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Demystifying Cognitive Approaches to Predictive Maintenance Part 1
- 1. Demystifying Cognitive Approaches to Predictive Maintenance: Part1 DataRPM webinar
- 2. DataRPM webinar Demystifying Cognitive Approaches to Predictive Maintenance: Part1 Anita Raj Aditya Murukutla
- 3. How to solve the current gaps in machine data analysis How you can build a scalable and repeatable approach to harness the trillions of data points generated by sensors How cognitive approaches to data science can be a game-changer for Industry 4.0 Use Case: Enabling cognitive predictive maintenance for a Fortune 10 Industrial Manufacturer 1 2 3 4 Anita Raj Principal Growth Hacker | DataRPM @anitarajsekaran anita@datarpm.com Aditya Murukutla Senior Product Manager | DataRPM @aditya_narayana aditya@datarpm.com HOST AGENDA SPEAKER
- 4. Welcome!Welcome! This webinar is best appreciated if you are associated with: You want to do Predictive Maintenance on Industrial Assets You deal with large amounts of Sensor Data Industrial asset-centric organizations Industrial asset-intensive organizations
- 5. The Gap in the Current State of IIoT Data Analysis Low compute power restricts the size of data to be analyzed Scalability Data scientist has to build new models for every new use-case Repeatability Models cannot be used in production systems Operationalize
- 6. KEY DATA SCIENCE CHALLENGES Static, Manual Modeling Fails for the IIOT © 2017 DataRPM – Proprietary and Confidential 6 Only 20% of Asset Failures are Common & Predictable 80% of Asset Failures are Random & specific to an Individual Assets Modeling today takes: Samples of Data from Samples of Sensors from Samples of Assets Builds Generalized Models & Extrapolates to the entire Asset Population Leading to Poor Results Need Individual Predictive Models for every Asset to get at that Unknown 80%
- 7. KEY DATA SCIENCE CHALLENGES For PdM For IIoT © 2017 DataRPM – Proprietary and Confidential 7 Massive Unlabeled Data in the IIoT Minimal Labeled Training Data Thus Supervised-Learning not possible Use Unsupervised-Learning of Unlabeled Data first to Generate Labeled Data for Supervised-Learning Predictions are only useful ahead of time Recommendations only possible with timely Predictions
- 8. KEY DATA SCIENCE CHALLENGES For PdM For IIoT © 2017 DataRPM – Proprietary and Confidential 8 Need Adaptive & Online Model Generation for Continuous Learning Static Models becomes Obsolete really fast Results in Poor Predictions in actual Operating Environments Failures are Rare Events & caused by Dynamic Changes
- 9. What is a Scalable Approach? Distributed Architecture In-Memory Algorithms Expansion Horizontally Open Source Key Takeaway: The technologies and methodologies used will grow to the needs of the projects long-term. Large distributed Compute platform Run Machine Learning in Memory to optimize speed on a distributed Infrastructure Architected so we can add new nodes on the fly All technologies used are open source
- 10. What is a Repeatable Approach? Repeatable Data Product Data Frame ML Recipes API & Interfaces Ingestion Consumption Modeling Storage Customization possible with ability to easily do smaller tweaks in interface since all code is open and recipe parameters are modifiable Client has ability to plugin their own algorithms (as long as scala/spark code) into the Recipes
- 11. How can you Operationalize ? Prescriptive Recommendations Influencing Factors PredictionSegmentation Divides the population into groups for analysis Finds factors in groups that are similar and/or different for an outcome Builds a model that can classify new data points based on similar factors Clustering Distributions Rules Association Rules Frequent Patterns Correlation Classification Regression Recommendations for specific segments based on predictions & influencing factors Collaborative Filtering Content Filtering
- 12. What DataRPM offers Consumption APIs Micro Apps Framework Security Data Management Data Sync Data Lake Metadata Machine Learning and Analytics Spark Engine Workflow Builder Data Science Recipes Meta Learning Natural Language Visualization IIoT Sensors Data Enterprise Asset Management Systems RDBMS Data sources Hadoop Insights App Discovery App Admin App PdM Apps
- 13. F Use Case|Predictive Maintenance for Industrial IOT MANUAL ANALYSIS CHALLENGE 2 sensors at-a-time only 15 minutes of sensor data merely a tiny Data Sample for Model Predictions of Future Failures & Recommend Actions USE CASE Identify the indicators of failures for washing machines Each sensor records multiple data points in millisec range 75 Unique Sensor Recordings 011101 11001 0101 0110 110 DATA OVERLOAD 6+ Months for Team of Data Scientists to process Resulted in poor prediction with lots of false positives COGNIT IVE DATA SCIENCE SOLUTION ALL sensors in parallel Months of sensor data used to train Data Models accurately < 2 Days Highly Accurate Prediction Model Automated building of thousands of models in parallel to deliver the optimal model OTHER PdM EXAMPLES Predict failure of robotic and other machinery in assembly lines and identify internal and external factors that cause failures Predict failures of set top boxes and identify the internal and ext ernal causes of failures and recommend actions FORTUNE 10 MANUFACTURER
- 14. Use Case|Predictive Maintenance for Industrial IOT
- 15. © 2017 DataRPM – Proprietary and Confidential 15 IF YOU’RE INTERESTED IN LEARNING MORE: Aditya@datarpm.com Anita@datarpm.com THANKYOU