PREDICTIVE ANALYTICS AND DESIGN OPTIMIZATIONFOR EVERY EXPERTISE

November, 2016

Big data and predictive

health maintenance

Sergey Morozov, CEO

About DATADVANCE

DATADVANCE is an independent software vendor specialized in

development of process integration, data analysis and design

optimization software.

DATADVANCE has been incorporated in 2010 as a result of a

collaborative research program by:

Institute for Information Transmission Problems – one of the leading mathematical

centers with three Fields prize winners on the staff, and

Airbus – a global leader in aerospace and defense industry.

Using our software Airbus reduced design lead time by up to 10%*

Used across all Airbus engineering departments

Used in Airbus customer service department

100+ active users and 200+ engineers trained

Disruptive approach to engineering data analysis and optimization led to drastic

improvements, and now Datadvance’s platform enables this for all

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* Airbus press release

Our products and services

Two product lines based on the same platform and math core:

pSeven DSE – a software platform to build, explore and operate predictive

models at the product design stage powered by pSeven Core, a software

library of advanced data analysis and optimization mathematical methods

pSeven PHM – a software platform for operational predictive maintenance

We integrate elements of artificial intelligence into customers’

IT systems

Our domains of excellence:

Computed Aided Design and Engineering

Automation of engineering and manufacturing processes

Operational Predictive Maintenance

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Industry 4.0: Cyber-physical systems & Predictive analytics

Products dominated by mechanical components are replaced by smart and connected systems integrating

mechanical, electrical, controls.

Smart sensors collect data during product manufacturing and service, generating vast amount of data.

Internet connection is capable to transfer big amount of data to people, machines or services companies.

Machine learning algorithms process data to optimize product behavior, operations and maintenance.

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DataReports/analytics

MonitoringPredictive analytics

What has happened?Why did it happen?

What is happeningright now?

What is going to happen in the future?

Operational Predictive Maintenance

Operational predictive maintenance

Online and real-time monitoring of assets/equipment using various embedded sensors

Real-time prediction of the condition of assets/equipment using machine learning techniques

Determines the operational status of equipment

Evaluates present condition of equipment

Detects abnormal conditions in a timely manner

Maintenance at appropriate or practical time, i.e. if any particular asset requires maintenance

Initiates actions to prevent possible forced outages

Benefits:

Significant reduction in unplanned machine downtime

Minimization of production losses

Increase of customer quality perception and satisfaction

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Predictive maintenance explained

delay

normal warning

Scheduled maintenance

Equipment Fault

normal

normal

Maintenance

Time

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d/P

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d

Ma

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na

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Pre

dic

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Ma

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nce or

delays and costs

no information

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КОНФИДЕНЦИАЛЬНО

Why railways need Operational Predictive Maintenance and Predictive Analytics?

Large historical datasets from diagnostic and monitoring systems open a way

to create high-quality predictive models – the main enabler for service-by-

forecast approach.

OPM allows to reduce OPEX of infrastructure and to increase reliability and

security at the same time. For example:

Reduced locomotive availability due to unexpected breakdowns

Rolling stock failures like broken wheels or valve failures in tankers

Unfulfilled customer orders and SLA warranties

High network congestion and mission failures, like derailments

Poorly functioning signals and wayside equipment

Unnecessary train stops due to malfunctioning wayside equipment

Failures in locomotives, railcars and commuter trains

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Over $400 mln. is lost

annually in the U.S.

due to asset failures

within Class I Railroads

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Online Offline

pSeven PHM: Intellectual data analysis for operational predictive maintenance

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Data collection and Transmission

Data Storage

Data analysis and Visualization

Maintenance Strategy Implementation

(Decision Making / Asset Management)

Data analysis, Construction of predictive

models and Interpretation

Flows of data and models

Users

Data scientists

Analysts

How does it work? Typical data analysis pipeline

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: Airbus Real Time Health Monitoring

Видео-ролик в отдельном файле

(3 минуты)

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Video online (3 min)

We developed machine learning and predictive modeling capabilities of AiRTHM(Airbus Real Time Health Monitoring)

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: Prediction of failures of aircraft auxiliary power unit

Goal

Predict failures of APU to improve maintenance procedure

Data for model training

30 aircrafts and about 200 parameters per aircraft

Learning data set: 3 years (~400 flights during an year)

Model testing: ~0.5 year in operation

Benefits of predictive maintenance

Early warning about some types of failures

Detection of failures with an accuracy of about 90% (9 correctly

predicted failures account for 1 false alarm)

Cost reduction associated with downtime of plane due to

unexpected failures by ~30%

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Benefits of predictive maintenance for airlines

Predictive maintenance (aka condition-based maintenance) technologies:

perform maintenance at an appropriate time, and before the equipment loses optimum performance or fail

reduce disruptions to facility operations and increase equipment availability

monitor the condition of in-service equipment.

Benefits:

“Predictive maintenance can increase aircraft availability by up to 35%”, – Luiz Hamilton Lima, vice president of

services and support at Embraer

Adopting predictive maintenance through the use of data analysis can reduce maintenance budgets by 30-40%,

reports claim.

Sizing the benefits:

~$10 000/hour – cost of keeping a commercial passenger jet grounded*.

~95 000 hours – Delta Airline delay from July, 2015 to July, 2016.

$30 mln./year – potential savings if just 10% of the delay hours is because of maintenance

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* According to the aerospace arm of SAP, the software group

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: Analysis of accident of power plant gas turbine

Information about accident

Location: Some power plant in Russia

Accident data: May 2016

Estimated gas turbine maintenance cost: 10+ mln. Euro

Turbine has 100+ sensors

Data from sensors was stored but it was not analyzed online

Would it be possible to predict accident and stop turbine before

accident in case of online monitoring and diagnostics?

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: Analysis of accident of power plant gas turbine

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Accident early precursor

Systematic deviation from the normal state

Accident

The turbine can be stopped 2 weeks prior to the accident!

No domain specific knowledge was used. Just pure data analysis!

Potential cost savings: ~10 mln. Euro!

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: Railway Monitoring System: Incident Ranking

Problem

Railway monitoring system automatically logs a large amount of complex alerts (incidents). Incidents should be handled manually by operators.

Moscow Railway: ~5000 incidents a day, 24x7, ~4 hours to fix

Vast majority of incidents (~97%) are not related to real failures, and occur due to unplanned maintenance and flaws of diagnostic procedures.

As a result, operators spend much of their time on non-critical incidents and do not have time to handle all incidents, missing the real system failures.

Moscow Railway: Wrong ranking is the reason of ~54% of “missed” incidents

Project scope:

Automatic incident ranking by importance with machine learning on real historical data (5.5 bln alarms, 4.5 years) Predict probability of failure

Root cause analysis for major accidents

recommendations on the of diagnostic tools coverage

Result - Significant increase in situation center efficiency:

~2x times increase in reaction time

~5x times load drop

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Project run by Telum, our partner company in railway industry.

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Conclusions

Industry 4.0 is already here, with its challenges and opportunities!

Operational predictive maintenance enabled by big data and advances in machine learning allows to

Significantly reduce unplanned asset/machine/infrastructure downtime

Reduce OPEX of infrastructure

Increase reliability and security at the same time

Increase of customer quality perception and satisfaction

DATADVANCE and Telum, our partner company in railway industry, are your reliable partners to

implement efficient predictive analytics solution for your railway company.

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pSeven PHM: Key features of the platform

Build and explore predictive models

Data import, cleaning and pre-processing

Feature selection and extraction

Advanced data analysis mathematical methods

– Efficient in-house methods for anomaly detection and failure prediction for multidimensional data

– Clustering on graphs for automatic extraction of system components, including in-house approaches

– Modern methods of robust classification, including imbalanced classification

SmartSelection™ technology to select the mode efficient data analysis method

Post-processing and visualization

Deploy predictive models as services

Package and export predictive models

Publish and deploy to pSeven model server (Model as a Service)

Integrate predictive models into existing IIoT ecosystem

Powerful cloud software platform with reach data pre- and post-

processing capabilities

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