22nd October 2019

Navigating the data maze - tools & techniques for successful prognostics – Part II

1

Agenda

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1. What’s ‘Not’ Predictive Maintenance

2. Advanced Analytics – Predictive Diagnostics Quadrants

3. Evolution of Predictive Diagnostics

4. Case Study – Predicting RUL

5. Redefining the status quo – Predictive Diagnostics

6. Takeaways

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What’s ‘Not’ Predictive Maintenance

Dashboarding

Expensive

No PM on new Automobiles

It’s Complex

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Advanced Analytics – Predictive Diagnostics Quadrants

Predict failure within a time-window▪ Whether the component will fail in next 1 hr.?▪ Solved using: Classification based Deep learning and Machine Learning

Algorithms.

Predict Remaining Useful Life (RUL)▪ How much time left before the failure happens?▪ Solved using: Regression based Deep learning / Machine Learning

Algorithms.

Flagging Anomalous behavior▪ Is this behavior normal?▪ Solved using: Classification based Deep learning and Machine Learning

Algorithms.

Failure Probability Over Time▪ Can my past learnings on failure be saved over time?▪ Solved using: Bayesian Networks and Markov Chain Models.

“Predict the future failure point of a machine”

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Evolution of Predictive Diagnostics

Threshold Based• Single Value• Correlation

Learning Based Algorithms• Pattern Recognition• Machine Learning

Advanced Self Learning Algorithms• Automated Domain Knowhow• Real-time Prognosis• Deep Learning

Limitations:o Emphasis on few

parameterso Limited to domain.o Sensitive to noise

Limitations:o Limited to domain.o Explicit Feature

Engineeringo Limited to Linear

relationships

Case Study: Predict Remaining Useful Life

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Case Study I - Predicting Remaining Useful Life (RUL) of a spark plug

Worn-out NewCustomer:

“Spark plugs are failing invariably; can you

estimate when would they fail?”

Data:

• 2 Years time-series data for 8 Gensets

• Data sampled at 2Hz; 120GB worth of Data

• Logs when the spark plugs were replaced.

Challenges:

• Gensets with variable specifications.

• Different operating Conditions.

• Diverse make of spark-plugs across time.

13001000

20001700

1400 1350

1900

1100

0

500

1000

1500

2000

2500

3000

3500

1 2 3 4 5 6 7 8

Genset-SparkPlugs Theoretical Life

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Data Aggregation

Will my Business

need output every

second?

NO

• Aggregate over sliding time window – min, max, mean, std-dev, median.

• Right Aggregation strategy –No loss in data granularity.

• Reduces the processing data by 85% i.e. to ~10GB

• Big-Data Processing tools –Apache PySpark

Aggregating Data – Predict RUL

1

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Data Exploration and Validation

Data Exploration & Validation

2

Not the right problem to solve!

Can the provided

data solve the use case?

NO

YES Replacements

Domain Validation

?NO YES

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Modelling

3ML Ensemble Regression Models

• RUL linearly decreases with time – Assumption• Predictor is RUL

Feature Snippet:

• Secondary voltage standard deviation• Mixture temperature mean• Mixture pressure A mean• Mixture pressure B mean• Electrical Power mean

and a few many more…

P / T ratio (Derived)

Input by Domain experts

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Evaluation

Validation

Critical input from Domain

Experts

Feature Importance

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Redefining the status quo

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Encapsulate the Domain

Advantages:

• Neural Networks are nota black box anymore!

• 50% Time Reduction in Problem Solving

• Learnings are back propagated!

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An Ongoing Case Study – Towards Perfection…

• Recurrent Neural Network with perfected Architecture

• No explicit domain feature engineering –Fully Encapsulated

• Ability to predict fluctuating values.

Initial ResultsActual

Predicted

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Takeaways

✓ Evolution of Diagnostics

✓ Case study positively impacting a Business

✓ Encapsulating the Domain – The Future