Responding to your environment

Predictive Analytics for Maintenance UREASON | PPA Conference February 2017

UREASON

Active in: Process Industry, Telecom, Smart Grid, Smart Cities

since 2001

Vast Experience: Big Data, IoT, AI, Fault Management,

Predictive Analytics, Predictive Maintenance

A.I. Technology House

Proven track record with customers in wide variety of industry

– general theme: reason over large volumes of data to reduce

business uncertainty

Known as Innovator – from Concept to Feasibility and Roll-out

Main offices in the Netherlands (Delft) and the UK

(Maidenhead), sales offices in France and Germany

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We Entered Into the World of Real-Time Predictive Analytics

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Revolution(s)

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http://pages.experts-exchange.com/processing-power-compared/

Revolution(s)

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1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015 2020

Mem

ory

Pri

ce (

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B)

Year

Historical Cost of Computer Memory and Storage

Flip-Flops

Core

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SIMMs

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Big Drives

Floppy Drives

Small Drives

Flash Memory

SSD

1,00E-06

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Pri

ce (

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Year

Disk Drive Cost with TIme

Floppy Disk Drives Mainframe Drives

Small Disk Drives

Flash Memory

http://www.jcmit.com/memoryprice.htm

Revolution(s)

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World Economic Forum, January 2015, Industrial Internet of Things: Unleashing the Potential of Connected Products and Services

“If you can’t measure it, you can’t manage it.”

‘New’ Business Models Appearing

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Source TechWorld

New Models

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Predictive Maintenance

Predictive Maintenance:

Maintenance upon indication.

Indication is based on predictive

models that have been trained on

datasets that capture:

- Asset condition

- Asset usage

- Assets failure modes

- Asset failures

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Predictive Maintenance:

• Reduces size and scale of repairs

• Reduces downtime

• Increases accountability

• Reduces number of repairs

• Increases asset useful lifetime

• Increases throughput

• Increases quality of output

• Reduces investments

• Lowers overall maintenance costs

through better use of labor and

materials

Predictive Maintenance Programs

Identify the critical assets (critical to operations)

- Total Loss of Production

- Partial Loss of Production

- Negligible Impact

Balance the investment in PM programs on TMC

- Use failure data already available

- Improve asset data management

Keep in mind that often the effects (financial benefits) of

Predictive Maintenance programs or often not seen in 1st years

of program but successive years!!

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ADVANCE

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Ingredients Needed for Predictive Maintenance

Technical

• Historical Data - Data Historians DCS/SCADA, EAMS

Systems (Maximo and alike)

• Classification of failures – Asset failed on ‘time/date’, failure is

type ‘x’ in addition to Common Failure Mode info

• Analytical Tools/Models to train predictive models –>

UREASON Platform

• Streaming Data - Asset vibration, temperature, load, …

• Real-Time Monitoring System –> UREASON Platform

Organizational

• Start small

• Project champion

• Management Support

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Typical Steps– Predictive Maintenance Programs

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5

Updates &

Tuning (3)

4

Model

Deployment

3C

Model

Validation

3B

Model

Training

3A

Feature

Selection

2B

Data

Preparation

2A

Data

Understanding

1

Business

Understanding

Heavy end-user involvement

Knowledge Intensive Heavy end-user involvement

Data Collection Data Science

Process: ~ 2 - 4 Weeks – Feasible YES/NO

Based on results of the POC and business adoption possible rollout plan can be established

Example: Asset availability, Industrial Turbines, Energy Sector

Use Case : Condition based monitoring across all Turbines

• Comparison with training data, showed furnace was 50˚C

above average

• Operators ran routine maintenance checks, but showed NO

ISSUES.

• Business decided not to ignore analytics

• Furnaces were taken offline and early maintenance

procedures instigated

• Maintenance found faults with Combustion units, Rotating

blades, etc.

• Resulting saving accounted for £5 million over one year.

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Predictive Models

Feature and failure data determine to what extend supervised

learning models can be used.

In the PmP/APM domain ANNs provide value as (analogue)

‘soft sensors’ that describe complex data relationships. As such

they can be used to highlight suspect anomalies – e.g.

prediction of bearing temperature/axle vibration versus

measured.

In the PmP/APM domain SVM provide excellent value for

asset/process failure prediction. In cases failure data is absent,

or insufficiently available Unsupervised learning route is an

option left to find structure in the inputs.

UREASON prefers to combine condition/asset data with event

data to create hybrid probabilistic failure models (see image)

that include CEP and ML techniques.

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Source: http://ureason.com/espcep-predictive-maintenance/

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Responding to your environment

Contact

UREASON International BV

Drie Akersstraat 11

2611 JR, Delft

The Netherlands

Telephone:

General: +31 85 273 49 20

Fax: +31 85 273 49 29

Email:

General: info@ureason.com

Support: support@ureason.com