing
Smartphone
Configuration
Application
Software
Data Monitoring
AI *Future Plan

24
LoRaWAN MultiTech Gateway
 MultiTech gateway: Model MTCDTIP-L4N1-266A-915
 USB Port with Type A Receptacle
 Micro-SIM Holder (Verizon and AT&T)
 LoRa Specifications: LoRaWAN 1.0.2 standard
 Radio Frequency: 915 MHz ISM band for US & Canada
 Power: Power over Ethernet 37-57 Volts DC.
 Environment
 Operating Temperature: -40° C to +70° C
 Chassis: IP67 rated
 Radio compliance: US & Canada
 Warranty: 2 years
 Up to 200 sensors per gateway

25
On-Premise Solution with GA10 (R3.07)
New Functions: Sushi Sensor connection and AI function
 Simple connection of GA10 and Sushi Sensor
 Automatic Sushi sensor recognition and registration of tag
and group
 AI function implemented (Anomaly detection)
 AI learning and anomaly detection with one-click
！
Ethernet connection
Notify
anomaly
AI
analysis
Simple installation of AI facility management as
on-premises system
Gateway

26
Sensor Diagnostics
Determine failure type, parts, cause and extent of a
problem.
・Diagnose by equipment experts such as
device vendors and vibration analysts.
・Vibration waveform analysis.
 Purpose
 Method
Waveform FFT analysis
Detailed DiagnosisSimple Diagnosis
(Sushi Sensor Coverage)
Determine whether there is problem or not in
the target equipment.
 Purpose
 Method
・Operator rounds with field operators.
・Continuous monitoring of the target equipment.
Thresholds
Trend monitoringVelocity
Trend Monitoring
Time
To Detect the
Cause

27
Bearing
 Velocity Mode and Acceleration Mode
 In a rotating piece of equipment, locations of failure are:
Rotating axis and Joint
Bearing
Vibration measurement and anomaly detection methods
Axis & Joint
Velocity mode
 Velocity Mode
 Acceleration Mode
Acceleration mode

28
Actual Vibration
(amplitude)
Axis of vibration component
Axis, Joint
 Why velocity and acceleration measurement are
effective for detecting axis and bearing failures?
 Actual vibration signals are contained in axis and bearings.
 Bearing vibration amplitude is very small and it is hidden by axis
vibration. A method to magnify it is acceleration measurement.
The vibration
component of the
bearing is dominant
The vibration
component of the
axis is dominant
Amplitude (mm)
Rate (mm/sec)
Acceleration (mm/sec2)
µm unitmm unit

29
Frequency [Hz]
10 100 1K 10K 100K
Sense of touch range
Sense of ears range
Unbalance
Misalignment
Bent axis
Oil whirl whip
Gear failure
Bearing failure (initial stage)
(late stage)
 Detection methods and measurement purposes
Velocity
Acceleration
Displacement

30
Customer value - Maximize asset utilization
Help customer move to Condition Based Monitoring Planning to provide equipment failure prediction with Sushi Sensor
and AI.
 Enable customer to shift from TBM to CBM.
Operating Time
FailureRate
Periodic
Replacement
(TBM)
Ideal time to
replace
(CBM)
Equipment
failure
Initial failure
Aging
Failure
You can use equipment
for this duration
Bathtub curve
1st year 2nd year 3rd year 4th year 5th year 6th year 7th year 8th year 9th year 10th year
SDM SDM
Shift to Condition Based Monitoring

31
Sushi Sensor Target
Major Installation Equipment in Japan *As of the End of 2018

32
Anomaly Detection using GA10 & AI

33
Data Utilization: Future
 In near future, Automatic anomaly detection by
machine learning (AI)
 Enabling automation of judgments on huge amounts of
data.
 Capable of finding irregularity which people can not
easily find.
Cloud
AI Algorithms

34
Anomaly Detection using GA10
Anomaly
detected
Anomaly detection score
(Health Score*)
Smaller HealthScore value means
larger anomaly.
0 or more：Normal
Less than 0：Anomaly
*Math option (/MT) is necessary to
display HealthScore
Measurement data Anomaly detection score

35
Anomaly Detection (Application example)
Application: Detect sign of equipment abnormalities
Target device: Rotating machines, Agitators, Compressor, etc
Measurement data: Vibration and surface temperature of moving parts
Sensor: Sushi Sensor*
*other sensors’ data like ISA100 vibration sensor or thermal camera also could be used.
Vibration
acceleration
Surface temp.
Vibration speed
Normal
Abnormal
Inspection and
maintenance before
failure become possible.
Learning
period
Anomaly
happens frequently
Failure
Anomaly alarm
happens frequently
Large anomaly is increasing
Anomaly
detection line
Much larger
Anomaly
(Health Score)

36
Data Utilization (digitalization)
 Sushi Sensor and machine learning
Source:
Yokogawa Technical Report English
edition Vol.61 No.1 (2018) P.26, 27
Deviation data
assuming failure

37
GA10 Demo

38
Usage:
Visualize utility
data of facility
Device:
Power monitor, DTSX
Wireless etc.
Usage:
Report for product
evaluation and test
Device:
Data logger, WT etc.
GA10 New Application
Test &
Evaluation
Operation＆
Monitoring
Data
Visualization
Facility
maintenance
Usage:
Operation and monitoring of
manufacturing line
Device:
Recorder, PLC
Temperature controller etc.
Usage:
Maintenance of
manufacturing facility
Device：
Sushi Sensor
Data logger
Wireless device etc.
NEW !
Detect sign of failure
Find abnormal from huge dataSupport quality inspection
Support manufacturing line monitoring

Copyright © Yokogawa Corporation of America
39
Lifecycle Management Services
Design Implement Integrate Maintain
• Conduct site surveys
• Design mobile
workforce and
industrial networks for
full site coverage
• Determine optimal
location for access
points on site
• Spectrum analysis to
determine any
interference
• Factory provisioning of
all wireless
instrumentation
• Onsite commissioning
• Integrate the wireless
network to the DCS /
PRM or other upper
level system
• Periodic maintenance
to ensure optimal
network performance
• Monitor system health
• Address any Add /
Move / Changes on site

40Co-innovating tomorrow is a registered trademark of Yokogawa Electric Corporation.
Thank you very much for joining us
today!

Award Winning IIoT Plant Asset Management

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