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IoT-Enabled Predictive Maintenance

IoT is reshaping the manufacturing and industrial processes, effectively changing the paradigm from one of repair and replace to more of predict and prevent. Using data streaming from connected equipment and machinery, organizations can now monitor the health of their assets and effectively predict when and how an asset might fail. However, without the right data management strategy and tools, investments in IoT can yield limited results. Join Cloudera and Tata Consultancy Services (TCS) for a joint webinar to learn more about how organizations are using advanced analytics and machine learning to drive IoT enabled predictive maintenance.

IoT-Enabled Predictive Maintenance

  1. 1. 1© Cloudera, Inc. All rights reserved. How Leading Organizations are using Data Analytics and Machine Learning to enable IoT IoT Enabled Predictive Maintenance
  2. 2. 2© Cloudera, Inc. All rights reserved. Your Speakers for Today… Vijay Raja Solutions Marketing Lead, IoT Cloudera Chellury (Ram) Sastry IoT Practice Lead Tata Consultancy Services Vinod J Nair Solution Delivery Manager, IoT Tata Consultancy Services
  3. 3. 3© Cloudera, Inc. All rights reserved.
  4. 4. 4© Cloudera, Inc. All rights reserved. The Need for Predictive Maintenance Unplanned outages due to equipment failure, leading to increased costs & lowered productivity Limited to no analytic modeling capabilities to predict and prevent issues before they impact Unplanned Outages Fundamentally Reactive Performing preventative maintenance regardless of condition can lead to unnecessary costs and delays Unnecessary Work
  5. 5. 5© Cloudera, Inc. All rights reserved. The Foundation of PredictiveMaintenance • Sensors continuously monitor critical attributes – temp, pressure, vibrations/sec etc. • Use machine learning to detect anomalies or patterns that are indicative of failure • Real-time alerts • Early intervention as soon as initial signs of failure are detected
  6. 6. 6© Cloudera, Inc. All rights reserved. The Challenge – Managing Machine Data Data can come from a variety of “siloed” sources • Massive volumes of intermittent data streams • Generated from a variety of data sources • Predominantly time-series • Can come in streams (real-time) or batches • Diverse data structures and schemas • Some of it may be perishable Combining sensor data with contextual data is the key to value creation from IoT
  7. 7. 7© Cloudera, Inc. All rights reserved. Predictive Maintenance – The Data Value Chain Data Sources Data Ingest & Storage Data Processing Machine Learning Analytics/ BI • Diverse Data Sources • Intermittent data streams • Batch/ Streaming • Diverse schema/ formats • Real-Time Data Ingest • Big-Data platform • Unlimited Storage • Low Cost/ TB • In-memory processing • Real-Time Processing • Data enrichment • Data contextualization • Machine Learning libraries • Unsupervised M/L • Supervised M/L • Iterative data modeling • Data Visualization • Data Science • SQL Analytics • Search
  8. 8. 8© Cloudera, Inc. All rights reserved. Cloudera Enterprise – The Data Mgmt. Platform for IoT Sensors/ IoT Data Sources Enterprise Data Sources External Sources BI Solutions Real-Time Apps Search Data Science Workbench SQL Machine Learning Data Center Cloud Sensor/ IoT Data IoT Gateway • Data Storage • Data Processing • Machine Learning • Real-time Analytics OPERATIONS Cloudera Manager Cloudera Director DATA MANAGEMENT Cloudera Navigator Encrypt and KeyTrustee Optimizer BATCH Sqoop REAL-TIME Kafka, Flume PROCESS, ANALYZE, SERVE UNIFIED SERVICES RESOURCE MANAGEMENT YARN SECURITY Sentry, RecordService FILESYSTEM HDFS RELATIONAL Kudu NoSQL HBase STORE INTEGRATE BATCH Spark, Hive, Pig MapReduce STREAM Spark SQL Impala SEARCH Solr SDK Partners
  9. 9. 9© Cloudera, Inc. All rights reserved. The Cloudera Platform for IoT – Data Mgmt. Value Chain Data Sources Data Ingest Data Storage & Processing Serving, Analytics & Machine Learning ENTERPRISE DATA HUB Apache Kafka Stream or batch ingestion of IoT data Apache Sqoop Ingestion of data from relational sources Apache Hadoop Storage (HDFS) & deep batch processing Apache Kudu Storage & serving for fast changing data Apache HBase NoSQL data store for real time applications Apache Impala MPP SQL for fast analytics Cloudera Search Real time searchConnected Things/ Data Sources Structured Data Sources Security, Scalability & Easy Management Deployment Flexibility: Datacenter Cloud Apache Spark Stream & iterative processing, ML
  10. 10. 10© Cloudera, Inc. All rights reserved. 1Predictive Maintenance – Business Impacts Using real-time data to predict and prevent breakdowns can reduce downtime by 50 percent 50% Repair & Replace Predict & Prevent Predictive maintenance could reduce maintenance costs of factory equipment by 10 to 40 percent. 40% Reduced Downtime - Source: McKinsey Analysis Reduced Costs
  11. 11. 11© Cloudera, Inc. All rights reserved. IoT Enabled Predictive Maintenance – Customer Examples Automotive Energy & Utilities Industrial Robotics • Monitor the performance of 300,000+ trucks in real-time • Predict failures, improve uptime and vehicle performance • Lower maintenance costs and vehicle downtimes by 40% • Continuous monitoring of industrial-grade turbines for hydroelectric power stations • Gather, store and analyze noise levels from turbines for anomaly detection • Predict failures in advance to perform required maintenance • Gather, store and analyze sensor data from 10,000 robots in real-time • Zero Down Time (ZDT) application • Diagnostic solution predicts potential failures and alerts the operators in advance
  12. 12. 12© Cloudera, Inc. All rights reserved. TCS - Cloudera Confidential & Proprietary TCS Sensor Data Analytics IoT Framework (SDAF)
  13. 13. 13© Cloudera, Inc. All rights reserved. TCS - Cloudera Confidential & Proprietary Adopting an integrated approach with best of breed partner products to provide a strong foundation and helping enterprises unlock compelling business value TCS Sensor Data Analytics IoT Framework Enterprise Data Hub powered by Apache Hadoop Intel Atlantic Ridge architecture McAfee Security Key highlights • Certified on Cloudera platform • Built using open standards • Proven scalability and security • Seamless and securely connect devices • Batch and real time processing of data • Deep Analytical Insights IoT Partnership – Intel, Cloudera & TCS
  14. 14. 14© Cloudera, Inc. All rights reserved. TCS - Cloudera Confidential & Proprietary TCS Sensor Data Analytics IoT Framework (SDAF) A highly customizable Big Data based IoT framework to collect, transform, store and analyze variety of sensor data. Highlights:  Certified by Cloudera  Ready to use configurable framework across Edge, Cloud and Analytics functions.  Comprehensive Device Management & Sensor Data Stream Handling capabilities.  End-to end Security to ensure secure transfer, storage and access to data.  Analytics of Things – Edge Analytics, Real-time Analytics and Deep Learning at scale.  Built using open standards ensuring zero vendor lock-in.  Strong collaboration and partnerships with leading technology vendors, platform providers and IoT consortiums. TCS SENSOR DATA ANALYTICS IoT Framework
  15. 15. 15 SDAF on ClouderaSensors Grove,Siemens,EMX,Honeywell Devices Communications Acquire & Analyze Smart Applications ControllerGateway Intel(MoonIsland),ARM Enterprise Intelligent Application Web Portals Mobile Apps BI Tools Protocols Wi-Fi,BLE,ZigBee,Modbus,NFC,3GPP,LTE BusLayer MQTT,CoAP,HTTP,TCP,UDP Real-time Processing Acquire Deep Analytics Failure Prediction Anomaly Detection Time Series Analysis Real-time AnalyticsParse & Enrich Cloudera Hadoop Distribution TCS Sensor Data Analytics IoT Framework (SDAF)Partner Ecosystem Frequent Pattern Mining Segmentation Demand Forecasting Device Management Zero Touch Device Commissioning Remote Terminal GUI Health Monitoring Alerts & Notifications SDAF Agent
  16. 16. SDAF - Feature Overview STREAMING DATA INGESTION DATA ANALYTICS DEVICE DATA ACQUISITION SECURITY DATA PROCESSING & STORAGE DEVICE MANAGEMENT • Zero-touch Device Commissioning • Health Monitoring • Remote Terminal Access • Software and Firmware Upgrades • Support for industry standard protocols • Data streaming using CoAP and MQTT protocols • QoS support • Support for protocols such as Modbus, Bacnet, Zigbee, BLE, etc • High-throughput framework • Data Enrichment • Complex Event Detection • Sliding Window based Correlation • Real-time Alerts & Notification • Data Formatting & Filtering • Storage into HDFS, HBase and Hive • Data Governance and Meta data Management • Geo-Fencing and Geo Spatial Correlation • Edge Analytics • Real-time analytics on data streams • Data Profiling framework for time series data • Scalable Machine Learning library for predictive analytics • Comprehensive Edge Security • Audit Logs • Role based Access control on data • Kerberos integration for authorization • HDFS Encryption
  17. 17. Analytical Models Warranty Estimation Model Segmentation Model Anomaly Detection • Interesting Pattern Detection • Peak Detection • Outlier sub-sequences • Time series classification Association/Rule Mining Telematics Model Clickstream Analytics and Recommendation Model Time Series Analysis Prediction Model Failure Probability & Time to Failure Frequent Pattern Mining and Apriori • Missing Trip Identification • Parking Location Identification • Route Matching • Acceleration, Braking & Cornering Scoring • Speeding Score • Trip Score, Daily Score and Overall Score • Score Projection Forecasting
  18. 18. 18© Cloudera, Inc. All rights reserved. Case Studies TCS - Cloudera Confidential & Proprietary
  19. 19. 19© Cloudera, Inc. All rights reserved. Solution Overview: Perform real-time monitoring of Port equipment, improve operational efficiency & facilitate Predictive Maintenance Technology: TCS SDAF components for capturing data from port equipment, store data on Amazon Web Services and Cloudera & uses SparkML Lib for running machine learning algorithms. Business Outcomes:  Increased Equipment availability  Reduced Operational costs Finnish Shipping and Port Equipment Manufacturer TCS - Cloudera Confidential & Proprietary
  20. 20. 20© Cloudera, Inc. All rights reserved. Largest Indian HVAC Manufacturer Solution Overview: Centralized monitoring of Chillers across different locations and proactive maintenance to avoid frequent failures and reduce maintenance cost. Technology: TCS SDAF for real-time data capture from Intel IoT gateways to a centralized Cloudera platform and SparkML Lib for running machine learning algorithms. Device Management features to remotely monitor, configure and manage Intel gateways. Business Outcomes:  New Revenue Models using Chiller Monitoring as a Service  Proactive customer support and service using predictive maintenance TCS - Cloudera Confidential & Proprietary
  21. 21. 21© Cloudera, Inc. All rights reserved. Demo – Integrated Maintenance Management TCS - Cloudera Confidential & Proprietary
  22. 22. 22© Cloudera, Inc. All rights reserved. Visualization/ Consumption Layer APIGatewayTools Data Scientists Business Analysts Asset Monitoring Portal Mobile Applications TCS Sensor Data Analytics IoT Framework (SDAF) Real-time Batch Real-time Layer Batch Layer Broker/Server FTP/ SFTP DB Connectors Data Ingestion Load balancer Data Management Sematic LayerStaging Area Raw Data Store Integrated Data Layer Complex Event Processing Parsing Alerts & Notifications Data Enrichment Data Transformations Data Aggregation Real-time Analytics Machine Learning OSI/PI Server Distributed Queue Maximo PLC/SCADA Cloud Predictive Maintenance For Fermenter Unit in Pharmaceutical Plant
  23. 23. 23© Cloudera, Inc. All rights reserved. Thank you

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