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predictive maintenance advanced solution

The document provides a comprehensive overview of implementing predictive maintenance within organizations, emphasizing its importance and cost-effectiveness. It outlines the predictive maintenance process, which includes data collection, analysis, and operationalization, while also addressing the various benefits and challenges associated with it. The content aims to equip practitioners with the foundational knowledge and analytics skills necessary to effectively utilize predictive maintenance strategies.

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© 2 0 1 9 S P L U N K I N C . © 2 0 1 9 S P L U N K I N C . Implementing Predictive Maintenance Young Cho, Sr IoT Practitioner Sept 8, 2019 | Ver 1.0
During the courseofthis presentation,we maymake forward‐lookingstatementsregarding future eventsorplans ofthe company.Wecautionyouthat suchstatementsreflectour currentexpectationsand estimatesbasedonfactorscurrentlyknownto us and that actual eventsorresultsmaydiffermaterially. Theforward-lookingstatementsmadeinthe this presentationare beingmadeas of the time and date ofits live presentation.Ifreviewedafter its live presentation,itmaynot containcurrentoraccurateinformation.Wedo notassume any obligationto updateanyforward‐lookingstatementsmadeherein. Inaddition,any informationaboutourroadmapoutlinesourgeneralproductdirectionand is subjectto changeatany time withoutnotice.Itis forinformationalpurposesonly,and shall notbe incorporatedinto anycontractorothercommitment.Splunkundertakesno obligation eitherto developthe featuresorfunctionalitiesdescribedorto includeanysuchfeatureor functionalityina future release. Splunk,Splunk>,TurnDataInto Doing,TheEngineforMachineData,SplunkCloud,Splunk Lightand SPLare trademarksand registeredtrademarksof SplunkInc.inthe United States and othercountries.Allotherbrandnames,productnames,ortrademarksbelongto their respectiveowners.© 2019SplunkInc.Allrightsreserved. Forward- Looking Statements © 2 0 1 9 S P L U N K I N C .
© 2 0 1 9 S P L U N K I N C . Sr. IoT Practitioner | Splunk Inc Young Cho ▶ Firm believer of human creativity with data will conquer all problems in the world. ▶ Long computer and data career journey − Unix system admin during BBS time − PERLprogrammer– primarily fordata parsing − Data ware house designand developmentforTelcos − Big data consultant and architect − Splunk solutions architect, product marketing, IoT practitioner ▶ Amateur “Go” player − Favorite documentary, Netflix AlphaGo – Story of Deepmind challenging human “Go” champion
© 2 0 1 9 S P L U N K I N C . Welcome to “ImplementingPredictive Maintenance” Why do we care about Predictive Maintenance? Intro to the solutionsthat will make a big impact in your org. Get you pragmatic analytics skills for Predictive Maintenance. Why? What? How? Give you foundational knowledge to make you a PdM practitioner.
© 2 0 1 9 S P L U N K I N C . 1. Introduction to Predictive Maintenance 1
© 2 0 1 9 S P L U N K I N C . 1) MIT Sloan Review, “GE’s Big Bet on Data and Analytics” 2) ThomasNet, “Downtime Costs Auto Industry $22k/Minute” 3) TechTarget, “Predictive maintenance software points to machinery problems” “Predictive Maintenance is the Holy Grail of Industrial IoT” - Heather Ashton, manufacturing industry analyst at IDC3 $25M / Day Liquefied NaturalGas Platform $7M / Day Offshore OilPlatform $1.3M / Hour Auto Manufacturing 1 1 2 Equipment Downtime Costs Millions of $
© 2 0 1 9 S P L U N K I N C . ML Predictive Maintenance Condition-based Maintenance Preventative Maintenance Reactive Maintenance Scheduled and planned maintenance based on usage and time Fix when broken The global process industry loses $20 billion annually from unplanned downtime* *ARC Advisory Group O E E Proactive, Strategic Operations Real-time analytics and sensing insights to predict machine reliability Rules-based logic for sensor data Advanced machine learning and AI driven maintenance 80% of Industrial Operations Are Reactive
© 2 0 1 9 S P L U N K I N C . Current Maintenance Strategy / Methods How the world is maintained. Failure Reactive Maintenance Preventive Maintenance Scheduled Predictive Maintenance Optimal time Per asset Predicted Failure • Maintaining when the asset fails • Example : Light bulb • Applies to assets with minimal failure impact • Majority of operational asset can’t not do this. Costly downtime. • Maintaining at the regular schedule regardless of condition • Example : Engine Oil Change • Applies to majority of assets in operation. • High maintenance expense – time, availability, materials • Maintaining at the optimal time based on data and prediction • Example : Impact YOU can make! • Should be applied to all assets in operation. • High availability, cost savings, organizational efficiency
© 2 0 1 9 S P L U N K I N C . So, what is Predictive Maintenance? Every asset has different life expectancy – Different operating environments – Weatherconditions,Typesofworkload,Differentoperatingschedules& Frequency Data is the key to insights – contains similarity in how things will behave Key consideration element of Predictive Maintenance Time / Age Optimal time Per asset Predicted Failure Predicted Failure Predicted Failure Condition Indicator 100 Cycles 65 Cycles 145 Cycles
© 2 0 1 9 S P L U N K I N C . Intro to the Predictive Maintenance business case Splunk App – Designed for you to learn and experiment predictive maintenance • IncludesJet Enginesdatasetfrom Nasa • Key analyticstechniqueson how to understand and design your analysis model • Step-by-step guides on using Splunk for predictive maintenance analysis + algorithm creation. Jet engine use-case : Splunk Essentials for Predictive Maintenance App
© 2 0 1 9 S P L U N K I N C . Predictive Maintenance And Business Operations Introduces challenges and opportunities
© 2 0 1 9 S P L U N K I N C . Critical Business Implication It’s not as simples as as we may think it could be A SafetyThreshold • Fine balancebetween efficiency and safety • Each day of downtimecould cost up to $1 million USD for a jumbo jetliner.
© 2 0 1 9 S P L U N K I N C . Let’s look at what the data look like? unit_cycle sname_Bleed_Enthalp y sname_Bypass_R atio sname_Corr_Cor e_Speed sname_Corr_Fan _Speed sname_Fuel_Flo w_Ratio sname_HPC_Outl et_Temp sname_HPT_Cool ant_Bleed sname_LPC_Outl et_Temp sname_LPT_Outl et_Temp sname_Phys_Cor e_Speed sname_Phys_Fan _Speed sname_Static_HP C_Outlet_Pres sname_Total_HP C_Outlet_Pres 1 392 8.4195 8138.62 2388.02 521.66 1589.7 39.06 641.82 1400.6 9046.19 2388.06 47.47 554.36 2 392 8.4318 8131.49 2388.07 522.28 1591.82 39 642.15 1403.14 9044.07 2388.04 47.49 553.75 3 390 8.4178 8133.23 2388.03 522.42 1587.99 38.95 642.35 1404.2 9052.94 2388.08 47.27 554.26 4 392 8.3682 8133.83 2388.08 522.86 1582.79 38.88 642.35 1401.87 9049.48 2388.11 47.13 554.45 5 393 8.4294 8133.8 2388.04 522.19 1582.85 38.9 642.37 1406.22 9055.15 2388.06 47.28 554 6 391 8.4108 8132.85 2388.03 521.68 1584.47 38.98 642.1 1398.37 9049.68 2388.02 47.16 554.67 7 392 8.3974 8132.32 2388.03 522.32 1592.32 39.1 642.48 1397.77 9059.13 2388.02 47.36 554.34 8 391 8.4076 8131.07 2388.03 522.47 1582.96 38.97 642.56 1400.97 9040.8 2388 47.24 553.85 9 392 8.3728 8125.69 2388.05 521.79 1590.98 39.05 642.12 1394.8 9046.46 2388.05 47.29 553.69 10 393 8.4286 8129.38 2388.06 521.79 1591.24 38.95 641.71 1400.46 9051.7 2388.05 47.03 553.59 11 392 8.434 8140.58 2388.01 521.4 1581.75 38.94 642.28 1400.64 9049.61 2388.05 47.15 554.54 12 391 8.3938 8134.25 2388.02 521.8 1583.41 39.06 642.06 1400.15 9049.37 2388.09 47.18 554.52 13 393 8.4152 8128.1 2388.08 521.85 1582.19 38.93 643.07 1400.83 9046.82 2388.12 47.38 553.44 14 393 8.3964 8134.43 2388 521.67 1592.95 39.18 642.35 1399.16 9047.37 2388.09 47.44 554.48 15 391 8.4199 8127.56 2388.08 522.5 1583.82 38.99 642.43 1402.13 9052.22 2388.11 47.3 553.64 16 392 8.3936 8136.11 2388.07 521.49 1587.98 38.97 642.13 1404.5 9049.34 2388.05 47.24 553.94
© 2 0 1 9 S P L U N K I N C . Let’s look at what the data look like? Different assets with different degradation cycles A full life-cycle of a engine lasted 500+ cycles A full life-cycle of another engine Only lasted 135cycles
© 2 0 1 9 S P L U N K I N C . What are we trying to accomplish? Big difference in length of life
© 2 0 1 9 S P L U N K I N C . Predictive Maintenance Solution Demonstration 1
© 2 0 1 9 S P L U N K I N C . Demo
© 2 0 1 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
© 2 0 1 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
© 2 0 1 9 S P L U N K I N C .
© 2 0 1 9 S P L U N K I N C . Predictive Maintenance Development Process 0
© 2 0 1 9 S P L U N K I N C . Predictive Maintenance Analytics Process STAGE 1 : Data collection and ingestion focuses on how you can easily use Splunk software to collect,store,and structure asset metrics (datasetfrom airplane jet engines included). STAGE 2 : Data exploration covers key methods for pre-processing and exploring data to help you understand the type of data in use and the characteristics of the dataset, which is crucial for getting the desired outcome. Predictive maintenance analytics methodology: Essential predictive analytics knowledge STAGE 3 : Analysis teaches you the 3 key analysis options (Anomaly Detection, Unsupervised Learning, Supervised Learning) when doing predictive maintenance analysis. STAGE 4 : Operationalization teaches you how to apply the analytics model to a broader implementation, and how to create reports and alerts for operational actions.
© 2 0 1 9 S P L U N K I N C . 1. Getting Data In
© 2 0 1 9 S P L U N K I N C . Splunk for All of Enterprise Asset Data Universal data platform for events and metrics
© 2 0 1 9 S P L U N K I N C . How your industrial data is collected Different protocols and collection methods supported
© 2 0 1 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
© 2 0 1 9 S P L U N K I N C . 2. Explore Data / Feature Engineering
© 2 0 1 9 S P L U N K I N C . Understand how the asset operate Turbo Jet Engines has 3 major components Speed values Temperature values Flow / Bypass ratios Bleed values Pressure values
© 2 0 1 9 S P L U N K I N C . Data Normalization Method Prepares the data to create analytics model
© 2 0 1 9 S P L U N K I N C . Event Windowing Captures a full comparable operational cycle of an asset
© 2 0 1 9 S P L U N K I N C . Condition Feature Selection Understanding what information correlates with condition
© 2 0 1 9 S P L U N K I N C . Feature Profile for an Asset Visual inspection and understand of asset characteristics Speed values Temperature Values Flow / Bypass ratios Bleed values
© 2 0 1 9 S P L U N K I N C . Feature Engineering Different types of condition indicators • Time based features • Frequency based features Creating new features from statistics – Best condition indicators
© 2 0 1 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
© 2 0 1 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
© 2 0 1 9 S P L U N K I N C . 3. Analysis
© 2 0 1 9 S P L U N K I N C . Predictive Maintenance Analysis Domain Two main approaches to Predictive Maintenance analysis • Anomaly Detection • Remaining Useful Life • Unsupervised Learning • Supervised Learning Statistical Analysis Approach Machine Learning Approach
© 2 0 1 9 S P L U N K I N C . Analysis Techniques & Approach How to decide how to approach the problem
© 2 0 1 9 S P L U N K I N C . 1. Understand standard deviation 2. Value distribution analysis 3. Defining threshold based on standard deviation Statistical Method Anomaly Detection
© 2 0 1 9 S P L U N K I N C . Analysis – Anomaly Detection Conditional value “Standard Deviation”
© 2 0 1 9 S P L U N K I N C . Analysis – Anomaly Detection Conditional values distribution
© 2 0 1 9 S P L U N K I N C . Analysis – Anomaly Detection Standard Deviation Thresholding
© 2 0 1 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
© 2 0 1 9 S P L U N K I N C . 1. Asset degradation model 2. Calculating remaining useful life 3. Defining condition indicators 4. Optimizing remaining useful life using similarity model Statistical Method Remaining Useful Life Analysis
© 2 0 1 9 S P L U N K I N C . Analysis – Remaining Useful Life What is remaining Useful Life? Failure Condition ASSET DEGRADATION MODEL Condition Indicator Remaining Useful Life Time / Age Current Condition
© 2 0 1 9 S P L U N K I N C . Analysis – Remaining Useful Life How to calculate remaining useful life? Failure Condition Condition Indicator Remaining Useful Life Time / Age Current Condition ASSET DEGRADATION MODEL • Known “End Of Life” distribution • Calculate conditionindicator
© 2 0 1 9 S P L U N K I N C . Analysis – Remaining Useful Life Calculating remaining useful life using similarity model Failure Condition Condition Indicator Remaining Useful Life Time / Age Current Condition ASSET DEGRADATION MODEL • Known “End Of Life” distribution • Calculate condition indicator
© 2 0 1 9 S P L U N K I N C . Data reduction and combination technique Enhancing representation of condition indicators Physical Fan Speed LPC Outlet Temperature Pre-processing Data • Standard Scaling • Average 2 features into 1 2 ▶ Condition Indicator Model 3 Enhanced Condition Indicator | eval condition_ind = ((SS_sname_LPC_Outlet_Temp + SS_sname_Phys_Fan_Speed ) / 2) * -1 ▶ Pick feature as a reduction 1
© 2 0 1 9 S P L U N K I N C . What is remaining useful life? • Input as current cycle(Age) and conditional indicator value • Group similar historical models (nearest) • Statistics of known “End of Life” – mean of known eol. How to calculate the remaininglife based on historical similarity model Asset Degradation Model Failure Condition Condition Indicator Remaining Useful Life Time / Age Current Condition Expected End Of Life 200 270 End Of Life Range 235
© 2 0 1 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
© 2 0 1 9 S P L U N K I N C . 1. Principal ComponentAnalysis 2. Clustering 3. Various visual data explorations Machine Learning Approach Unsupervised Machine Learning
© 2 0 1 9 S P L U N K I N C . Analysis – Unsupervised Machine Learning
© 2 0 1 9 S P L U N K I N C . Analysis – Unsupervised Machine Learning
© 2 0 1 9 S P L U N K I N C . Analysis – Unsupervised Machine Learning
© 2 0 1 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
© 2 0 1 9 S P L U N K I N C . 1. Convey meaning and be inspirational with your message when possible 2. Use powerful imagery to support your point 3. Use animation to support your message, not just to entertain the audience Machine Learning Approach Supervised Learning
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning Supervised learning analytics process components
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning Applying training the “Good” model approach When used?: • Training a “Good” model,when there are other variations (Often many) of patternsnot well known. • So, anything otherthan a very specific “good”pattern as bad. Pick out rest of non-good pattern as bad.
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning Applying training the “Good” model approach
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning Applying training the “Bad” model approach When used? : • Training a “Bad” model, when a bad pattern is certain and known,alsolimited “bad” possibility. • So, pick specific bad pattern from the data where there maybe multiple “good” patterns.
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning Applying training the “Bad” model approach
© 2 0 1 9 S P L U N K I N C . When used? : • Training a model with multipleconditions as differentlabels.(0,1,2,3) • Define multiple different patternsbased on severity or differentsymptom. Analysis – Supervised Machine Learning Examples : • Based on condition (Good → Warning → Bad) • Based on differentsymptom (Compressorfailure vs fan failure)
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning Define the range of time series data, then label “state=” with numeric value. Training a multiple conditions example
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning Training a multiple conditions pre-training model labeling
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning Different machine learning algorithm for trial and error
© 2 0 1 9 S P L U N K I N C . Analysis – Supervised Machine Learning Analyzing accuracy and precision of trained machine learning model Accuracy and precision verification Confusion Matrix
© 2 0 1 9 S P L U N K I N C .
© 2 0 1 9 S P L U N K I N C . 4. Apply
© 2 0 1 9 S P L U N K I N C . Apply – Dashboard & Reports Immediately operationalize Splunk predictive maintenance Creating Dashboard : Predictive Maintenance Dashboard • Use the analyzed prediction,create dashboard to show the status of engines in different state. • Use Splunk Dashboard Examples App to customize visualization and various user inputs and controls. Creating Reports : Using Splunk Enterprise • Watch training video on "How to create reports in Splunk" • Splunk documentation on "How to create reports"
© 2 0 1 9 S P L U N K I N C . Apply – Generate Alerts & Connected Experience Notify your maintenancecrew in real-time CreatingAlerts in Splunk • Watch training video on "How to create alerts in Splunk" • Splunk documentation on "How to define alerts” Splunk Connected Experience : • LaunchSplunkMobile to empower your field teams – to access analytics and alertsin mobile phones • Augmented reality to make analyticsavailable to all field crew in a easy to see augmented realityon top of your assets.
© 2 0 1 9 S P L U N K I N C . Apply – Automate and Control Automate and Orchestrate your operations • Process multiple conditions • Apply logic to decide • Even involve human Confirmation • Interact with any assets with APIs • Fully automated response to intergrate Automation using Phantom • Define full automation using Phantomand integrate with your legacy assets
© 2 0 1 9 S P L U N K I N C . 5. Wrap Up
© 2 0 1 9 S P L U N K I N C . Let’s get you hands dirty! Methodology Tools / Technology Passion This is where the subtitle goes Now you know key analytics techniques to accomplishpredictive maintenance Splunk Essentials for Predictive Maintenance + ML Toolkit + Basic Statistics It’s time for a big impact - reach out to all the resource– Vertical Team Specialists+ Practitioners
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predictive maintenance advanced solution

  • 1.
    © 2 01 9 S P L U N K I N C . © 2 0 1 9 S P L U N K I N C . Implementing Predictive Maintenance Young Cho, Sr IoT Practitioner Sept 8, 2019 | Ver 1.0
  • 2.
    During the courseofthispresentation,we maymake forward‐lookingstatementsregarding future eventsorplans ofthe company.Wecautionyouthat suchstatementsreflectour currentexpectationsand estimatesbasedonfactorscurrentlyknownto us and that actual eventsorresultsmaydiffermaterially. Theforward-lookingstatementsmadeinthe this presentationare beingmadeas of the time and date ofits live presentation.Ifreviewedafter its live presentation,itmaynot containcurrentoraccurateinformation.Wedo notassume any obligationto updateanyforward‐lookingstatementsmadeherein. Inaddition,any informationaboutourroadmapoutlinesourgeneralproductdirectionand is subjectto changeatany time withoutnotice.Itis forinformationalpurposesonly,and shall notbe incorporatedinto anycontractorothercommitment.Splunkundertakesno obligation eitherto developthe featuresorfunctionalitiesdescribedorto includeanysuchfeatureor functionalityina future release. Splunk,Splunk>,TurnDataInto Doing,TheEngineforMachineData,SplunkCloud,Splunk Lightand SPLare trademarksand registeredtrademarksof SplunkInc.inthe United States and othercountries.Allotherbrandnames,productnames,ortrademarksbelongto their respectiveowners.© 2019SplunkInc.Allrightsreserved. Forward- Looking Statements © 2 0 1 9 S P L U N K I N C .
  • 3.
    © 2 01 9 S P L U N K I N C . Sr. IoT Practitioner | Splunk Inc Young Cho ▶ Firm believer of human creativity with data will conquer all problems in the world. ▶ Long computer and data career journey − Unix system admin during BBS time − PERLprogrammer– primarily fordata parsing − Data ware house designand developmentforTelcos − Big data consultant and architect − Splunk solutions architect, product marketing, IoT practitioner ▶ Amateur “Go” player − Favorite documentary, Netflix AlphaGo – Story of Deepmind challenging human “Go” champion
  • 4.
    © 2 01 9 S P L U N K I N C . Welcome to “ImplementingPredictive Maintenance” Why do we care about Predictive Maintenance? Intro to the solutionsthat will make a big impact in your org. Get you pragmatic analytics skills for Predictive Maintenance. Why? What? How? Give you foundational knowledge to make you a PdM practitioner.
  • 5.
    © 2 01 9 S P L U N K I N C . 1. Introduction to Predictive Maintenance 1
  • 6.
    © 2 01 9 S P L U N K I N C . 1) MIT Sloan Review, “GE’s Big Bet on Data and Analytics” 2) ThomasNet, “Downtime Costs Auto Industry $22k/Minute” 3) TechTarget, “Predictive maintenance software points to machinery problems” “Predictive Maintenance is the Holy Grail of Industrial IoT” - Heather Ashton, manufacturing industry analyst at IDC3 $25M / Day Liquefied NaturalGas Platform $7M / Day Offshore OilPlatform $1.3M / Hour Auto Manufacturing 1 1 2 Equipment Downtime Costs Millions of $
  • 7.
    © 2 01 9 S P L U N K I N C . ML Predictive Maintenance Condition-based Maintenance Preventative Maintenance Reactive Maintenance Scheduled and planned maintenance based on usage and time Fix when broken The global process industry loses $20 billion annually from unplanned downtime* *ARC Advisory Group O E E Proactive, Strategic Operations Real-time analytics and sensing insights to predict machine reliability Rules-based logic for sensor data Advanced machine learning and AI driven maintenance 80% of Industrial Operations Are Reactive
  • 8.
    © 2 01 9 S P L U N K I N C . Current Maintenance Strategy / Methods How the world is maintained. Failure Reactive Maintenance Preventive Maintenance Scheduled Predictive Maintenance Optimal time Per asset Predicted Failure • Maintaining when the asset fails • Example : Light bulb • Applies to assets with minimal failure impact • Majority of operational asset can’t not do this. Costly downtime. • Maintaining at the regular schedule regardless of condition • Example : Engine Oil Change • Applies to majority of assets in operation. • High maintenance expense – time, availability, materials • Maintaining at the optimal time based on data and prediction • Example : Impact YOU can make! • Should be applied to all assets in operation. • High availability, cost savings, organizational efficiency
  • 9.
    © 2 01 9 S P L U N K I N C . So, what is Predictive Maintenance? Every asset has different life expectancy – Different operating environments – Weatherconditions,Typesofworkload,Differentoperatingschedules& Frequency Data is the key to insights – contains similarity in how things will behave Key consideration element of Predictive Maintenance Time / Age Optimal time Per asset Predicted Failure Predicted Failure Predicted Failure Condition Indicator 100 Cycles 65 Cycles 145 Cycles
  • 10.
    © 2 01 9 S P L U N K I N C . Intro to the Predictive Maintenance business case Splunk App – Designed for you to learn and experiment predictive maintenance • IncludesJet Enginesdatasetfrom Nasa • Key analyticstechniqueson how to understand and design your analysis model • Step-by-step guides on using Splunk for predictive maintenance analysis + algorithm creation. Jet engine use-case : Splunk Essentials for Predictive Maintenance App
  • 11.
    © 2 01 9 S P L U N K I N C . Predictive Maintenance And Business Operations Introduces challenges and opportunities
  • 12.
    © 2 01 9 S P L U N K I N C . Critical Business Implication It’s not as simples as as we may think it could be A SafetyThreshold • Fine balancebetween efficiency and safety • Each day of downtimecould cost up to $1 million USD for a jumbo jetliner.
  • 13.
    © 2 01 9 S P L U N K I N C . Let’s look at what the data look like? unit_cycle sname_Bleed_Enthalp y sname_Bypass_R atio sname_Corr_Cor e_Speed sname_Corr_Fan _Speed sname_Fuel_Flo w_Ratio sname_HPC_Outl et_Temp sname_HPT_Cool ant_Bleed sname_LPC_Outl et_Temp sname_LPT_Outl et_Temp sname_Phys_Cor e_Speed sname_Phys_Fan _Speed sname_Static_HP C_Outlet_Pres sname_Total_HP C_Outlet_Pres 1 392 8.4195 8138.62 2388.02 521.66 1589.7 39.06 641.82 1400.6 9046.19 2388.06 47.47 554.36 2 392 8.4318 8131.49 2388.07 522.28 1591.82 39 642.15 1403.14 9044.07 2388.04 47.49 553.75 3 390 8.4178 8133.23 2388.03 522.42 1587.99 38.95 642.35 1404.2 9052.94 2388.08 47.27 554.26 4 392 8.3682 8133.83 2388.08 522.86 1582.79 38.88 642.35 1401.87 9049.48 2388.11 47.13 554.45 5 393 8.4294 8133.8 2388.04 522.19 1582.85 38.9 642.37 1406.22 9055.15 2388.06 47.28 554 6 391 8.4108 8132.85 2388.03 521.68 1584.47 38.98 642.1 1398.37 9049.68 2388.02 47.16 554.67 7 392 8.3974 8132.32 2388.03 522.32 1592.32 39.1 642.48 1397.77 9059.13 2388.02 47.36 554.34 8 391 8.4076 8131.07 2388.03 522.47 1582.96 38.97 642.56 1400.97 9040.8 2388 47.24 553.85 9 392 8.3728 8125.69 2388.05 521.79 1590.98 39.05 642.12 1394.8 9046.46 2388.05 47.29 553.69 10 393 8.4286 8129.38 2388.06 521.79 1591.24 38.95 641.71 1400.46 9051.7 2388.05 47.03 553.59 11 392 8.434 8140.58 2388.01 521.4 1581.75 38.94 642.28 1400.64 9049.61 2388.05 47.15 554.54 12 391 8.3938 8134.25 2388.02 521.8 1583.41 39.06 642.06 1400.15 9049.37 2388.09 47.18 554.52 13 393 8.4152 8128.1 2388.08 521.85 1582.19 38.93 643.07 1400.83 9046.82 2388.12 47.38 553.44 14 393 8.3964 8134.43 2388 521.67 1592.95 39.18 642.35 1399.16 9047.37 2388.09 47.44 554.48 15 391 8.4199 8127.56 2388.08 522.5 1583.82 38.99 642.43 1402.13 9052.22 2388.11 47.3 553.64 16 392 8.3936 8136.11 2388.07 521.49 1587.98 38.97 642.13 1404.5 9049.34 2388.05 47.24 553.94
  • 14.
    © 2 01 9 S P L U N K I N C . Let’s look at what the data look like? Different assets with different degradation cycles A full life-cycle of a engine lasted 500+ cycles A full life-cycle of another engine Only lasted 135cycles
  • 15.
    © 2 01 9 S P L U N K I N C . What are we trying to accomplish? Big difference in length of life
  • 16.
    © 2 01 9 S P L U N K I N C . Predictive Maintenance Solution Demonstration 1
  • 17.
    © 2 01 9 S P L U N K I N C . Demo
  • 18.
    © 2 01 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
  • 19.
    © 2 01 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
  • 20.
    © 2 01 9 S P L U N K I N C .
  • 21.
    © 2 01 9 S P L U N K I N C . Predictive Maintenance Development Process 0
  • 22.
    © 2 01 9 S P L U N K I N C . Predictive Maintenance Analytics Process STAGE 1 : Data collection and ingestion focuses on how you can easily use Splunk software to collect,store,and structure asset metrics (datasetfrom airplane jet engines included). STAGE 2 : Data exploration covers key methods for pre-processing and exploring data to help you understand the type of data in use and the characteristics of the dataset, which is crucial for getting the desired outcome. Predictive maintenance analytics methodology: Essential predictive analytics knowledge STAGE 3 : Analysis teaches you the 3 key analysis options (Anomaly Detection, Unsupervised Learning, Supervised Learning) when doing predictive maintenance analysis. STAGE 4 : Operationalization teaches you how to apply the analytics model to a broader implementation, and how to create reports and alerts for operational actions.
  • 23.
    © 2 01 9 S P L U N K I N C . 1. Getting Data In
  • 24.
    © 2 01 9 S P L U N K I N C . Splunk for All of Enterprise Asset Data Universal data platform for events and metrics
  • 25.
    © 2 01 9 S P L U N K I N C . How your industrial data is collected Different protocols and collection methods supported
  • 26.
    © 2 01 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
  • 27.
    © 2 01 9 S P L U N K I N C . 2. Explore Data / Feature Engineering
  • 28.
    © 2 01 9 S P L U N K I N C . Understand how the asset operate Turbo Jet Engines has 3 major components Speed values Temperature values Flow / Bypass ratios Bleed values Pressure values
  • 29.
    © 2 01 9 S P L U N K I N C . Data Normalization Method Prepares the data to create analytics model
  • 30.
    © 2 01 9 S P L U N K I N C . Event Windowing Captures a full comparable operational cycle of an asset
  • 31.
    © 2 01 9 S P L U N K I N C . Condition Feature Selection Understanding what information correlates with condition
  • 32.
    © 2 01 9 S P L U N K I N C . Feature Profile for an Asset Visual inspection and understand of asset characteristics Speed values Temperature Values Flow / Bypass ratios Bleed values
  • 33.
    © 2 01 9 S P L U N K I N C . Feature Engineering Different types of condition indicators • Time based features • Frequency based features Creating new features from statistics – Best condition indicators
  • 34.
    © 2 01 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
  • 35.
    © 2 01 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
  • 36.
    © 2 01 9 S P L U N K I N C . 3. Analysis
  • 37.
    © 2 01 9 S P L U N K I N C . Predictive Maintenance Analysis Domain Two main approaches to Predictive Maintenance analysis • Anomaly Detection • Remaining Useful Life • Unsupervised Learning • Supervised Learning Statistical Analysis Approach Machine Learning Approach
  • 38.
    © 2 01 9 S P L U N K I N C . Analysis Techniques & Approach How to decide how to approach the problem
  • 39.
    © 2 01 9 S P L U N K I N C . 1. Understand standard deviation 2. Value distribution analysis 3. Defining threshold based on standard deviation Statistical Method Anomaly Detection
  • 40.
    © 2 01 9 S P L U N K I N C . Analysis – Anomaly Detection Conditional value “Standard Deviation”
  • 41.
    © 2 01 9 S P L U N K I N C . Analysis – Anomaly Detection Conditional values distribution
  • 42.
    © 2 01 9 S P L U N K I N C . Analysis – Anomaly Detection Standard Deviation Thresholding
  • 43.
    © 2 01 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
  • 44.
    © 2 01 9 S P L U N K I N C . 1. Asset degradation model 2. Calculating remaining useful life 3. Defining condition indicators 4. Optimizing remaining useful life using similarity model Statistical Method Remaining Useful Life Analysis
  • 45.
    © 2 01 9 S P L U N K I N C . Analysis – Remaining Useful Life What is remaining Useful Life? Failure Condition ASSET DEGRADATION MODEL Condition Indicator Remaining Useful Life Time / Age Current Condition
  • 46.
    © 2 01 9 S P L U N K I N C . Analysis – Remaining Useful Life How to calculate remaining useful life? Failure Condition Condition Indicator Remaining Useful Life Time / Age Current Condition ASSET DEGRADATION MODEL • Known “End Of Life” distribution • Calculate conditionindicator
  • 47.
    © 2 01 9 S P L U N K I N C . Analysis – Remaining Useful Life Calculating remaining useful life using similarity model Failure Condition Condition Indicator Remaining Useful Life Time / Age Current Condition ASSET DEGRADATION MODEL • Known “End Of Life” distribution • Calculate condition indicator
  • 48.
    © 2 01 9 S P L U N K I N C . Data reduction and combination technique Enhancing representation of condition indicators Physical Fan Speed LPC Outlet Temperature Pre-processing Data • Standard Scaling • Average 2 features into 1 2 ▶ Condition Indicator Model 3 Enhanced Condition Indicator | eval condition_ind = ((SS_sname_LPC_Outlet_Temp + SS_sname_Phys_Fan_Speed ) / 2) * -1 ▶ Pick feature as a reduction 1
  • 49.
    © 2 01 9 S P L U N K I N C . What is remaining useful life? • Input as current cycle(Age) and conditional indicator value • Group similar historical models (nearest) • Statistics of known “End of Life” – mean of known eol. How to calculate the remaininglife based on historical similarity model Asset Degradation Model Failure Condition Condition Indicator Remaining Useful Life Time / Age Current Condition Expected End Of Life 200 270 End Of Life Range 235
  • 50.
    © 2 01 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
  • 51.
    © 2 01 9 S P L U N K I N C . 1. Principal ComponentAnalysis 2. Clustering 3. Various visual data explorations Machine Learning Approach Unsupervised Machine Learning
  • 52.
    © 2 01 9 S P L U N K I N C . Analysis – Unsupervised Machine Learning
  • 53.
    © 2 01 9 S P L U N K I N C . Analysis – Unsupervised Machine Learning
  • 54.
    © 2 01 9 S P L U N K I N C . Analysis – Unsupervised Machine Learning
  • 55.
    © 2 01 9 S P L U N K I N C . Insert your own screenshot here. For best results, use an image sized at 1450 x 850
  • 56.
    © 2 01 9 S P L U N K I N C . 1. Convey meaning and be inspirational with your message when possible 2. Use powerful imagery to support your point 3. Use animation to support your message, not just to entertain the audience Machine Learning Approach Supervised Learning
  • 57.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning
  • 58.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning
  • 59.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning Supervised learning analytics process components
  • 60.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning Applying training the “Good” model approach When used?: • Training a “Good” model,when there are other variations (Often many) of patternsnot well known. • So, anything otherthan a very specific “good”pattern as bad. Pick out rest of non-good pattern as bad.
  • 61.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning Applying training the “Good” model approach
  • 62.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning Applying training the “Bad” model approach When used? : • Training a “Bad” model, when a bad pattern is certain and known,alsolimited “bad” possibility. • So, pick specific bad pattern from the data where there maybe multiple “good” patterns.
  • 63.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning Applying training the “Bad” model approach
  • 64.
    © 2 01 9 S P L U N K I N C . When used? : • Training a model with multipleconditions as differentlabels.(0,1,2,3) • Define multiple different patternsbased on severity or differentsymptom. Analysis – Supervised Machine Learning Examples : • Based on condition (Good → Warning → Bad) • Based on differentsymptom (Compressorfailure vs fan failure)
  • 65.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning
  • 66.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning Define the range of time series data, then label “state=” with numeric value. Training a multiple conditions example
  • 67.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning Training a multiple conditions pre-training model labeling
  • 68.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning Different machine learning algorithm for trial and error
  • 69.
    © 2 01 9 S P L U N K I N C . Analysis – Supervised Machine Learning Analyzing accuracy and precision of trained machine learning model Accuracy and precision verification Confusion Matrix
  • 70.
    © 2 01 9 S P L U N K I N C .
  • 71.
    © 2 01 9 S P L U N K I N C . 4. Apply
  • 72.
    © 2 01 9 S P L U N K I N C . Apply – Dashboard & Reports Immediately operationalize Splunk predictive maintenance Creating Dashboard : Predictive Maintenance Dashboard • Use the analyzed prediction,create dashboard to show the status of engines in different state. • Use Splunk Dashboard Examples App to customize visualization and various user inputs and controls. Creating Reports : Using Splunk Enterprise • Watch training video on "How to create reports in Splunk" • Splunk documentation on "How to create reports"
  • 73.
    © 2 01 9 S P L U N K I N C . Apply – Generate Alerts & Connected Experience Notify your maintenancecrew in real-time CreatingAlerts in Splunk • Watch training video on "How to create alerts in Splunk" • Splunk documentation on "How to define alerts” Splunk Connected Experience : • LaunchSplunkMobile to empower your field teams – to access analytics and alertsin mobile phones • Augmented reality to make analyticsavailable to all field crew in a easy to see augmented realityon top of your assets.
  • 74.
    © 2 01 9 S P L U N K I N C . Apply – Automate and Control Automate and Orchestrate your operations • Process multiple conditions • Apply logic to decide • Even involve human Confirmation • Interact with any assets with APIs • Fully automated response to intergrate Automation using Phantom • Define full automation using Phantomand integrate with your legacy assets
  • 75.
    © 2 01 9 S P L U N K I N C . 5. Wrap Up
  • 76.
    © 2 01 9 S P L U N K I N C . Let’s get you hands dirty! Methodology Tools / Technology Passion This is where the subtitle goes Now you know key analytics techniques to accomplishpredictive maintenance Splunk Essentials for Predictive Maintenance + ML Toolkit + Basic Statistics It’s time for a big impact - reach out to all the resource– Vertical Team Specialists+ Practitioners
  • 77.
    RATE THIS SESSION Goto the .conf19 mobile app to © 2 0 1 9 S P L U N K I N C . You! Thank