The Internet of Things Wind Turbine Predictive Analytics. Fluitec Wind s Tribo-Analytics System Predicting Time-to-Failure

Transcription

1 The Internet of Things Wind Turbine Predictive Analytics Fluitec Wind s Tribo-Analytics System Predicting Time-to-Failure

2 Big Data and Tribo-Analytics Today we will see how Fluitec solved real-world challenges Data wrangling on a Big Data scale Predictive modeling and data mining of complex, heterogeneous data sources Utilization of cloud resources Integration of Big Data tools such as Hadoop with MySQL and Java Use of network connected devices to manage complex machinery

3 Internet of Things Devices embedded with electronics, software and sensors... network-connected... enabling communication and data transfer... incorporating automated analytics

4 Tribo-Analytics and IoT Automated feeds from sensors on wind turbine components Incorporated into central repository Automated analytics produce warnings and recommendations WTG 89172: Add 1000 mg P to GB oil WTG 00811: Yaw motor likely to fail < 3 mos

5 M&S Consulting Big Data Architecture Machine Learning Data Wrangling (ETL) Hybrid Cloud

6 Fluitec International

7 Fluitec Fluitec International - Tribology experts Fluitec Wind - Decreasing the cost of wind energy through advanced data analytics

8 Fluitec Wind

9 Fluitec Wind Tribo-Analytics SaaS platform Help turbine operators utilize existing data to reduce costs and guide decision making Aggregates data from thousands of turbines globally Recommend Maintenance Plans Identify At-Risk Components

10 Value Proposition for Customers Using data that is already being collected, predictive analytics can be used to identify which components will fail and when Customers can then either take preventive action or at least plan for replacement Enables condition-based oil changes

11 Why Wind Turbines? Expensive to build $2 MM/MW On-shore, $6 MM/MW Off-shore Expensive to maintain $50,000 / WTG per year $7,500 / WTG oil change (every 6 years) $100,000 Gearbox replacement cost Often remote = Difficult logistics Huge amounts of data already being collected

12 Often Remote

13 Big and Expensive

14 Really Big...

15 Exposed to the Elements

16 Maintenance can be Tricky

17 Distracted Staff

18 Not So Simple - Lots of Connected "Things"

19 Predict Component Failure Initial Focus on Gearboxes Very Expensive Difficult to Replace One of the most frequent causes of failure in wind turbines

20 Gearboxes

21 Damaged Bearing

22 Gear Micropitting

23 Types of Data Used Sensors on gearbox major components Ambient conditions Oil analysis Technician s reports

24 The Data 5,000 WTGs (Wind Turbine Generators) 30,000 oil samples Make, model, rated capacity, etc. Billions of rows of sensor data (to start) Bearing temperature Shaft RPM Vibration alerts Many more...

25 Collecting and Preparing Data Collect data historical + ongoing feeds Traditional ETL on low-volume data MDM is important Preprocessed high-volume data ETL Derived data (statistics)

26 High-Volume Data Flow Continuous File Processing Secure Landing Zone For Data Uploads From Customers File-level Validation Cataloging Transfer to HDFS Repository Copy to Backup Storage Hadoop Repository Cleansing Transformation Pre- Aggregation Calculation of Statistics Data Feed Object Catalog and Lineage Repository Cheap Storage (Raw File Repository)

27 Complete Data Flow (generic) Data Sources (examples) Real-Time Routing Data Cleansing & Restructuring Pre- Processing Delivery (PCs, tablets, phones) Internal financial data in Oracle Machine Sensor data (flat files) Internal CRM data in SQL Server Semi-structured (customer clicks on website) Unstructured (technician incident report) One or more applications which determine which ETL path the data will follow (batch, real-time, or both). The data may be sent to more than one downstream system. For example, fastmoving financial markets data may be streamed to a real-time analytics app for immediate use but would also be sent to the data warehouse for more in-depth analysis and inclusion in the historical data repository. Batch Batch High-volume ETL in Hadoop Cluster Low/Mediumvolume ETL and MDM in Relational Database Real-time Pre-processing and light-weight validation Reporting and Analytics Processing (traditional BI - calculation of KPIs, summaries, dashboards) Advanced Analytics (machine learning, predictive analytics, NLP, etc.) Real-Time Analytics Decision Makers (Dashboards Self-service BI Download to Excel) Analysts (Data Mining, R,SAS, SPSS, Predictive Models) Dashboards, Alerts, Reports Reports, Alerts

28 Hybrid Cloud Architecture Secure Customer Upload AWS Customer #1 Hadoop Data Repository + MDM (copy) MySQL Pre-Processed Data For Model Ingestion Cataloging Data Lineage MDM On-Premise Secure Customer Upload Hadoop Development Customer #2

29 Data Collection and Pre-Processing Systems Landing Zone - hybrid Internal and AWS-based Linux servers Secure, encrypted transfer to Hadoop cluster in cloud Pre-Processing in Hadoop Pre-Aggregated Values and Statistics Sqoop ed to MySQL

30 Machine Learning Methodology Identify patterns associated with gearbox failure modes using training set (create model) Validate model using N-fold cross-validation Using model based on training set, predict TTF for: New wind turbines Existing turbines using updated data

31 Anomaly Detection Sensor Data

32 Oil Anomalies

33 Oil Contamination

34 Side Note... Big Data tools are not just for big data Powerful, open-source software Data Mining Exploring data in new ways Create KPIs for automated monitoring

35 Predictive Model Selection Ran millions of scenarios Used automated model generator 128 scenarios run in parallel on multiple cloud servers Cloud servers shutdown when not running scenarios = $0 cost

36 Challenges and Lessons Learned Data Quality Semantics Data Volume Identifying Distinct Modes of Failure Identifying Failures Validate Peer Groups Should be clear that model works or doesn t

37 Key Features Repository for Data Mining Predictive Model Data Catalog Model Logs Automated Model Generation and Assessment No Lost Information

38 Key Technologies MySQL Hadoop Java R Python

39 Led to New Lines of Business Breeze Oil Never change the oil But need monitoring to establish credibility Condition-based oil changes With monitoring and Fluitec expert analysis, only change oil when needed Additional additives sales Top up additives instead of complete oil change

40 M&S Consulting Process and Technology Consulting National clientele

41 Q&A Questions welcomed...