Automatic data acquisition systems provide large amounts of streaming data generated by physical sensors. This data forms an input to computational models (soft sensors) routinely used for monitoring and control of industrial processes, traffic patterns, environment and natural hazards, and many more. The majority of these models assume that the data comes in a cleaned and pre-processed form, ready to be fed directly into a predictive model. In practice, to ensure appropriate data quality, most of the modelling efforts concentrate on preparing data from raw sensor readings to be used as model inputs. This study analyzes the process of data preparation for predictive models with streaming sensor data. We present the challenges of data preparation as a four-step process, identify the key challenges in each step, and provide recommendations for handling these issues. The discussion is focused on the approaches that are less commonly used, while, based on our experience, may contribute particularly well to solving practical soft sensor tasks. Our arguments are illustrated with a case study in the chemical production industry.

1. From sensor readings to prediction: on the
process of developing practical soft sensors
Marcin Budka1, Mark Eastwood2, Bogdan Gabrys1, Petr Kadlec3, Manuel Martin Salvador1,
Stephanie Schwan3, Athanasios Tsakonas1, Indre Zliobaite4
1Bournemouth University, UK
2Coventry University, UK
3Evonik Industries, Germany
4Aalto University and HIIT, Finland IDA 2014. Leuven, Belgium

2. Outline
1. INFER project
2. Sensors, sensors, sensors
3. Easy vs difficult
4. Soft Sensors
4.1. Soft Sensors: models
4.2. Soft Sensors in the Process Industry
4.3. An unsuccessful soft sensor
4.4. A successful soft sensor
4.5. How to build a successful data-driven soft sensor?
4.5.1. Performance goal and evaluation criteria
4.5.2. Data Analysis
4.5.3. Data Preparation and Pre-processing
4.5.4. Training and validation
5. Our case study
5.1. Versions of the data
5.2. Evaluation
6. Conclusion

4. Sensors, sensors, sensors

5. Sensors, sensors, sensors
SSEENNSSOORRSS
Image copyright by Disney Pixar. Qualifies fair usage.
SSEENNSSOORRSS EEVVEERRYYWWHHEERREE

6. Easy vs difficult
Easy-to-measure variables Difficult-to-measure variables
Temperature
Polymerisation progress
Pressure
Humidity
Flow
Fermentation progress
Concentration

7. Soft Sensors
Soft sensors are computational models
that aggregate readings of physical sensors
Soft sensors operate online using streams of sensor readings,
therefore they need to be robust to noise
and adaptive to changes over time.

8. Soft Sensors: models
First principle models Data-driven models
Based on physical and
chemical process
knowledge
Usually focus on ideal
states of the process
Process knowledge is not
available
Such knowledge can be
extracted from the data
(Machine Learning algorithms)
y=temp + press/2 - flow2
Linear Regression
PLS regression
Support Vector Machines
π

9. Soft Sensors in the Process Industry
Main areas of application
1. Online prediction of a difficult-to-measure variable
2. Inferential control in the process control loop
3. Multivariate process monitoring for determining the process state
4. Hardware sensor backup

10. An unsuccessful soft sensor
Image copyright by Disney Pixar. Qualifies fair usage.

11. A successful soft sensor
Implemented into the process online environment
Accepted by the process operators
Requirements:
• Reasonable performance
• Stable
• Predictable
• Transparency
• Automation
• Robustness
• Adaptivity

12. A successful soft sensor
Image copyright by Disney Pixar. Qualifies fair usage.

13. How to build a successful
data-driven soft sensor?
Proposed framework:
1) Setting up the performance goals and evaluation criteria
2) Data analysis (exploratory)
3) Data preparation and preprocessing
4) Training and validating the predictive model
Keep domain expert in the loop from the beginning

14. 1. Performance goals and evaluation criteria
Performance goal examples:
● Classification accuracy > 85%
● Processing time per sample < 1s
Evaluation criteria:
● Qualitative evaluation:
● Transparency
● Model complexity
● Quantitative evaluation:
● RMSE
● MAE
● Jitter
● Confidence

15. 2. Data Analysis
Exploratory data analysis
Time series analysis

16. 3. Data Preparation and Pre-processing
✔Queries from
databases
✔Sampling rate
✔Synchronization

17. 3. Data Preparation and Pre-processing
✔Remove data
from shutdown
periods

18. 3. Data Preparation and Pre-processing
1. Physical
constraints
2. Univariate
statistical tests
for individual
sensors
3. Multivariate
statistical tests
for all variables
together
4. Missing values

19. 3. Data Preparation and Pre-processing
✔If outliers=noise,
replace them
with missing
values imputation
techniques

20. 3. Data Preparation and Pre-processing
✔Discretization
✔Derive new
variables
✔Data scaling
✔Data rotation

21. 3. Data Preparation and Pre-processing
✔Feature selection
✔Subsampling

22. 3. Data Preparation and Pre-processing

23. 4. Training and Validation
Training set for tuning
pre-processing methods
and building the model
Testing set for
evaluating the model

24. Our case study
Background picture is Creative Commons by Paul Joyce
Real industrial dataset from a debutanizer column
3 years of operation
189,193 records (every 5 min)
85 sensors
Target: concentration of the product

25. Versions of the data
Code Description
RAW no pre-processing (188752 training / 21859 testing)
SUB subsampling (every 1h – 15611 training / 1822 testing)
SYN features are synchronised
FET-E 20 features selected using the first 1000 training samples
FET-L 20 features selected using the latest 1000 training samples
FRA additional features derived by computing the fractal dimension
DIF original values are replaced with the first derivative with respect
to time

26. Evaluation
Partial Least Squares regression → transparency
MAE = Mean Absolute Error
Data #1 MAE #1 Data #2 MAE #2 % improvement
RAW 225 RAW-SYN 222 1%
SUB 227 SUB-SYN 221 3%
RAW-FET-E 228 RAW-FET-L 198 13%
RAW-SYN-FET-E 245 RAW-SYN-FET-L 201 18%
SUB-FET-E 236 SUB-FET-L 193 18%
SUB-SYN-FET-E 215 SUB-SYN-FET-L 185 14%
SUB-DIF 41.8 SUB-DIF-SYN 35.3 16%
SUB-DIF 41.8 SUB-DIF-FRA 32.4 22%

27. Evaluation (cont.)
● Feature synchronization can have positive or negative effect
in prediction
● Adaptive feature selection using the latest samples is
beneficial → Feature importance change over time
● Taking into account temporal differences is very beneficial
→ Product concentration does not change suddenly

28. Conclusion
✔Framework for building a successful soft sensor
✔Case study with real data from industrial production process
✔Adaptive pre-processing could be very beneficial (and
sometimes a must)
Future directions:
Extend feature space with autoregressive features
Filter out the effects of data compression
Ongoing work:
Automation and adaptation of data stream pre-processing

29. Thanks!
Slides available in http://slideshare.net/draxus
msalvador@bournemouth.ac.uk