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From sensor readings to prediction: on the process of developing practical soft sensors

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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.

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From sensor readings to prediction: on the process of developing practical soft sensors

  1. 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. 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
  3. 3. Sensors, sensors, sensors
  4. 4. Sensors, sensors, sensors SSEENNSSOORRSS Image copyright by Disney Pixar. Qualifies fair usage. SSEENNSSOORRSS EEVVEERRYYWWHHEERREE
  5. 5. Easy vs difficult Easy-to-measure variables Difficult-to-measure variables Temperature Polymerisation progress Pressure Humidity Flow Fermentation progress Concentration
  6. 6. 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.
  7. 7. 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
  8. 8. 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
  9. 9. An unsuccessful soft sensor Image copyright by Disney Pixar. Qualifies fair usage.
  10. 10. A successful soft sensor Implemented into the process online environment Accepted by the process operators Requirements: • Reasonable performance • Stable • Predictable • Transparency • Automation • Robustness • Adaptivity
  11. 11. A successful soft sensor Image copyright by Disney Pixar. Qualifies fair usage.
  12. 12. 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
  13. 13. 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
  14. 14. 2. Data Analysis Exploratory data analysis Time series analysis
  15. 15. 3. Data Preparation and Pre-processing ✔Queries from databases ✔Sampling rate ✔Synchronization
  16. 16. 3. Data Preparation and Pre-processing ✔Remove data from shutdown periods
  17. 17. 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
  18. 18. 3. Data Preparation and Pre-processing ✔If outliers=noise, replace them with missing values imputation techniques
  19. 19. 3. Data Preparation and Pre-processing ✔Discretization ✔Derive new variables ✔Data scaling ✔Data rotation
  20. 20. 3. Data Preparation and Pre-processing ✔Feature selection ✔Subsampling
  21. 21. 3. Data Preparation and Pre-processing
  22. 22. 4. Training and Validation Training set for tuning pre-processing methods and building the model Testing set for evaluating the model
  23. 23. 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
  24. 24. 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
  25. 25. 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%
  26. 26. 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
  27. 27. 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
  28. 28. Thanks! Slides available in http://slideshare.net/draxus msalvador@bournemouth.ac.uk

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