Anomalies in Oil Temperature Variations in a Tunnel Boring Machine, by Guillem Ràfales, Construction Management Product Leader at SENER, and Guillem Vidal, Machine Learning Engineer at BigML. *MLSEV 2020: Virtual Conference.

1. 2nd edition

2. #MLSEV 2
Tunnel Boring Machines
Oil Temperature Variations ML Analysis
Guillem Rafales Queralt
Infrastructures & Transport
Construction Manager Leader
SENER
Guillem Vidal
Machine Learning Engineer
BigML

3. #MLSEV 3
Tunnel Boring Machines

4. #MLSEV 4
SENER´s Experience

6. #MLSEV 6
SENER´s Experience

7. #MLSEV 7
SENER´s Experience

8. #MLSEV 8
TBM Description
Tunnel Boring Machines are those machines used to perform Rock
tunneling excavation by mechanical means. Close faced shielded
machines also permit the excavation in Soft Ground conditions.
There are two basic types of pressurized closed-face tunneling systems:
Slurry Tunneling machines and Earth Pressure Balance EPB machines.
Courtesy video of an EPB machine, from Herrenknecht
Ibérica S.A. and only for academic purposes.

9. #MLSEV 9
TBM Gear Oil Temperature
Main bearing of a TBM is the mechanical core of the machine which
enables TBM to turn cutter Head and transmits the torque of motorization
to terrain to be excavated.
The bearing maintains lubrication due of a full charge of gear oil of about
680 cSt viscosity. About 5000 liters of oil is necessary to full fit the main
bearing of a 12m Ø tunneling machine.
Oil analysis are performed to
monitor wear status and adequate
condition of the main bearing

10. #MLSEV 10
TBM External Factors Impact
External factors have a high impact in TBMs outcome, such as difficult
geological conditions, urban areas, logistic problems, economical, politic
matters and influent stake-holders.
Taegu Metro, Corea del Sur (2000)
Colonia Metro, Alemania (2009)

11. #MLSEV 11
Objective
Prepare a preliminary study about Tunnel Boring Machines gear oil
temperature variations. Analyze relationships between oil temperature
changes and the multiple TBM internal parameters. If possible, explore
relevant oil temperature variations predictions to anticipate related failures
Oil temperature being related to internal TBM matters, it has been picked
as the PoC analysis objective to avoid external impact complexity.
Oil temperature variations are slow and rare which makes the analysis a
challenge

12. #MLSEV 12
Data

13. #MLSEV 13
Provided Data
• 2GB of historic data from 1 tunnel have been provided
• Over 1700 DBF files each one representing a tunnel ring
• Each instance represents a TBM data measure with hundreds of
attributes at a given instant
• Measures are provided every 10 seconds
Discarded data:
• When the TBM is not advancing
• When gear oil temperature is under 46 degrees(C)

14. #MLSEV 14
Main Features
• Torque
• Speed
• Penetration
• Forces
• Pressure
• Liquid flows and volumes
• Chamber material measures
• Times and frequencies
• Amongst many others…

15. #MLSEV 15
Feature Engineering
Future gear oil temperature variations
• Tested with different intervals (3, 5, 10, 15, 30 and 60 minutes) to find
relevant variation causes as short as possible
• A boolean flag indicating 0.3 degrees (C) temperature raises in the
next 10 minutes has been kept
Sliding windows data for the past 5 minutes
• A 5 minutes past summary has been aggregated for overall features
including ranges, standard deviation and averages
Resulting dataset 120k rows, 5k temperature raises (4.3%), 171MB

16. #MLSEV 16
Data Insights

17. #MLSEV 17
Association Discovery
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
Thr Sally 6788 sign food 26339 51
An unsupervised algorithm that looks for coincidences in the data and returns
association rules with an antecedent, a consequent and several metrics. It is very useful
for data exploration amongst other goals. Example:
zip = 46140
amount < 100
Antecedent Consequent
Rules: {customer = Bob, account = 3421}
{class = gas}

18. #MLSEV 18
Association Discovery
Consequent: Temp_range_10m > 0.3 degrees (temperature range in the next 10m)

19. #MLSEV 19
Association Rule Example
Rule summary
• When:
• pushing force metric has been high for 5 minutes
• temperature has been to an average value for 5 minutes
• material is currently being cumulated inside the machine
• Then the oil gear temperature increases over 0.3 degrees during the next 10 minutes
Rule metrics
• 100% confidence (32 instances in 2 different rings)

20. #MLSEV 20
Data Exploration
• Multiple Association Discovery have been trained in the data exploration phase. In
each case results were shared with SENER experts to identify irrelevant and interesting
rules
• After several iterations Association Discovery attributes were sufficiently optimized to
produce interesting rules
• A final set of rules was sent to SENER in order to select the most potentially interesting
rules and proceed with specific analysis for each one
• In general a large amount of rules contained negative material deviation features
• Data observations showed material accumulations in the chamber were often tied to
temperature raises

21. #MLSEV 21
Data Visualization
Grafana screenshot showing a case where negative material deviation (bottom right)
happen together with gear oil temperature raise (top left)

22. #MLSEV 22
Anomaly Detection

23. #MLSEV
date customer account auth class zip amount
Mon Bob 3421 pin clothes 46140 135
Tue Bob 3421 sign food 46140 401
Tue Alice 2456 pin food 12222 234
Wed Sally 6788 pin gas 26339 94
Wed Bob 3421 pin tech 21350 2459
Wed Bob 3421 pin gas 46140 83
Thr Sally 6788 sign food 26339 51
23
Anomaly Detection
An unsupervised algorithm that looks for unusual instances in a dataset. Anomaly
detectors provide an anomaly score to each instance, the higher is the score the most
unusual is the instance. Example:
• Amount $2,459 is higher than all other transactions
• Only transaction
• In zip 21350
• For the purchase class “tech"

24. #MLSEV 24
Anomaly Detector
Features: Only material deviation variables

25. #MLSEV 25
Anomaly Detector Results
• Top anomalies often correspond to
temperature raises over 0.3 degrees
in the next 10 minutes
• Filtering anomaly scores over 50%
results into 4.5% of the original data
including 12.6% of the original
temperature raise instances
• Resulting dataset is more
balanced: 11.8% temperature
raises (orange data points in the
graph)

26. #MLSEV 26
Classification

27. #MLSEV 27
Classification
• After filtering high anomaly scores the dataset is more balanced and classification
makes sense with supervised learning. The goal is to predict whether the gear oil
temperature will raise considerably in the future 10 minutes
• Data has been split linearly to evaluate models:
• Training dataset 70%: 3687 rows, 273 with high temperature raise
• Test dataset 30%: 1804 rows, 377 with high temperature raise
• Feature engineering and feature selection has been performed resulting into a
subset of features including material deviation, oil temperature and some generic
parameters

28. #MLSEV 28
Decision Tree
Decision trees being simple and interpretable have been chosen as the initial
classification approach

29. #MLSEV 29
Decision Tree Evaluation
• Precision and Recall are 45.5% and
48.3% respectively. Not specially
high but still better than average.
This means predictions are
possible. Results are much better
than in preliminar tests
• Ideally current evaluation results
should be improved
• OptiML has been used to optimize
the algorithm and parameters choice

30. #MLSEV 30
OptiML Model Optimization
• An OptiML has been trained to
optimize ROC Area Under the Curve
metric using the same training and
test datasets
• OptiML resulting ROC AUC
measures appear much higher than
the decision tree ones, there is a
clear improvement
• Best results are achieved by decision
tree ensembles

31. #MLSEV 31
Ensemble Evaluation
• With a 39% probability threshold
precision and recall values 50.9%
and 69.5%
• This means model would be able to
predict half of the temperature raises
and 70% predictions would be
correct
• The Area Under the ROC Curve
(ROC AUC) is almost 80% which is
a good overall indicator for the model

32. #MLSEV 32
Summary

33. #MLSEV 33
Workflow
TUNNEL
DATA
SOURCE
ORIGINAL
DATASET
ADD
FEATURES
DATA
CLEANING
ASSOCIATION RULES
EXPLORATION
ML READY
DATASET
MATERIAL
DEVIATION
ANOMALY
DETECTOR
ANOMALY
SCORE >
0.5 FILTER
HIGH ANOMALY
SCORED
DATASET
TRAIN 70% TEST 30%
ENSEMBLE
PREDICT HIGH
TEMPERATURE
RAISE
EVALUATION
OPTIML MODEL
OPTIMIZATION

34. #MLSEV 34
Results
• An Anomaly Detector has isolated 12.6% of all temperature raise cases within a
smaller dataset (4.6% overall). 650 temperature raises were filtered into a smaller
dataset of 5501 instances
• Based on Recall in the evaluation, overall 8.8% of temperature raises would be
predicted by the Anomaly Detector together with the Ensemble
• Based on Precision, the Anomaly Detector together with the Ensemble could predict a
temperature raise on 3.8% of the original dataset with 1.9% correct predictions
• Based on this numbers the Machine Learning workflow could predict 8.8%
temperature raises and provided predictions would have a 50% reliability

35. #MLSEV 35
Conclusions
•Material deviation real time alerts could be implemented in Tunnel Boring
Machines using this method
•It has been proved Machine Learning means can provide useful TBM
insights
•Plenty of other Machine Learning analyses and implementations are
possible in TBMs. An advanced cockpit could be implemented