CBM, Big Data and the Proactive Enterprise -...

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FP7-ICT-2013.1.3 Grimstad, Norway , 08.06.2015 CBM, Big Data and the Proactive Enterprise Riglogger™, Proasense, Prognostics and Health Management Dr. Ing Tor I. Waag, MHWirth

Transcript of CBM, Big Data and the Proactive Enterprise -...

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FP7-ICT-2013.1.3

Grimstad, Norway , 08.06.2015

CBM, Big Data and the Proactive EnterpriseRiglogger™, Proasense, Prognostics and Health Management

Dr. Ing Tor I. Waag, MHWirth

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MHWirth in Brief

June 9, 2015 2

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Global Reach

June 9, 2015 3

Equipment on ~500 rigs 4 Regions | 4300 employees

One Company

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World-class solutions, lifecycle services and advanced drilling systems for onshore and offshore drilling units, world wide

We go beyond the conventional drilling solution to provide our customers with the safer, more efficient and reliable alternative

Today more than 500 floaters, jack-ups and fixed installations operate in the market with our equipment

Powerful Collaboration

Key Figures 2014

Revenue NOK 10 681 mill

EBITDA NOK 941 mill

Margin 8.8%

Note: Preliminary unaudited pro form figures

June 9, 2015 4

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Our Products and Services

June 9, 2015 5

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How our Drilling Equipment drives change

Drilling Equipment

June 9, 2015 6

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Drilling, Make and Break

June 9, 2015 7

Uninterrupted drilling operations and high performance - Our drilling, make and break equipment is a powerful collaborator during drilling operations.

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Content

June 9, 2015 8

Riglogger™

PHM

Proasense

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Riglogger™

An IT infrastructure built by MHWirth AS (previously Aker Solutions)

Real time acquisition, on-the-fly analytics and long term storage of all available, drilling related variables on oil platforms

Valuable for evaluation of Performance Maintenance Incidents

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Prognostics and Health Management , PHM

An internal MHWirth project Real time acquisition, on-the-fly analytics and long term

storage of industrial Big Data Valuable for evaluation of condition and planning of

Maintenance Reduction of Product Lifecycle Cost Opportunistic instead of Calendar based Maintenance

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Proasense

An IT project in the EU 7th Framework Program Real time acquisition, on-the-fly analytics and long term

storage of industrial Big Data Valuable for evaluation of Performance Maintenance Incidents

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Definitions

Condition Monitoring Condition Based Maintenance Prognostics Proactivity

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Definition: Condition Monitoring

“Activity, performed either manually or automatically, intended to observe the actual state of an item.”Definitions [BS 13306]

CM is a source of information, monitoring the state of equipment

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Definition: Condition Based Maintenance

”Preventive maintenance based on performance and/or parameter monitoring and the subsequent actions.”Definitions [BS 13306]

CBM is a maintenance strategy

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Definition: Condition Based Maintenance

CBM consists of all these activities:

Data acquisition and management Analysis Interpretation Fault detection Diagnosis Prognosis and prediction Decision-making Planning and performance of maintenance actions

Ref: Al-Najjar 2007b, in E-maintenance, by Kenneth Holmberg, Adam Adgar, Aitor Arnaiz, Erkki Jantunen, Julien Mascolo, Samir Mekid

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Definition: Prognostics

The art and science of making scientifically sound, observation based predictions

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Definition: Proactivity

The art and science of making scientifically sound recommendations or automatic actions based upon prognostics

Includes probability distribution function based, automatically calculated recommendations to act

Includes predicted cost and gains of several possible actions to choose between

Also includes the cost of delay, important in our business

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Detection of change

The art and science of detecting changes to a process variable

Departure from a constant to an increasing value Change from a constant to another constant value Change from one speed to another speed Chance from a linear function to a non-linear (accelerating)

function

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Detection of change, contd. Detection of all of the previous taking into account:

(e.g. as probabilistic cost functions ) The cost of missed detections The cost of false alarms

Set appropriate thresholds in terms of standard deviations σ for level or slope, balancing A and B

The cost of delay Averaging reduces standard deviation σ Averaging delays detection by the number of samples included in the

averaging Computational cost

not trivial for thousands of variables or combinations of variables

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Detection of change

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Proasense vs other methods Drilling vs steady state production

Event based data flow Event detection Complex event processing Detection of change Probabilistic decision making Automatic action (or notification to act, cannot interfere in critical,

remote operations)

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Proasense, the OODA cycle The phrase OODA loop refers to the decision cycle of

observe, orient, decide, and act, developed by military strategist and USAF Colonel John Boyd.

Boyd applied the concept to the combat operations process, often at the strategic level in military operations. It is now also often applied to understand commercial operations and learning processes.

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Proasense, the OODA cycle

Observe (sensor input, event detection) Orient (complex event processing) Decide (probabilistic decision support) Act (notification, or automatic feedback)

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Data input

Event detection

criteria

Event detectionComplex event

processing

Analyse dynamic

behaviour

Offline analytics:Establish

normal rangeof behaviour

Online analytics:Detection of change

(slow, rapid)

ActDecideCost functions

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Understanding of the importance and benefits of the proactive behavior in an

enterprise context

To enable comprehensive observation of the relevant business context/ecosystem

(Observe)

To enable semantic understanding of sensed information (Orient)

ProaSense Objectives (1-3)

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Making decisions ahead of time (Decide)

Proactive handling for sustainable business improvements (Act)

Demonstrate the efficiency and added business value

Disseminate results in the wider research and industry community

ProaSense Objectives, continued

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Sensing Architecture Layer

Data Infra-structure

Enterprise data adapter

Business context data adapter

User-provided input

Sensing Architecture LayerSoftware SensorsHardware Sensors Human Sensors

Historian adapter

CSV files Legacy system(s)Historian

External systemsOSIsoft PI

(MHWirth)HYDRA MES

(HELLA) MHWirth HELLAOpen Historian

ChallengeThe design of the architecture will be in the spirit of “Big Data” supporting three major dimensions when dealing with intensive streaming data, namely: • Volume (scale of data being processed), • Velocity (speed of moving data and optimized reaction time), and • Variety (supporting heterogeneous types to data under consideration).

State of the art analysis Internet of Things (IoT) platforms that support the registration and management of heterogeneous sensors and their data, providing APIs and data aggregation.• Commercial solutions: Xively , NanoService, TempoDB• Open source solutions: Nimbits, ThingSpeak, 52° North SOS , SensApp, ThingML

Approach • Process/filter data as close to the sensors as possible• Virtual sensors that optimize sensor data acquisition by filtering raw

sensor data, e.g. data cleaning, sampling frequency, merging sensor data, and simple calculations.

• Using common standards and semantics (e.g. SSN ontology) to precisely specify structure/context of sensor data

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PrognosticsMarkov and stochastic processes

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Markov Decision Process Parameters of the method, general

Markov Decision ProcessProbability Distribution of the occurrence of the eventParameters of the probability distribution

Input from events

Actionsai

Costs Cai (tai)

Delays δai

Cost of undesired

event Cu

Optimal action

Optimal time of action

Input from user

Output

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Cost Matrix / Optimisation and (Probabilistic) RulesParameters of the method

Cost MatrixAnd

Probabilistic Rules

Corrective Maintenance

Cost Cc

Planned Maintenance

Cost Cp

Planned Time for

Maintenance

Optimal Time for Maintenance

Input from events

Input from user

Output

Predicted time of undesired event

• If there are more than one possible action, the same procedure can be followed for each action and then, the action which minimizes the generalized cost is selected.

• Probabilistic Rules can be used to express company’s policies regarding maintenance when there is uncertainty about a decision.

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Complex Event Processing

Sensors

Applications

Processes

Notifications

ActionsEven

t Pr

oduc

ers

Even

t Co

nsum

ers

Event Processing Network

Relevant Situations

Humans

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• Processing pipelines:– Integration of streams, real-time processing logic and consumers– Fast pipeline definition and modification should be possible

• without further implementation effort• for non-technical users

Modeling Distributed Complex Event Processing PipelinesObjectives

Example: Sensor Transformation Pipeline

Sensor #1

Filter bythreshold value

Enrich withcontextualknowledge

Performpattern

detection

DecisionManagement

Event Stream

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Motivation: Technical HeterogeneityIntegration of heterogeneous technical landscapes

Sensor #1

Filter bythreshold value

Enrich withcontextualknowledge

Performpattern

detection

DecisionManagement

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Motivation: Technical HeterogeneityDistributed processing

Source EPA EPA EPA

Source

Source

Source EPA

EPA

EPA

EPA

EPA

EPA

Consumer

Consumer

Consumer

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Motivation: Technical HeterogeneityDifferent stream processing technologies depending on the purpose/data frequency

Source Storm Online Analytics

Online Analytics

Source

Source

Source Spark

Online Analytics

CEP Engine

CEP Engine

Storm

CEP Engine

Consumer

Consumer

Consumer

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Motivation: Technical HeterogeneityMultiple protocols on the event transportation layer

Source Storm Algorithm Algorithm

Source

Source

Source Spark

Algorithm

CEP Engine

CEP Engine

Storm

CEP Engine

Consumer

Consumer

Consumer

MQTT

MQTT

JMS

Kafka

Kafka

MQTT

Websocket

Websocket

JMS

AMQP

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ChallengeEnd-To-End Modelling of distributed stream processing pipelines

Source Storm Algorithm Algorithm

Source

Source

Source Spark

Algorithm

CEP Engine

CEP Engine

Storm

CEP Engine

Consumer

Consumer

Consumer

MQTT

MQTT

JMS

Kafka

Kafka

MQTT

Websocket

Websocket

JMS

AMQP

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Copyright and DisclaimerCopyrightCopyright of all published material including photographs, drawings and images in this document remains vested in MHWirth and third party contributors as appropriate. Accordingly, neither the whole nor any part of this document shall be reproduced in any form nor used in any manner without express prior permission and applicable acknowledgements. No trademark, copyright or other notice shall be altered or removed from any reproduction.

DisclaimerThis Presentation includes and is based, inter alia, on forward-looking information and statements that are subject to risks and uncertainties that could cause actual results to differ. These statements and this Presentation are based on current expectations, estimates and projections about global economic conditions, the economic conditions of the regions and industries that are major markets for MHWirth AS and MHWirth AS’ (including subsidiaries and affiliates) lines of business. These expectations, estimates and projections are generally identifiable by statements containing words such as “expects”, “believes”, “estimates” or similar expressions. Important factors that could cause actual results to differ materially from those expectations include, among others, economic and market conditions in the geographic areas and industries that are or will be major markets for MHWirth’s businesses, oil prices, market acceptance of new products and services, changes in governmental regulations, interest rates, fluctuations in currency exchange rates and such other factors as may be discussed from time to time in the Presentation. Although MHWirth AS believes that its expectations and the Presentation are based upon reasonable assumptions, it can give no assurance that those expectations will be achieved or that the actual results will be as set out in the Presentation. MHWirth AS is making no representation or warranty, expressed or implied, as to the accuracy, reliability or completeness of the Presentation, and neither MHWirth AS nor any of its directors, officers or employees will have any liability to you or any other persons resulting from your use.

MHWirth consists of many legally independent entities, constituting their own separate identities. MHWirth is used as the common brand or trade mark for most of these entities. In this presentation we may sometimes use “MHWirth”, “we” or “us” when we refer to MHWirth companies in general or where no useful purpose is served by identifying any particular MHWirth company.

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mhwirth.com

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Generic Proactive Maintenance

Generic model from literature (e.g. Muller et al. 2008)

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Proactive Maintenance and OODAIn ProaSense

ObserveSense –

Proactivemonitoring of real time data

Decide – based on predictionsOrient – detect

a deviation and predict future

system performance

ACT –(i) Action taken at the

operational level (since this is the maintenance

process);(ii) Provide feedback to the

strategic processes of the organisation.

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• Data analytics for Condition Monitoring (CM) and Condition Based Maintenance (CBM) purposes can roughly be divided into four steps:– Data storage– Data preparation/ pre-processing/ concentration– Data processing– Decision making

• Each step of the cycle has to be configured to perform effectively to result in a reliable CBM system. The next slides will review the level of complexity of the process developing such systems in more detail.

Layers of ComplexityData analytics

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• Ensure that relevant parameters are stored (iterative process).• Configure data resolution per parameter to be sufficient to make use

of the time series without storing excessive information.• Nature of each parameter to be considered (Slow or rapid, high or low

dynamic range, …). • Define relevant context parameter from non-hardware sensors.

Data Storage

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• Decide which time periods are of most interest for a specific case.• Define logic to isolate these periods of interest and configure the

processing infrastructure accordingly.• Define required variables relevant to include for the periods of interest

to prepare for subsequent steps.

Preparation/Preprocessing/Concentration

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• Define input parameters, configuration of algorithm steps including necessary interim storage of results and finally the output parameters.

• Define trending requirements of the output parameter(s) • Define realistic thresholds value(s) to compare the output parameters

towards • Examine the possibility to launch more advanced mathematical or

physical models or methods to improve the results or the interpretation.

Data Processing

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• Define the range of preventive or corrective actions that are relevant for the specific case.

• Define rules for when to act (thresholds or degradation)• Define context data which can improve the confident in the decision

findings (and move to step one)• Configure possible optimization rules for which preventive or corrective

actions is most suitable at what time.• Define who is relevant to notify, when and how?

Decision Making

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Motivation: ReusabilityExample: Esper Event Processing Language

insert into Filtered select value, timestamp, type, location from Sensor1

insert into Enrichedselect a.value, b.value, compute(a.type, b.type, timestamp) as enrichedDatafrom Filtered.win:time(30 min)

insert into SomethingHappens select a.value, b.value, a.variableTypefrom pattern [every a=Enriched -> b=Enriched whereb.value > a.value * 120 where timer:within(20 secs)];

Sensor #1

Filter bythreshold value

Enrich withcontextualknowledge

Performpattern

detection

DecisionManagement

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Motivation: ReusabilityExample: Sensor failure, required modifications

insert into FilteredS2 selectobservation, timestamp, sensorId, lat, lng from Sensor2

insert into FilteredS2 select a.observation, b.observation, a.sensorIdfrom pattern [every a=FilteredS2 -> b=FilteredS2where b.value > a.value * 120 wheretimer:within(30 secs)];

Sensor #2

Filter bythreshold value

Performpattern

detection

DecisionManagement

Steps required- register new event types- pattern adaptations

Reusing patterns in case of replacement of sensors or required adaptations of patterns requireshigh manual effort

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Motivation: Technical HeterogeneityAbstract view: Event Processing Network

Sensor EPA EPA EPA EPA

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