asset performance management predictive analytics ......asset value chain operations value chain...

October 2018

Jim ChappellVice President – Information Solutions

Asset Performance Management

Predictive Analytics / Artificial Intelligence

© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.

ASSET

Value chain

OPERATIONS Value chain

Complete digital definition across all elements of design, engineering, construction and production

Complete digital definition and orchestration of all elements across

operations & maintenance

ASSET

PERFORMANCE

MONITOR

AND CONTROL

ENGINEER

PROCURE

CONSTRUCT

OPERATE

AND OPTIMISE

PLAN AND

SCHEDULE

ASSET

PERFORMANCE


Assets are the Heart of the Business

• Insight into risks - Balance between financial

operational and safety risks

• Ever-changing legislation - Complying with

environmental and social requirements

• Sharing knowledge - Implement best practices

globally

• Continuous improvement – to drive maximum

return on industrial assets

RISK-BASED OVER-MAINTAINED

Goal

Cost of non-availability (production loss + break down)

Preventive maintenance cost

Total cost

Comfort mode

UNDER-MAINTAINED

Fire fight mode

Optimum Too muchNot enough effort


Asset Digital Lifecycle

Simulation

Design

Construct

Requirements

Commission

Preventive, Condition & Predictive Maintenance

Control of Work

Overall Asset Health & Risk

Operational Records & Shift Handover

Digital Maintenance Plan (Costs, History…)

A trusted digital asset in contextFor every physical asset

Common Asset Repository

Common Asset View


Connect ActAnalyzeCollect

Real-time Integration, IOT

Operations Data

Engineering Data

Inspection Data

Predictive Asset AnalyticsBusiness Intelligence

EAM, CM, Control of Work

Advance Visualization AR/VR

Workflow, ProceduresRisk & ReliabilityTools




Supervisory Control, IOT, etc.

Maintenance

Analytics


Information

Industrial Big Data


Hybrid

…

SECURE

Industrial Big Data




Maintenance

Analytics


Information

Industrial Big Data


Information

Cloud

On Premise

Hybrid

Information

Historical Real-time Engineering


Information


Alarms & Events

Information


Condition-based Alerts

Information

Trigger Work Orders


Information

Auto-generatedCustom Graphics


Information




Maintenance

Analytics


Information

Industrial Big Data


• Gain insights

• Help in decision-making

• Be alerted of possible issues

• Quickly pinpoint root cause

• Recommend actions

• Improve operations & maint.

Condition-

based

Performance

Business

Intelligence

Machine

Learning

Predictive

Anomaly

Detection

Prescriptive

Prognostics Operational

“News”

Analytics


• Real-time condition-based management

• Early warning detection of failures

• Connect control, safety, & maintenance environments

• Trigger work orders

• Reduce downtime

• Advanced calculations

Avantis.PRO

SAP

Maximo

Other EAMs

Condition-based

Analytics


Multi-Dimensional Analysis

Costs, etc…

Batch

Energy

Usage

Production

Data

Time/ Shifts

Equip states

Business Intelligence

Analytics

HQ

• Cloud • SaaS • Insight


Machine Learning

Analytics


• Anomaly detection

• Flatlined data

• Changes in value, cycle times, behavior…

• Interrelated tags (e.g., temp, press, flow)

• Integration with alerts, team collaboration…

• Zero configuration

• Significance score

• Variable threshold based on news volume

• Weighted based on user interests

Newsfeed

Machine Learning


Machine Learning

Analytics


EARLY WARNING DETECTIONDeviations from normal operation

identified and displayed3

Predictive

HISTORICAL DATAApplication learns normal

operation from historical data1

PATTERN RECOGNITIONAdvanced algorithms automatically create

and organize operational profiles2

• Automated model building

• Automated retraining

• Automated data cleansing

• Cloud

• MaaS

• SaaS


Machine Learning

Analytics

Predictive

Prescriptive


Prescriptive

• Signal impact

• Pinpoint problem

• Root cause analysis

• Upstream/downstream

line impacts

• Case management

Likely Fault Condition Probability of Fault Match

Asset Health Trend

Signal Contribution to

Change in Health Status


Machine Learning

Analytics

Predictive

Prescriptive

Prognostics


• How fast will the situation

worsen?

• What-if analysis

• Can you make it to the next

planned maintenance outage?

ACTUAL data

stops here

PREDICTED future values;

there is no actual value at

these times

Prognostics

➢ Neural Net ➢ Deep Learning ➢ Easy to Use




Maintenance

Analytics


Information

Industrial Big Data


Information & Analytics for Improved

Operations & Maintenance

Maintenance


Maintenance

2015 2016 2017

AVEVA EAM Insight

(Incremental thru 2019)

AVEVA EAM

Modernization

(Quarterly Deliveries)

EAM Extensions

Mobile Maintenance, Mobile

Inventory, Data Pilot

Condition Management

Cloud enablement

Maintenance

Analytics &

Dashboards

Cloud

Maintenance MRO Inventory Procurement

& Invoicing

Application Modernization

Mobile

2018 . . .

Approvals CoW, Engage, AR/VR

EAM Product Enhancements

Integrations and Enhancements


Mobile Workforce (IntelaTrac)

Maintenance

Enterprise

2017 202020192018

Thin Client

Cloud

Multi-platform


Putting it all together

AVEVA Insight

Cloud Mobile

IIoT


Cloud

On Premise

Hybrid

Simple – Intuitive – Frictionless

AVEVA Insight


Asset / Entity

Raw or

production

material

Prognostics

Calculations

Alarms &

events

Condition

triggers

Video

Prescriptive

actionsAsset

health

Performance

OEE

HMI/

Process

Graphics

Sensor

data

Location

Predictive

alerts

Common IntelligentModel


Integrated Digital Asset (Cloud)


Technician makes repairs and closes out Work Order > Case Management

System recommends procedure to rectify, providing step-by-step

video

> Prescriptive

> Mobile Workforce

System informs technician the pump is likely to fail within 7 days.

Emergency Work Order issued.> Prognostics

Technician finds oil reservoir filled with half water / half oil.

Determines valving supplying too much pressure to the seals,

resulting in water flowing to the bearings.

Real-world >> Valving Issue

System triggers Work Request> Condition-based

> EAM

Technician receives alert on mobile device > Mobile

System determines bearing temp too high for conditions > Predictive

Insight collects bearing sensor data in the cloud > Big data

> Troubleshoot


Unique Offers

Common

Features

The Familiarity and Benefits of Each Application

Award-winning Customer First Service

Component of the Largest Industrial Software Portfolio

On-premises

On-premise

Full-featured

Perpetual or

Subscription

Standalone

Cloud (CEP)

Native cloud

Full-featured

SaaS Model

Single Tenant

AVEVA

Insight

Native cloud

Easy to Consume Apps

SaaS Model

Multi-tenant

APM Suite of Products



Advanced Analytics Deep-dive

Advanced Analytics

What’s Happening

Real-Time

• Processing of real-time operational data

• Rule based inference for causal analysis

Real-Time Domain

What Happened

Historical

• Assessment and exploration of historical operational data

• Trends, KPIs, Dashboards to present abstracted views

Historical Domain

What If

Predictive

• Comprehensive model based assessment of operational data ranges to determine potential outcomes.

• Deterministic or non-deterministic models

• Open-loop simulations

Science DomainWhat to do

Prescriptive

• Systems that synthesize, predict and provide scenario-based guidance

• Fault diagnostics and knowledge capture

• Sensor contribution & root-cause analysis

Maintenance Action Domain

How bad will it get

Prognostics

• Forecast future state of assets and sensor values

• Determine if you can make it to the next planned maintenance outage

• Provide key input for risk assessment

Artificial Intelligence Domain



▲ Software based modeling of equipment using

advanced pattern recognition

▲ Uses historical data to describe how a piece of

equipment normally operates and build a model

▲ Continuously monitors behavior in real-time

▲ Alerts when the operation differs from the

historical norm

▲ Early warning detection of equipment problems

▲ Advanced analysis capabilities including problem

identification and root cause analysis

Predictive Asset Analytics Software


Monitoring without advanced analytics

Plot-0

4/6/2008 4:39:21.791 PM 4/9/2008 4:39:21.791 PM3.00 days

60

62

64

66

68

70

72

74

76

58

78

96

108

86

102

107

113

94

102

46

64

0

14

110

170

52

70

25

75

Subtle Changes

from normal asset signature

Actual Value

Predicted ValueOu

tbo

ard

Be

arin

g T

em

p (

°F)

Date and Time


A Smaller Box

▲ Group data into smaller modes of operation

Ou

tbo

ard

Be

arin

g T

em

p (

°F)

Oil Drain Temp (°F)

Traditional Alarms

PRiSM


Case Management

Core Functions of Avantis PRiSM

Early Warning Event

Detection & Management

(Alarm Manager)

Trend Analysis

Diagnostics Advisor

(Fault Diagnostics)

Reporting

Significance of the Deviation from Normal

Operation

Signal Contribution to Performance Anomaly – Normal/Predicted vs.

Actual

Likely Fault Condition

Signal Contribution to

Change in Health Status

Probability of Fault Match

Asset Health Trend

Asset Health Monitoring



Make new templates or

projects based on templates

Model Building

Easily Create New Models

From Scratch

Analytical Visualization


Intelligent Alert List

Case Library▲ Knowledge Capture


Data Playback

▲ Can have “n” number of training

data sets

▲ Determine when PRiSM would

catch the problems

▲ Determine when to alarm

▲ Easy to retrain model


Transient Analysis


Prescriptive DiagnosticsLikely Fault

Condition

Signal Contribution to Change in

Health Status


Vertical Markets and Equipment Types

▲ Turbine

▲ Compressor

▲ Electric Generator

▲ Pumps – Centrifugal, Integral

▲ VFD’s

▲ Fans, blowers

▲ Heat Exchanger, Boiler, Oven, Kiln

▲ Air Heaters

▲ Water Heaters

▲ Pulverizer, Crusher

▲ Condenser

▲ Transformers, Breakers, Capacitors

▲ Agitators, blender, Mixer

▲ Gearbox

▲ Chillers

▲ Seal systems

Power Generation

Power T&D

Oil & GasWater Management

Mining Process

Manufacturing & F&B


Types of Algorithms

• OPTiCS

• Soft Sensors

• Automated Data Cleansing

• KANN (RNN)

• Entropy

• Deep Learning (LSTM)



Real World

Predictive model

• Top temp of each zone

• Bottom temp of each zone

• Damper valve %

• Extraction fan speed

• Etc…

• Zone humidity

• Zone pressure

• Extraction fan current

• Oxidizer pressure


Issue

• Each oven zone needs to maintain certain heating

characteristics to drive product specifications, such as stack

height, color, % moisture content, etc. Predictive analytics

is used to minimize line stoppages.

Real world: Ovens

Early Warning Prevents Pump From Failure

Example PRiSM Catch

Observation:

Model is indicating an increase in vibration on multiple

bearings for the given flow rate and pressure

Result:

A coupling shim pack that was on the verge of failure


Catch PRiSM identified a conveyor motor drawing a higher than expected amount of

current (amps) for the given level of operation.

Cause In an effort to improve tracking, a mechanic changed the tension of the belt by

increasing the air pressure.

Avoided impacts If this hadn’t been caught, a bearing, roller, motor, or some

combination of these would have been compromised, resulting in significant downtime.

In addition, the belt would have likely separated.

Case Study: Dough Laminator

Issue Monitor dough quality by detecting multi-variate sensor changes which could indicate a

problem with a motor, gearbox, or bearing.

Predictive Models

Roller Mechanical

• Amps drawn by motors

• Speed of roller motors

• Gap between top & bottom rollers

Conveyor Mechanical

• Amps drawn by conveyor motors

• Speed of conveyor motors

• Speed of production line


Catch The system identified a higher than expected differential pressure

across the catalyst bed, indicating a clog which can disrupt the oxidation

process. However, this issue only occurred once in awhile.

Cause The site determined that this predictive notification only occurred

when the weather was extremely cold, causing the lines that bleed

moisture to the outside to freeze. Maintenance personnel unthawed the

lines, allowing the moisture to dissipate which, in turn, reduced the

pressure and eliminated the predictive analytics alarm.

Case Study: Oxidizer

Issue The oven oxidizer is responsible for reducing the environmentally

harmful emissions created during the baking process. This is done by

burning the oven exhaust gases at a high temperature (oxidizing) and then

running the hot exhaust over a catalyst to help induce a chemical reaction.

Predictive Models

Oxidizer Combustion Chamber

• Differential pressures

• Air temperatures

• Damper and thermal settings

Oxidizer Fan Inlet

• Amps from fan motor

• Differential pressures generated

• Temperature of oven exhaust


Case Study: Pump Control Issue

Example PRiSM Catch

Observation:

• Vacuum fell from 27.8 to 25.9 inches within an hour

• Hotwell temp increased from 98 degrees F to 123 degrees F within an

hour

Result:

Found water in the air lines to the suction valves at the vacuum pumps,

which was causing them to close


Case Study: Irregular Motor Operation

Example PRiSM Catch

Observation:

Motor current increased from 14 amps to 18 amps for a given load.

Result:

Plant found a leak on the floor above that saturated the Air Heater

insulation, causing expansion issues with the shroud


Issue

• Pasta production line creates methane gas which is used as

fuel in a cogen facility. The plant uses the methane to

generate steam in order to produce heat and electricity, both of

which are subsequently used back in the production process.

Predictive analytics is used to optimize maintenance practices

to avoid unplanned outages.

Predictive model

• Inlet/outlet water temps

• Inlet/outlet water pressures

• Inlet/outlet steam temps

• Inlet/outlet steam pressures

• Etc…

• Pump current

• Pump flowrate

• Boiler water level

• Fan current

Case Study: Cogeneration


PRiSM Catch

Observation:• Unit started after an outage

• Vibration step change on a LP turbine

• Notified Engineering & Plant

• Vibration data was collected & unit was retired for inspection

• Bolts on lower half of flow sleeve broke & flow sleeve contacted L-0 blades

Result:• Upper half of flow sleeve was no longer supported by lower half

• Avoided damaging multiple stages of blades, packing and diaphragms

• Est. Cost Avoided: $4.1 million USD

Outage

Case Study: Major Steam Turbine Catch


•31,000 MW; over 60 generating stations

•60% coal, 23% natural gas, plus nuclear, wind, hydro, and pumped storage.

•A collaborative work effort between AEP’s M&D Center, the plant, and the

engineering group resulted in several major turbine issues being identified and

corrected early, before much more devastating results occurred.

Summary of AEP

Case Study: Gas Turbine

Sample PRiSM Catches

Observation: Unaccounted for turbine vibrations for the given generation level

Result: After investigation, a turbine blade was found to have a chip that was

reducing the efficiency. This issue was getting worse over time and would have

eventually resulted in a failure.

Savings: The plant replaced the blade and estimated the cost avoidance at over

$17million (2016 PRiSM catch of the year)

Observation: A gas turbine bearing vibration had changed from 0.37 in/sec to

0.46 in/sec.

Result: During a planned outage, the plant found a chunk out of one of the stage

3 blades on the compressor.

Savings: The plant replaced blading on row 3, resulting in an estimated cost

savings of $1,500,000


Case Study: Transformer

PRiSM Catch

Observation:• Southern Company found a major transformer issue during Hurricane Matthew in October

2016. One of the PRiSM transformer health indicators (dissolved gas ratios) went extremely

high, indicating problems. Southern investigated, finding a transformer that was fully charged

with no load.

Result:• An explosion could have occurred in this situation if loads were brought on quickly. They

successfully prevented catastrophe. Typical replacement cost of a major transmission

transformer ($10 million USD).


Catch: System identified higher than expected vibrations on the second stage of

a compressor in relation to the current level of operation. Based on this detected

anomaly, the site investigated the issue during an upcoming planned maintenance

outage.

Cause: The site found a cracked impeller.

Avoided impacts: The customer estimated that over $500K in costs were

avoided by preventing reactive maintenance and unplanned downtime. Based on

the speed of the crack propagation, our Predictive Analytics detected this issue

approximately 3 months before operators would have noticed it.

Case Study: Compressor

Issue: Compressors are extensively used and critical to the operation of

one of the largest manufacturers of industrial gases in the world.


Line 8 Oven• Tag determining if the oven is ON/OFF

• Pounds per minute produced while running

Line 8 Accumulator• Percent fullness of accumulator

• Max capacity of accumulator in pounds

Packing Line 13• Tag ON/OFF

• Lbs packed per minute








• Lbs packed per min


• Lbs packed per min


Line Monitoring – Upstream/Downstream

Periods of increasing

accumulator levels

correspond with depressions

in the ‘Time Full’ chart.

Percent Fullness

of Accumulator

Time Left Until

Full Accumulator

Alarms typically configured for

when the ‘Time Full’ tag is less

than 5 minutes


Line Monitoring – Upstream/Downstream

Benefits and Costs Avoided

with Predictive Analytics

1. Reduce Unscheduled Downtime

2. Prevent Equipment Failures

3. Reduce Maintenance Costs

4. Improve Safety

5. Increase Asset Utilization

6. Extend Equipment Life

7. Identify Underperforming Assets


Integration with InTouch or System Platform


Open Integration

Any Data

Historian

Any

Control

System


Cloud Integration

Wonderware Online

Any

Control

System


Monitoring Diagnostics & Services Center (MDSC)

▲ Located in Chicago, IL

▲ Analytics Manager & Domain Experts

▲ Program Manager

▲ Subject Matter Experts (SMEs)

▲ Supporting Engineers with Industry Expertise

▲ 24x7 software monitoring with 8x5 analyst staffing

▲ Services offered:

▲ Model Building Services

▲ Monitoring Services

▲ Training Services

▲ Consulting Services


Questions?

Challenge/Opportunity

▲ Due to the advanced pattern recognition and alarming features of the Avantis PRiSM software, catastrophic equipment

damage and potential significant personnel injury were averted.

Duke Energy Steam Turbine


SE Products Used

▲ PRiSM

Results

▲ After reviewing the events and all actions taken to remedy the situation, a conservative estimate

of the equipment Avoided Costs was determined to be approximately $34.5 Million.

Duke Energy Steam Turbine


asset performance management predictive analytics ......asset value chain operations value chain...

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