Pythia A.I. …a story of digitalization, · Pythia™ A.I. is delivered via Remote or Passive Vibration Diagnostics data •Evaluating over 120,000 components daily …Continuously

© Siemens Gamesa Renewable Energy S.A

Pythia™ A.I. …a story of digitalization, by SGRE

January 29, 2019

Samuel Tasker

Aftermarket Manager, North America

© Siemens Gamesa Renewable Energy S.ASource: 2017 Annual Report

SGRE at a glance


Artificial Intelligence

The term “Artificial Intelligence” was coined in 1956, at ah historic conference at Dartmouth

But it has only been in the past 10 years, for the most part, that we’ve seen the first truly substantive glimpses of its power and application.

A.I., as its now universally called, is the pursuit of performing tasks usually reserved for human cognition; recognizing patterns, prediction outcomes clouded by uncertainty, and making complex decisions.

Lee, Kai-Fu. “The Four Waves of A.I.” Fortune Magazine, New York, November 2018: 92-94.


Blade condition assessment

Vibration Diagnostics Monitoring




Stage 1 – Internet A.I. is powered through the webStage 2 – Business A.I. is algorithms trained on proprietary data setsStage 3 – Perception A.I. gets an upgrade with eyes, ears, and (sensors)Stage 4 – Autonomous A.I. integrates all previous waves



What is Pythia™ A.I. and SGRE’s Vibration Diagnostics?

Pythia™ A.I. is delivered via Remote or Passive Vibration Diagnostics data

• Evaluating over 120,000 components daily

…Continuously investing and improving this detection service as evidenced by backing up data as far back as 2003.

• Pythia™ is agnostic to turbine type.

• Siemens models applied to Gamesa and Suzlon for detection practices with service contracts

• Vibration Diagnostics is a pillar of our service contracts and we extend it if you’re off contract as well ;)


Condition Monitoring Hitrates

Gearbox98,9%

IMS BEARINGS

99,25%

LSS BEARINGS

100%

PLANETARY BEARINGS

94,12%

PARALLEL GEARS100%

PLANETARY GEARS94,29%

HSS BEARINGS

100%

MAIN BEARING99,14%

GENERATOR

94,79%

YAW BEARING

100%

TOTAL

~99%99,94%


Why we believe

Business Case Story: “inspect the pl wheel 2nd stage again.”


Why we believe – Subsurface detection

BACKGROUND lesson

From 4 inspections over a 6 week period, Pythia demonstrated subsurface detection capability.

With subsurface failure profile pictured, Pythia detection is now able to provide accurate and repeatable alerts

…and fast; a typical analysis may take 6-8 hrs to determine what may be wrong for this failure mode

BENEFITS of earlier detection

Larger knowledge window helps planning scarce resources such as cranes and repair specialists around wind or to enable mobilization synergies

More knowledge to control business options proactively instead of decisions during outage

Less onsite inspection demand to use site resources for more critical business needs and reduce turbine visits via fewer false positives

PythiaCase Started

Without Pythia


Why we believe… a practical perspective from a planning manager:

Detection in advance of failures is indeed difficult comparison to make between different site conditions with different equipment models.

Planning benefits from with-in large similar-fleet type include:

- 3-7 months planning windows for gearbox replacements and a current 86% effectiveness at identification while the turbine is still running.

- Allows for coordinating with craneless solution advantages.

- 3- 12 months planning windowns for main shaft/bearing replacements.

- This reduces inventory demand constraints


What is in Pythia’s™ special sauce?

▪Among other characteristics, A.I. is based on a neural network, or deep learning algorithms, running on + 500 TB of data that is equipment agnostic to bring A.I. to practical use

▪The predictive magic simply needs an internet signal or passive data source, and premium monitoring sensor hardware

▪So, what’s a neural network and a deep learning algorithm?



While human brain tends to focus on the most obvious correlations between the input data and the outcomes, a deep learning algorithm trained on an ocean of information will discover connections between obscure features of the data that are so subtle or complex we humans cannot even describe them logically.

When you combine hundreds of thousands of them together, they naturally outstrip the performance of even the most experienced humans.

A.I. algorithms now beat humans in speech recognition, face recognition, the games of chess and Go, reading MRIs for certain cancers and any quantitative field…



Deep Neural Networks

A visual comparison between a standard PCA and Neural Network illustrates advantages

Classic PCA algorithms giving very early warnings. Dependant on data from faultless state

Deep Neural Network (DNN) giving even earlier warnings. Giving a probabilistic output and not dependant on data from faultless state

2010 2011 2012 2013 2014 2015 2016 2017 2018

DNN PCA FAULT


Practical benefit of Neural Network

WHERE IS BO?

A neural network is merely the concept to help us realize the power of the A.I.



WHERE IS BO?


It can find answers in things that aren’t as apparent to a human and it can do it in volume

In our data it can see the things we just can’t see



WHERE IS BO?




It can do it faster

It can learn and get better from previous experiences



WHERE IS BO?




It can do it faster

It can learn and get better from previous experiences

It provides consistent delivery of predictions

And it will make fewer mistakes!




Because A.I. can be programmed to

maximize profitability or replace

human labor, it adds immediate

value to the economy.

A.I. is fast, accurate, works around

the clock, doesn’t complain, and can

be applied to many tasks, with

substantial economic benefit


What does this all mean to our business in Wind?

Pythia™ and A.I. will see a failure before any other means …before visual inspection and before operational impact

Better prediction maximizes the operating choices for maintenance action and better RUL and lower LCOE

This is too late!!! predictive RUL failure model prototype


Failure prevention vs failure detection

Pythia™ unsurpassed intelligence to predict and prevent is the enhancement behind our standard premium hardware


Consider the difference to your business with your data handling:

Generic Monitoring

▪ Failure recognition

▪ Unable to identify specific failures modes

▪ Limited thresholds limit the decision making window for shutdown

SGRE Vibration Diagnostics & Pythia™ A.I.

▪ Pattern Recognition

▪ Identify specific failure modes

▪ Provides insights to allow larger decision makings windows before shutdown is required

▪ Advancing services for multiple turbine platforms


•Pythia™ is an A.I. ethos embedded in the SGRE service philosophy

•By providing increased up-time, reduction of collateral damages, bundling repair resources, insurance reduction, fewer turbine visits and lower OPEX costs

•After demonstrated methods of prediction and cost controls, we now know this is a critical requirement to extend LCOE to the customer.

• It enables our PROACTIVE capability to make better choices for controlling our business

Summary of our commitment to Pythia™ and A.I.


The evolution of an A.I. masterpieceBEFORE Pythia▪ Human Experts

▪ Manual Evaluations based on

outliers.

▪ Hundreds of evaluations

every day

based on outliers

▪ No early-detection

▪ Advanced models reducing

need for manual evaluation of

all data

The FUTURE of Pythia▪ Virtual Experts

▪ Evaluation by artificial-intelligence

▪ Millions of evaluations every day.

▪ Robust detection and evaluation

of outliers in near real time.

▪ Prognostics predicting remaining

use life time on components and

current reliability.

▪ Proactive and reliability centered

maintenance

With Pythia▪ Human Experts & Vitual

Experts

▪ Semi-automated evaluations

based machine learning

algorithms.

▪ Thousands of evaluations every

day based on outliers

▪ Increased early-detection

▪ 120.000 components evaluated

daily

Documents

Pythia A.I. …a story of digitalization, · Pythia™ A.I. is delivered via Remote or Passive Vibration Diagnostics data •Evaluating over 120,000 components daily …Continuously