1. IoT and ML for Predictive Maintenance
By : Mukul Joshi & Mitra Daram Nitor Infotech Pvt. Ltd. Global Summit 2018

7. Evolution of Maintenance
“Change is inevitable – except from a vending machine” – Robert Gallagher Since the invention of first machine, we had to deal with maintenance and repair. Thankfully we have evolved with the approach.
Reactive Maintenance
Use of Vibration analysis
Infrared imaging
Oil analysis etc. To do predictive maintenance
Fix it when it breaks
Preventive Maintenance
Using IoT to gather data
Using ML to understand behavior/patterns
ML models are used for predictive maintenance
Not to wait till things break.
Periodic checks.
Creates Unnecessary work
Labor intensive
Issues arise between schedules
Predictive Maintenance
IoT based Predictive Maintenance

8. Business Problems in predictive maintenance
Detect anomalies in equipment or system performance or functionality.
Predict whether an asset may fail in the near future.
Estimate the remaining useful life of an asset.
Identify the main causes of failure of an asset.
Identify what maintenance actions need to be done, by when, on an asset

10. Qualification of problem
Target or an outcome to predict. Clear path of action to prevent failures when detected. Problem has to be predictive in nature.
Record of the operational history of the equipment with both good & bad outcomes… Error reports, maintenance logs, repair logs etc. should be available.
The recorded history should be reflected in relevant data - sufficient enough quality to support the use case.
Finally, the business should have domain experts who have a clear understanding of the problem.

11. Sample use cases
Aviation : Flight delay and cancellation, Engine part failure
Finance : ATM failure
Energy : Wind turbine failure, circuit breaker failure
Transportation and logistics: wheel failure, subway train door failure

12. Five steps for predictive maintenance solution5
Learn &
Act Smart 1
Identify Assets 2
Gather Data 3
Design
Predictive
Model 4
Manage
Work

13. Data Preparation Data should be Relevant, Sufficient & Quality
Relevant data sources are
Failure history
Maintenance/repair history
Machine operating conditions
Equipment metadata
There are two major types of these
Temporal data
Static data

14. Static vs. Temporal treatment
Visualize the data as table of records with row as training instance. The columns as “predictor” features. Final column as “target”
Maintenance records: Asset identifier, maintenance action, time etc.
Failure records: Failure reasons as error codes. Correlation between conditions and codes.
Machine & Operation metadata: Merged together to associate with assets.
Missing value handling and normalization
Temporal data is divided into units. Time unit for asset offers distinct information.

15. Feature engineering Tumbling Aggregates Rolling Aggregates
Feature is a “predictive” attribute such as temperature, pressure, vibration etc. To predict the future, decide how much can be looked back. (Lag)
Rolling Aggregates
Tumbling Aggregates

16. Examples Flight delay: count of error codes over the last day/week.
Aircraft engine part failure: rolling means, standard deviation, and sum over the past day, week etc. This metric should be determined along with the business domain expert.
ATM failures: rolling means, median, range, standard deviations, count of outliers beyond three standard deviations
Subway train door failures: Count of events over past day, week, two weeks etc.
Circuit breaker failures: Failure counts over past week, year, three years etc.

17. Modeling : Binary Classification
Probability that piece of equipment fails within future time period. (Future horizon period X)
About to fail as label 1 (for period)

18. Examples
Flight delays: X may be chosen as 1 day, to predict delays in the next 24 hours. Then all flights that are within 24 hours before failures are labeled as 1.
ATM cash dispense failures: A goal may be to determine failure probability of a transaction in the next one hour. In that case, all transactions that happened within the past hour of the failure are labeled as 1. To predict failure probability over the next N currency notes dispensed, all notes dispensed within the last N notes of a failure are labeled as 1.
Train door failures: X may be chosen as two days.
Wind turbine failures: X may be chosen as two months.

19. Regression Compute Remaining Useful Life (RUL)
Assets that have not failed can’t be used in modelling
Survival Analysis

20. Multi class classification
Predict two outcomes
Range of time to failure
Likelihood of failure
What is the probability that asset will fail in next nZ units of time?

21. Multi class classification
What is the probability that the asset will fail in next X unit due to root cause/problem Pi?

22. Training, Validation and Testing
Failures are rare
Under-representation in training data
Cost sensitive learning : High cost to mis-classification of minority class.
Sampling techniques

23. Transformer Monitoring
Nitor case study
Transformer Monitoring

24. Transformer Monitoring
Business Situation
18,000+ Transformers
More than 250 Models / Variants
Located Across the Globe (Different Geo Locations with different conditions)
An Average of 10+ sensors capturing various readings (Oil temperature, Oil Volume, Load etc.)
Challenges
Data Generated from the transformers are in different frequencies
Collection of Historic Data including Events (Failure, Breakdown, Down time etc.) and Data Quality
Domain Expertise
Integration with External Data and Macro Environmental Factors
Data Storage, Processing and Archival Mechanism
Validating the Model

25. Solution Approach Test Models Training Data Device 1 Device 2
Step 1: Model Creation & Training with Historic Dataset
Azure Batch AI 1 2
Test Models
Training Data
ML Models
Job1
Job2
Job3
Anomaly detection & Failure Prediction algorithms
Step 2: Production Stage
Save model per device
Device 1
Real time Data Ingestion
Device 2
Time series data OR
Aggregated data at time interval
Azure Batch AI
Azure IoT Hub
Device 3
Stream Analytics
Device 4
Other Related External Data Sources
Azure SQL
Visualization Layer
Data Collection
Device n
React
Predict
Should be available over
internet to connect

26. Possible Impact & Benefits
Business Impact
Anticipating at least 30% reduction in Maintenance Costs
Transformers Productivity in terms of uptime / availability can go up by 10%
Number of Breakdowns / Total Failures can be avoided in at least 50% of the cases
Optimization of Spare Parts Inventory
Reduce Fuel Costs and Extend Life
Worker Safety can be improved
Maintenance Actions can be triggered based on the threat level rather than ‘Routine Checks’

27. Transformer Performance Dashboard

28. Thank you
Mukul Joshi & Mitra Daram
Nitor Infotech Pvt. Ltd.