Advances in PI System Streaming Analytics€¢Calc engines for computational mathematics exist...

#OSIsoftUC #PIWorld ©2018 OSIsoft, LLC#OSIsoftUC #PIWorld ©2018 OSIsoft, LLC

Advances in PI System Streaming Analytics

Stephen Kwan, OSIsoft Product Manager

Jim Stewart, Ph.D., MathWorks Senior Engineering Manager


• Use PI System as the data infrastructure

• Enable users to execute advanced streaming calculations• Execution based on new events into the PI System or by clock

• Encourage reuse of existing domain expertise

• Support different personas• Engineers, designers, users, etc.

• Retain ease of use, feature set and existing workflow

Goals and Objectives

2


• Asset Analytics released in 2014

• PE replacement

• Leverages AF and PI System

• Configuration experience

• Robust engine with scheduler

• Widely used, but users want more• Advanced calculations

• More flexibility

• Retain “ease of use”

Advanced Streaming Calculations

3


• Calc engines for computational mathematics exist

• Take advantage of existing applications

• MATLAB is the first 3rd party integration with Asset Analytics• MATLAB is used by millions of engineers and scientists

• Many PI users have existing MATLAB scripts

• Collaboration between MathWorks and OSIsoft• Mutual feedback for improvements

• Faster time to market

• Similar release cadence to the PI Server

Integration with 3rd Party Calc Engines

4


Personas: Streaming vs. Adhoc

Adhoc investigation and analytics development

Online, scheduled, streaming analytics

5


Integration with MATLAB Production Server

Analytics Development

MATLABMATLAB

Compiler SDK

Package Code / test

MATLAB Production Server

• Requirements • PI Asset Framework 2018

• MATLAB Production Server 2018a

• MATLAB, MATLAB Compiler and MATLAB Compiler SDK

HTTP/HTTPS

6


OSIsoft Headquarters• Leverage PI System to support the facility

• Collect data from Building Management System (BMS)

• Operational excellence

• Single pane of glass

• Energy management

• Optimize energy usage

• HVAC performance

• Anomaly detection

How can we predict Energy Usage?

7


MATLAB and MATLAB Production Server

Jim Stewart, Ph.D., MathWorks Senior Engineering Manager

#OSIsoftUC #PIWorld ©2018 OSIsoft, LLC

Founded in 1984

Revenues ~$1B in 2017

~4000 employees worldwide

More than 2 million users in 175+ countries

95% of technical support calls reach an engineer with an advanced technical degree in under 30 seconds

MATLAB® SIMULINK®

Technical computingSimulation and model

based design

MathWorks is the leading

provider of technical computing software

9


Why choose MATLAB? MATLAB lets you focus on solving your problems

Reliableentrusted to send a

spacecraft to Pluto, create

certified code for medical

devices

Productive

environment tuned for engineering and

scientific work

Ready to usewith toolboxes that

work out of the box

Execution speedwith optimized code that

leverages GPUs, clusters,

and clouds

Ready to run on

production systems

without rewriting

code

10


Medical Devices

Aeronautics

Off-highway

vehicles

Automotive

Oil & Gas

Industrial Automation

Clean Energy

Logistics

Retail

Finance

Healthcare

Management

Internet

Railway Systems

Data Analytics

11


Design and test condition monitoring and predictive maintenance algorithms

• Feature extraction for designing condition indicators

• Machine learning and time-series models for remaining useful life (RUL) estimation

Predictive Maintenance Toolbox New

12


Analyzing HVAC data with MATLAB follows four basic steps

13


Its easy to explore, clean, and preprocess data with MATLAB

Access and Explore Data

1Preprocess Data

2

14


Its easy to build a predictive model in MATLAB

• Use apps to help understand methods and workflows

Develop Predictive

Models

3

15


Its easy to try different models on the same data

Neural NetworksTime series models

(ARIMA, GARCH,..)

Develop Predictive

Models

3

16


MATLAB’s Regression Learner App lets you train with multiple algorithms in parallel

Develop Predictive

Models

3

17


Deploy your predictive model as a reliable and scalable service with MATLAB Production Server

Enterprise

Application

Mobile / Web

Application

Analytics Development

MATLABMATLAB

Compiler

SDK

< >

Package Code / test

Data sources

3rd party

dashboardScale and secure


Request

Broker

Worker processes

Access

Integrate

Deploy

Integrate with

Production Systems

4

18


The Deployment Tool with Compiler SDK makes it easy to package and deploy your predictive model

Integrate with

Production Systems

4

19


MATLAB Production Server operationalizes your predictive model as a scalable and reliable service

AF with Asset Analytics

Call analytic

functions using

REST API

Integrate with

Production Systems

4


Request

Broker

Worker processes

Predictive Model

20


• Support MATLAB data needs

• Asset Analytics mostly handled single values only

• Introduce support for multiple values as arrays in analyses

• Other complex types

• Configuration UI improvements

• Enhance existing functions for single values and arrays

• PI data infrastructure = first class citizen

Enhanced Data handling – Complex Data Types

21


• Retain existing user experience

• External codes are treated as Expression functions

• Authoring, testing, preview, scheduling, backfill, recalculation, etc. remain the same

User Experience

22


Data Retrieval and Array Operation Functions

23


I need the last 50 archived valuesOf the last 50 archive values, I only want ones > 185Of these values, I want to apply a transform to themI need data for the last day in 30 second increments

Example Use Cases


Demo

25

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• Asset analytics integration with MATLAB can help solve new problems

• Full use of your PI data infrastructure

• Execute your custom MATLAB functions online in a streaming fashion

• MATLAB functions are treated just like any built in functions

• Almost zero learning curve for existing users

• Support different use cases• Adhoc investigation vs. streaming calculations

Conclusions

27


2:30 – 4:15PM PARC 55: Mason, Level 3

Streaming calculations with the PI System and MATLAB

Abstract:

In this live How To, we will demonstrate the upcoming AF 2018 release with native integration between asset analytics and MATLAB Production Server enabling you to execute your custom MATLAB functions in a streaming fashion leveraging the PI System data infrastructure.

More Details and Q/A with Developers

28


• Stephen Kwan

• [email protected]

• Product Manager

• OSIsoft, LLC

• Jim Stewart, Ph.D.• [email protected]

• Senior Engineering Manager

• MathWorks, Inc.

29

mailto:[email protected]

mailto:[email protected]


Questions

Please wait for the

microphone before asking

your questions

State your

name & company

Please remember to…

Complete the Online Survey

for this session

30

#OSIsoftUC #PIWorld ©2018 OSIsoft, LLC 31

Thank You

Merci

Grazie

Date post:	10-Jul-2018
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Advances in PI System Streaming Analytics€¢Calc engines for computational mathematics exist...

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