This document discusses using Azure Machine Learning and stream processing to enable predictive maintenance for aircraft engines. It describes a use case of predicting whether a device will fail within the next two weeks using real-time sensor data streams. It then outlines the challenges of stream processing and applying machine learning to streaming data. The proposed solution architecture involves using Event Hub for data ingestion, Stream Analytics for stream processing and aggregations, Machine Learning for model training and predictions, and DocumentDB for storing prediction results. It provides examples of the Stream Analytics and Machine Learning workflows used to enable predictive maintenance from real-time sensor data streams.

1. V like Velocity, Predicting in
Real-Time with Azure ML
Barbara Fusinska
@BasiaFusinska

2. About me
Programmer
Machine Learning
Data Solutions Architect
@BasiaFusinska

3. Agenda
• Aircraft Predictive maintenance
• Use Case description
• Stream processing challenges
• Machine Learning Model
• Solution Architecture
• Azure Architecture
• Stream processing
• Applying intelligence to streams

4. Aerospace Predictive
Maintenance
Engines Sensors Analysis

5. Will the device fail in 2
weeks, 4 weeks or will
not fail during this time?
Use Case
When will the device
break?
Will the device fail in the
next 2 weeks?

6. Velocity
How is it different?

7. Big Data 4Vs
• Untrusted
• Uncleansed
• Speed of
generation
• Rate of analysis
• Unstructured
• Semi-structured
• Structured
• Click stream
• Active/passive sensors
• Logs
• Events
• Speech
• Social media
• Traditional
Volume Variety
VeracityVelocity

8. Big Data & Azure
Intelligence
Dashboards &
Visualizations
Information
Management
Big Data Stores Machine Learning
and Analytics
CortanaEvent Hub
HDInsight
(Hadoop and
Spark)
Stream Analytics
Data
Sources
Apps
Sensors
and
devices
Data Intelligence Action
People
Automated
Systems
Apps
Web
Mobile
Bots
Bot
Framework
SQL Data
WarehouseData Catalog
Data Lake
Analytics
Data Factory
Machine Learning
Data Lake Store
Blob Store
Cognitive Services
Power BI

9. Batch vs Real-time

10. Stream processing challenges
• Data Ingestion
• Stream Processing
• Applying intelligence to the
stream
• Aggregations
• Data sink

11. Machine Learning
Machine Learning
Model
Model Training
Published
Machine Learning
Model
Prediction
Training data
Publish model
Test stream
Scores

12. Machine Learning workflow
Data
preparation
Data split
Machine Learning
algorithm
Trained model Score
Clean
data
Training
data
Test data

13. Data -> Predictive model -> Operational web API in minutes
APIML STUDIO

14. Azure Machine
Learning Studio
Income prediction
Demo

15. Will the device break in the next 2 weeks?
0
1
2
3
4
5
6
cycle 11 cycle 12 cycle 13 cycle 14 cycle 15
Sensor reads
s1 s2 s3
Device
37
Cycle s1 ... s21 failed
1 518.67 23.419 0
2 518.67 23.4236 0
3 518.67 23.3442 0
...
134 518.67 23.1295 0
135 518.67 23.4085 1
(s, avg, stdev)

16. https://gallery.cortanaintelligence.com/SolutionTemplate/Predictive-Maintenance-for-Aerospace-1
https://gallery.cortanaintelligence.com/Collection/Predictive-Maintenance-Template-3

20. Aircraft
Predictive
Maintenance
Use Case

21. Solution Architecture
Stream AnalyticsEvent Hub DocumentDB
Machine Learning Web AppML Model Training
Training data
Retrain

22. Sensors data
simulation
Data generator

24. Event Hub configuration
Data Ingestion

30. DocumentDB configuration
Storing Maintenance Predictions

34. Stream analytics configuration
Maintenance prediction

41. Stream Analytics – Source and Sink
SELECT
DeviceId, result.ScoredLabels,
cycle, seeting1, …, s1, …, s21,
a1, …, a21, sd1, …, sd21
INTO maintenance
FROM predict

43. Aggregation in Stream Analytics
SELECT avg(s1) as a1
FROM CallStream
GROUP BY id
, TumblingWindow(minute, 1)
TIMESTAMP BY T

44. Stream Analytics – Aggregations
WITH aggregate AS (
SELECT
id,
avg(s1) AS a1, …, avg(s21) AS a21,
stdev(s1) AS sd1, …, stdev(s21) AS sd21
FROM CallStream AS s
GROUP BY id, TumblingWindow(minute, 1)
)

45. Join in Stream Analytics
SELECT c.s1, a.a1
FROM CallStream c
JOIN aggregate a
ON c.id = a.id
and DATEDIFF(minute, c, a)
BETWEEN 0 and 1

46. Stream Analytics – Join aggregations
WITH predict AS (
SELECT
s.id, s.cycle, s.seeting1, …, s.s1, …, s.s21,
a.a1, …, a.a21, a.sd1, …, a.sd21,
predmain(s.cycle, s.seeting1, …, s.s1, …, s.s21,
a.a1, …, a.a21, a.sd1, …, a.sd21) AS result
FROM CallStream AS s
join aggregate AS a
ON s.id = a.id
AND DATEDIFF(minute, s, a) BETWEEN 0 AND 1
)

47. Monitoring Dashboard
Reading Predictions from DocumentDB

49. Retraining scenario
Demo

52. Scaling web
services
• Azure Classic Portal
• 20-200 concurrent requests
• Azure ML optimization
• Multiple endpoints for the
same web service
• New web services
• Production web services
• Pricing Plans
• 1,000 – 50,000,000 request
per month

54. Keep in touch
BarbaraFusinska.com
@BasiaFusinska