Skip to main content

Semi-supervised Approach to Soft Sensor Modeling for Fault Detection in Industrial Systems with Multiple Operation Modes

  • Conference paper
  • First Online:
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017 (AISI 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 639))


In industrial systems, certain process variables that need to be monitored for detecting faults are often difficult or impossible to measure. Soft sensor techniques are widely used to estimate such difficult-to-measure process variables from easy-to-measure ones. Soft sensor modeling requires training datasets including the information of various states such as operation modes, but the fault dataset with the target variable is insufficient as the training dataset. This paper describes a semi-supervised approach to soft sensor modeling to incorporate an incomplete dataset without the target variable in the training dataset. To incorporate the incomplete dataset, we consider the properties of processes at transition points between operation modes in the system. The regression coefficients of the operation modes are estimated under constraint conditions obtained from the information on the mode transitions. In a case study, this constrained soft sensor modeling was used to predict refrigerant leaks in air-conditioning systems with heating and cooling operation modes. The results show that this modeling method is promising for soft sensors in a system with multiple operation modes.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. Bandaru, S., Ng, A.H., Deb, K.: Data mining methods for knowledge discovery in multi-objective optimization: part a-survey. Expert Syst. Appl. 70, 139–159 (2017)

    Article  Google Scholar 

  2. Bishop, C.M.: Pattern recognition. Mach. Learn. 128, 1–58 (2006)

    Google Scholar 

  3. Chiang, L.H., Russell, E.L., Braatz, R.D.: Fault Detection and Diagnosis in Industrial Systems. Springer Science & Business Media, Heidelberg (2000)

    MATH  Google Scholar 

  4. Dunia, R., Qin, S.J.: Joint diagnosis of process and sensor faults using principal component analysis. Control Eng. Pract. 6(4), 457–469 (1998)

    Article  Google Scholar 

  5. Fortuna, L., Graziani, S., Rizzo, A., Xibilia, M.G.: Soft Sensors for Monitoring and Control of Industrial Processes. Springer Science & Business Media, Heidelberg (2007)

    MATH  Google Scholar 

  6. Fujiwara, K., Kano, M., Hasebe, S., Takinami, A.: Soft-sensor development using correlation-based just-in-time modeling. AIChE J. 55(7), 1754–1765 (2009)

    Article  Google Scholar 

  7. Garcia-Alvarez, D., Fuente, M., Vega, P.: Fault detection in processes with multiple operation modes using switch-PCA and analysis of grade transitions. In: 2009 European Control Conference (ECC), pp. 2530–2535. IEEE (2009)

    Google Scholar 

  8. Ge, Z., Song, Z.: Multivariate Statistical Process Control: Process Monitoring Methods and Applications. Springer Science & Business Media, Heidelberg (2012)

    MATH  Google Scholar 

  9. Han, J., Pei, J., Kamber, M.: Data Mining: Concepts and Techniques. Elsevier (2011)

    Google Scholar 

  10. Jolliffe, I.: Principal Component Analysis. Wiley, New York (2002)

    MATH  Google Scholar 

  11. Kadlec, P., Gabrys, B., Strandt, S.: Data-driven soft sensors in the process industry. Comput. Chem. Eng. 33(4), 795–814 (2009)

    Article  Google Scholar 

  12. Kadlec, P., Grbić, R., Gabrys, B.: Review of adaptation mechanisms for data-driven soft sensors. Comput. Chem. Eng. 35(1), 1–24 (2011)

    Article  Google Scholar 

  13. Kalos, A., Rey, T.: Data mining in the chemical industry. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 763–769. ACM (2005)

    Google Scholar 

  14. Kaneko, H., Funatsu, K.: Classification of the degradation of soft sensor models and discussion on adaptive models. AIChE J. 59(7), 2339–2347 (2013)

    Article  Google Scholar 

  15. Kim, M., Yoon, S.H., Payne, W.V., Domanski, P.A.: Development of the reference model for a residential heat pump system for cooling mode fault detection and diagnosis. J. Mech. Sci. Technol. 24(7), 1481–1489 (2010)

    Article  Google Scholar 

  16. Lange, K.: Numerical Analysis for Statisticians. Springer Science & Business Media, Heidelberg (2010)

    Book  MATH  Google Scholar 

  17. Lin, B., Recke, B., Knudsen, J.K., Jørgensen, S.B.: A systematic approach for soft sensor development. Comput. Chem. Eng. 31(5), 419–425 (2007)

    Article  Google Scholar 

  18. Liu, J.: Developing soft sensors based on data-driven approach. In: 2010 International Conference on Technologies and Applications of Artificial Intelligence (TAAI), pp. 150–157. IEEE (2010)

    Google Scholar 

  19. Lou, S., Budman, H., Duever, T.: Comparison of fault detection techniques: problem and solution. In: 2002 Proceedings of the American Control Conference, vol. 6, pp. 4513–4518. IEEE (2002)

    Google Scholar 

  20. Qi, Y., Wang, P., Gao, X.: Enhanced batch process monitoring and quality prediction using multi-phase dynamic pls. In: 2011 30th Chinese Control Conference (CCC), pp. 5258–5263. IEEE (2011)

    Google Scholar 

  21. Sari, A.: Data-Driven Design of Fault Diagnosis Systems: Nonlinear Multimode Processes. Springer Fachmedien Wiesbaden, SpringerLink, Bücher (2014)

    Google Scholar 

  22. Serpas, M., Chu, Y., Hahn, J.: Fault detection approach for systems involving soft sensors. J. Loss Prev. Process Ind. 26, 443–452 (2013)

    Article  Google Scholar 

  23. Tian, H.X., Liu, X.J., Han, M.: An outliers detection method of time series data for soft sensor modeling. In: 2016 Chinese Control and Decision Conference (CCDC), pp. 3918–3922. IEEE (2016)

    Google Scholar 

  24. Valle, S., Li, W., Qin, S.J.: Selection of the number of principal components: the variance of the reconstruction error criterion with a comparison to other methods. Ind. Eng. Chem. Res. 38(11), 4389–4401 (1999)

    Article  Google Scholar 

  25. Van Loan, C.F.: Matrix computations (johns hopkins studies in mathematical sciences) (1996)

    Google Scholar 

  26. Wang, S., Xiao, F.: Ahu sensor fault diagnosis using principal component analysis method. Energy Build. 36(2), 147–160 (2004)

    Article  Google Scholar 

  27. Xiao, F., Wang, S., Zhang, J.: A diagnostic tool for online sensor health monitoring in air-conditioning systems. Autom. Constr. 15(4), 489–503 (2006)

    Article  Google Scholar 

  28. Yang, C.K., Alemi, A., Langari, R.: Sensor fault detection and isolation using phase space reconstruction. In: 2015 American Control Conference (ACC), pp. 892–899, July 2015

    Google Scholar 

  29. Yuan, X., Ge, Z., Song, Z.: Soft sensor model development in multiphase/multimode processes based on Gaussian mixture regression. Chemometr. Intell. Lab. Syst. 138, 97–109 (2014)

    Article  Google Scholar 

Download references


We would like to thank the Office of Air Conditioner Products Development of Fujitsu General Limited for providing us with the air-conditioning system datasets.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Shun Takeuchi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Takeuchi, S., Nishino, T., Saito, T., Watanabe, I. (2018). Semi-supervised Approach to Soft Sensor Modeling for Fault Detection in Industrial Systems with Multiple Operation Modes. In: Hassanien, A., Shaalan, K., Gaber, T., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2017. AISI 2017. Advances in Intelligent Systems and Computing, vol 639. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64860-6

  • Online ISBN: 978-3-319-64861-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics