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PRiSM 2.9.4: Predictive Asset Analytics

Confidential Property of AVEVA Group plc.

Christian-Marc Pouyez, Product Manager – Analytics & Energy

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

When the Condition to Monitor is Known and Definable

Condition is definable using rule based logic

Rule is fixed, and doesn’t change

based on loading, ambient or operational conditions

Intelligent integration of plant process data with

EAM systems

When the Condition to Monitor is Not Easily Defined and Requires Advanced Machine Learning

Industrial asset monitoring where

rule based modeling is too

complex

Where changes in loading, ambient

or operational conditions are

common

Specific rules for asset health

and/or performance

monitoring are not explicitly known

“The Whole is Greater than the Sum of Its Parts”- Aristotle

• Asset Health Analytics = Known Rules (CbM) + Machine Learning (PdM)

• Enterprise Asset Management integration and automated work order activation

• Reduce failures, increase reliability and improve performance

Condition-based Asset

Analytics

Predictive Asset

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

Monitoring without advanced analytics

Subtle changes from normal asset signature

Actual Value

Predicted ValueOu

tbo

ard

Be

ari

ng

Te

mp

(°F

)

Date and Time

A Smaller Box

Group data into smaller modes of operation

Ou

tbo

ard

Be

ari

ng

Te

mp

(°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

Example 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

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.

Real PRiSM catches Food & Beverage >> 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

System Overview

Predictive Analytics powered by PRiSM

Alarm

ManagerInterfaces

Web Server

Equipment

Models

Monitoring

Results

Database & Archive

Monitoring

EngineInterfaces

Analytics Server

Trending Alarming

Browser Client

Reports

Remote Access

Analytics Alarming

Client

Model Building

Interfaces

TXTODBC

REST API

Alert Notifications

Source Data

Analytics results output

What’s new in the latest PRiSM releases

2.9.0

• Case Management

• Asset Health

2.9.1

• Undo

• Reverse selection

2.9.2

• Improved fault diagnotics

• Improved Case Management

2.9.3• Hadoop Data Lake interface

2.9.4

• Wonderware Online Interface

• REST API and OPC UA updateREST

➢ HTML 5 PRiSM Web:

➢ Preferences / and User Profile

➢ SSRS and custom reports

➢ New Outlier Detection algorithm

➢ KANN Plugin

PRiSM 2.10Planned for July 2018

Page 15Confidential Property of Schneider Electric |

PRiSM Vision

PRiSM 2.9.x

Streamlining

User Experience

2019+

Reduce TCO

2018

Expanding modelling

capabilities

2017 2018 +

HTML 5

PRiSM Web

Self-Learning

Model

Cloud

Services

WW Online

Data Quality

Management

0 1 0

1 0 0 1

Single variable

Prediction

KANN

Model

Wizard

Central Model

Maintenance

Multi-variable

Prediction

KANN

PRiSM

REST API

REST

Load

BalancingUser Rights

Management

0 1 0

1 0 0 1

Near Real

Time Mode

Page 16Confidential Property of Schneider Electric |

PRiSM evolution towards SaaSFeatures initially implemented in on-premises version that are also steps towards SaaS

Complete

HTML 5 UI

Auto

Retrain

PRiSM

SaaSModel

Wizard Central Model

MaintenanceLoad

Balancing

Data Quality

Management

0 1 0

1 0 0 1

WW Online

interface

User Rights

Management

Infrastructure (IaaS)

• Hardware, Network, Power

• Eg. Server room in the cloud

Platform (PaaS)

• Toolkit to build software

• Eg. Machine Learning, Database

Software (SaaS)

• Application

• Eg. Concur, SalesForce

Monitoring (MaaS)

• On-premises or in Cloud

• Eg.: PRiSM MDSC

“As a Service” options

Networking

Data Center

Servers

Storage

Virtualization IaaS

Operating Systems

Middleware

Security PaaS

Data

Applications SaaS

MaaSMonitoring

Cloud Engineering Platform (CEP)

Virtual Machines (VM) in Azure

One or more VM per customer (no customer

sharing of VM)

Temporary approach to offer SaaS for non-cloud

software

Available today:

• DynSim

In progress:

• Avantis

• PRiSM

• Intelligence

Insight

Native Cloud offering

Available today:

• Historian

• MES Performance

In progress:

• More MES functions

• Intelligence

• Condition Monitoring

Aveva’s Industrial Software PlatformCloud architecture

Both options allow the delivery

of a SaaS solution

Now

MaaS

VM on CEP

May 2018

SaaS

VM on CEP

2019+

SaaS

Insight integration

Cloud Predictive Analytics as a Service evolution

VM: Virtual Machine

CEP: Cloud Engineering Platform

MaaS: Monitoring as a Service

SaaS: Software as a Service

Questions?

Page 20Confidential Property of Schneider Electric |