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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
Sch
ed
ule
d/P
lan
ne
d
Ma
inte
na
nce
Pre
dic
tive
Ma
inte
na
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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