[1]

N. Dedić and C. Stanier. Towards differentiating business intelligence, big data, data analytics and knowledge discovery. *Lecture Notes in Business Information Processing* 285:114–122, 2017. Google Scholar

[2]

I.A.T. Hashem, I. Yaqoob, N.B. Anuar, S. Mokhtar, A. Gani, and S. Ullah Khan. The rise of “big data” on cloud computing: Review and open research issues. *Information Systems* 47:98–115, 2015. Google Scholar

[3]

E. K. Juuso. Computational intelligence in distributed interactive synthetic environments. In Agostino G. Bruzzone and Eugene J. H. Kerckhoffs, editors, *Simulation in Industry, Proceedings of the 8th European Simulation Symposium, Simulation in Industry, ESS’96, Genoa, Italy, October 2–5, 1996* pages 157–162, San Diego, USA, 1996. SCS International. Google Scholar

[4]

Maryam M. Najafabadi, Flavio Villanustre, Taghi M. Khoshgoftaar, Naeem Seliya, Randall Wald, and Edin Muharemagic. Deep learning applications and challenges in big data analytics. *Journal of Big Data* 2(1):1–21, Feb 2015. Google Scholar

[5]

Jürgen Schmidhuber. Deep learning in neural networks: An overview. *Neural Networks* 61(Supplement C):85 – 117, 2015. Google Scholar

[6]

CLIC Innovation. Final report: Measurement, monitoring and environmental eflciency assessment. http://mmeafinalreport.fi/ 2015. Accessed: 2018-01-03.

[7]

Arrowhead framework. http://www.arrowhead.eu 2017. Accessed: 2018-01-03.

[8]

E. Jantunen, M. Karaila, D. Hästbacka, A. Koistinen, L. Barna, E. Juuso, P. Punal Pereira, S. Besseau, and J. Hoepffner. Application system design - Maintenance. In Jerker Delsing, editor, *IoT Automation - Arrowhead Framework* pages 247–280. CRC Press, Taylor & Francis Group, Boca Raton, FL, 2017. ISBN 9781-4987-5675-4. Google Scholar

[9]

G. E. P. Box and K. B. Wilson. On the experimental attainment of optimum conditions. *Journal of the Royal Statistical Society. Series B* 13(1):1–45, 1951. Google Scholar

[10]

L. Ljung. *System Identification - Theory for the User* Prentice Hall, Upper Saddle River, N.J., 2nd edition, 1999. Google Scholar

[11]

I. T. Jolliffe. *Principal Component Analysis* Springer, New York, 2 edition, 2002. 487 pp. Google Scholar

[12]

R. W. Gerlach, B. R. Kowalski, and H. O. A. Wold. Partial least squares modelling with latent variables. *Anal. Chim. Acta* 112(4):417–421, 1979. Google Scholar

[13]

L. Wasserman. *All of Nonparametric Statistics* Springer Texts in Statistics. Springer, Berlin, corr. 3rd edition, 2007. Google Scholar

[14]

L. A. Zadeh. Fuzzy sets. *Information and Control* 8(June):338–353, 1965. Google Scholar

[15]

D. Dubois, H. Prade, and L. Ughetto. Fuzzy logic, control engineering and artificial intelligence. In H. B. Verbruggen, H.-J. Zimmermann, and R. Babuska, editors, *Fuzzy Algorithms for Control, International Series in Intelligent Technologies* pages 17–57. Kluwer, Boston, 1999. Google Scholar

[16]

A. Krone and H. Kiendl. Automatic generation of positive and negative rules for two-way fuzzy controllers. In H.-J. Zimmermann, editor, *Proceedings of the Second European Congress on Intelligent Technologies and Soft Computing -EUFIT’94, Aachen, September 21 - 23, 1994* volume 1, pages 438–447, Aachen, 1994. Augustinus Buchhandlung. Google Scholar

[17]

A. Krone and U. Schwane. Generating fuzzy rules from contradictory data of different control strategies and control performances. In *Proceedings of the Fuzz-IEEE’96, New Orleans, USA* pages 492–497, 1996. Google Scholar

[18]

M. De Cock and E. E. Kerre. Fuzzy modifiers based on fuzzy relations. *Information Sciences* 160(1–4):173–199, 2004. Google Scholar

[19]

W. Pedrycz. An identification algorithm in fuzzy relational systems. *Fuzzy Sets and Systems* 13(2):153–167, 1984. Google Scholar

[20]

J. M. Mendel. Advances in type-2 fuzzy sets and systems. *Information Sciences* 177(1):84–110, 2007. Google Scholar

[21]

R. E. Moore. *Interval Analysis* Prentice Hall, Englewood Cliffs, NJ, 1966. Google Scholar

[22]

J. J. Buckley and Y. Qu. On using *α*-cuts to evaluate fuzzy equations. *Fuzzy Sets and Systems* 38(3):309–312, 1990. Google Scholar

[23]

J. J. Buckley and Y. Hayashi. Can neural nets be universal approximators for fuzzy functions? *Fuzzy Sets and Systems* 101:323–330, 1999. Google Scholar

[24]

J. J. Buckley and T. Feuring. Universal approximators for fuzzy functions. *Fuzzy Sets and Systems* 113:411–415, 2000. Google Scholar

[25]

T. Takagi and M. Sugeno. Fuzzy identification of systems and its applications to modeling and control. *IEEE Transactions on Systems, Man, and Cybernetics* 15(1):116–132, 1985. Google Scholar

[26]

E. K. Juuso and K. Leiviskä. Adaptive expert systems for metallurgical processes. In S.-L. Jämsä-Jounela and A. J. Niemi, editors, *Expert Systemsin Mineral and Metal Processing, IFACWorkshop, Espoo, Finland, August 26-28, 1991, IFAC Workshop Series, 1992, Number 2* pages 119–124, Oxford, UK, 1992. Pergamon. Google Scholar

[27]

E. K. Juuso. Fuzzy control in process industry: The linguistic equation approach. In H. B. Verbruggen, H.-J. Zimmermann, and R. Babuška, editors, *Fuzzy Algorithms for Control, International Series in Intelligent Technologies* volume 14 of *International Series in Intelligent Technologies* pages 243–300. Kluwer, Boston, 1999. Google Scholar

[28]

E. K. Juuso. Integration of intelligent systems in development of smart adaptive systems. *International Journal of Approximate Reasoning* 35(3):307–337, 2004. Google Scholar

[29]

E. K. Juuso. Tuning of large-scale linguistic equation (LE) models with genetic algorithms. In M. Kolehmainen, editor, *Revised selected papers of the International Conference on Adaptive and Natural Computing Algorithms - ICANNGA 2009, Kuopio, Finland, Lecture Notes in Computer Science* volume LNCS 5495, pages 161–170. Springer-Verlag, Heidelberg, 2009. Google Scholar

[30]

E. Juuso and S. Lahdelma. Intelligent scaling of features in fault diagnosis. In *7th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2010 - MFPT 2010, 22-24 June 2010, Stratford-upon-Avon, UK* volume 2, pages 1358–1372, 2010. Google Scholar

[31]

E. K. Juuso. Recursive tuning of intelligent controllers of solar collector fields in changing operating conditions. In S. Bittani, A. Cenedese, and S. Zampieri, editors, *Proceedings of the 18th World Congress The International Federation of Automatic Control,Milano (Italy) August 28 - September 2, 2011* pages 12282–12288. IFAC, 2011. Google Scholar

[32]

E. Juuso and S. Lahdelma. Intelligent trend indices and recursive modelling in prognostics. In *8th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2011 - MFPT 2011, 20-22 June 2011, Cardiff, UK* volume 1, pages 440–450. Curran Associates, NY, USA, 2011. www.scopus.com

[33]

E. K. Juuso. Intelligent methods in modelling and simulation of complex systems. *Simulation Notes Europe SNE* 24(1):1–10, 2014. Google Scholar

[34]

E. K. Juuso. Informative process monitoring with a natural language interface. In *2016 UKSim-AMSS 18th International Conference on Modelling and Simulation, 6-8 April, 2016, Cambridge, UK* pages 105–110. IEEE Computer Society, 2016. Google Scholar

[35]

E. K. Juuso. Intelligent trend indices in detecting changes of operating conditions. In *2011 UKSim 13th International Conference on Modelling and Simulation* pages 162–167. IEEE Computer Society, 2011. Google Scholar

[36]

J. T.-Y. Cheung and G. Stephanopoulos. Representation of process trends - part I. A formal representation framework. *Computers & Chemical Engineering* 14(4/5):495–510, 1990. Google Scholar

[37]

J. Lee, B. Bagheri, and H.-A. Kao. A cyber-physical systems architecture for industry 4.0-basedmanufacturing systems. *Manufacturing Letters* 3(Supplement C):18 – 23, 2015. Google Scholar

[38]

D. Anguita. Smart adaptive systems - state of the art and future directions for research. In *Proceedings of Eunite 2001 - European Symposium on Intelligent Technologies, Hybrid Systems and their implementation on Smart Adaptive Systems, July 13-14, 2001, Tenerife, Spain* pages 1–4. Wissenschaftsverlag Mainz, Aachen, 2001. Google Scholar

[39]

I. Guyon and A. Elisseeff. An introduction to feature extraction. In I. in Guyon, S. Gunn, M. Nikravesh, and L. Zadeh, editors, *Feature Extraction: Foundations and Applications* volume 207 of *Studies in Fuzziness and Soft Computing* pages 1–25. Springer, Heidelberg, 2003. Google Scholar

[40]

S. Lahdelma and E. Juuso. Generalised *l*_{p} norms in vibration analysis of process equipments. In *7th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2010 - MFPT 2010, 22-24 June 2010, Stratfordupon-Avon, UK* volume 1, pages 614–626. Curran Associates, NY, USA, 2010. ISBN 978-1-61839-013-4. Google Scholar

[41]

S. Lahdelma and E. Juuso. Signal processing and feature extraction by using real order derivatives and generalised norms. Part 1: Methodology. *International Journal of Condition Monitoring* 1(2):46–53, 2011. Google Scholar

[42]

Y. Deville, C. Jutten, and R. Vigario. Overview of source separation applications. In P. Comon and C. Jutten, editors, *Handbook of Blind Source Separation* pages 639–681. Academic Press, 2010. Google Scholar

[43]

Y. Saeys, I. Inza, and P. Larranaga. A review of feature selection techniques in bioinformatics. *Bioinformatics* 23(19):2507–2517, 2007. Google Scholar

[44]

H. A. Gaberson. The use of wavelets for analyzing transient machinery vibration. *Sound and Vibration* 36:12–177, 2002. Google Scholar

[45]

D. E. Newland. *An Introduction to Random Vibrations, Spectral and Wavelet Analysis* Longman Scientific & Technical, Harlow, UK, 3rd edition, 1993. Google Scholar

[46]

S. G. Samko, A. A. Kilbas, and O. I. Marichev. *Fractional Integrals and Derivatives. Theory and Applications* Gordon and Breach, Amsterdam, 1993. 976 pp. Google Scholar

[47]

K. Karioja and E. Juuso. Generalised spectral norms – a new method for condition monitoring. *International Journal of Condition Monitoring* 6(1):13–16, 2016. Google Scholar

[48]

C. Solomon and T. Breckon. *Fundamentals of Digital Image Processing: A Practical Approach with Examples in Matlab* John Wiley & Sons, 2010. ISBN 9780470689776. Google Scholar

[49]

J. Tomperi, E. Koivuranta, A. Kuokkanen, E. Juuso, and K. Leiviskä. Real-time optical monitoring of the wastewater treatment process. *Environmental Technology (United Kingdom)* 37(3):344–351, 2016. Google Scholar

[50]

M. H. Ramsey and S. L. R. Ellison, editors. *Eurachem/ EUROLAB/ CITAC/ Nordtest/ AMC Guide: Measurement uncertainty from sampling: a guide to methods and approaches* Eurachem, 2007. ISBN 978 0 948926 26 6. Google Scholar

[51]

E. K. Juuso and S. Lahdelma. Intelligent performance measures for condition-based maintenance. *Journal of Quality in Maintenance Engineering* 19(3):278–294, 2013. Google Scholar

[52]

E. K. Juuso. Integration of knowledge-based information in intelligent condition monitoring. In *9th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 12-14 June 2012, London, UK* volume 1, pages 217–228. Curran Associates, NY, USA, 2012. Google Scholar

[53]

E. Juuso, T. Latvala, and I. Laakso. Intelligent analysers and dynamic simulation in a biological water treatment process. In I. Troch and F. Breitenecker, editors, *6th Vienna Conference on Mathematical Modelling - MATHMOD 2009, February 11-13, 2009, Argesim Report no. 35* pages 999–1007. Argesim, 2009. ISBN 978-3-901608-35-3. Google Scholar

[54]

E. K. Juuso. Model-based adaptation of intelligent controllers of solar collector fields. In I. Troch and F. Breitenecker, editors, *Proceedings of 7th Vienna Symposium on Mathematical Modelling, February 14-17, 2012, Vienna, Austria, Part 1* volume 7, pages 979–984. IFAC, 2012. Google Scholar

[55]

E. Juuso. *Integration of intelligent systems in development of smart adaptive systems: linguistic equation approach* PhD thesis, University of Oulu, 2013. 258 pp., http://urn.fi/urn:isbn:9789526202891

[56]

E. K. Juuso and T. Ahola. Case-based detection of operating conditions in complex nonlinear systems. In M. J. Chung and P. Misra, editors, *Proceedings of 17th IFAC World Congress, Seoul, Korea, July 6-11, 2008* volume 17, pages 11142–11147. IFAC, 2008. Google Scholar

[57]

E. K. Juuso. Advanced data analysis in condition-based operation and maintenance. In *WCCM 2017 - 1st World Congress on Condition Monitoring 2017* volume 2, pages 750–761, Red Hook, NY, 2017. Curran Associates. Google Scholar

[58]

E. K. Juuso. Modelling and simulation in adaptive intelligent control. *Simulation Notes Europe SNE* 26(2):109–116, 2016. Google Scholar

[59]

A. Aamodt and E. Plaza. Case-based reasoning: Foundational issues, methodological variations and system approaches. *AICom- Artifical Intelligence Communications* 7(1):39–59, 1994. Google Scholar

[60]

I. Watson. Case-based reasoning is a methodology not a technology. *Knowledge-Based Systems* 12:303–308, 1999. Google Scholar

[61]

E. K. Juuso. Generalised statistical process control GSPC in stress monitoring. *IFAC-PapersOnline* 48(17):207–212, 2015. Google Scholar

[62]

E. K. Juuso. Recursive data analysis and modelling in prognostics. In *12th International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, CM 2015 - MFPT 2015, 9-11 June 2015, Oxford, UK* pages 560–567. BINDT, 2015. ISBN: 978-1-5108-0712-9. Google Scholar

[63]

E. K. Juuso and L. J. Yebra. Smart adaptive control of a solar collector field. In *IFAC Proceedings Volumes (IFAC-PapersOnline)* volume 19, pages 2564–2569, 2014. Google Scholar

[64]

S. Lahdelma and E. Juuso. Signal processing and feature extraction by using real order derivatives and generalised norms. Part 2: Applications. *International Journal of Condition Monitoring* 1(2):54–66, 2011. Google Scholar

[65]

E. K. Juuso. Advanced prognostics based on intelligent data analysis. In *WCCM 2017 - 1st World Congress on Condition Monitoring 2017* volume 2, pages 782–794, Red Hook, NY, 2017. Curran Associates. Google Scholar

[66]

E. K. Juuso. Intelligent performance analysis with a natural language interface. *Management Systems in Production Engineering* 25(3):168–175, 2017. Google Scholar

## Comments (0)

General note:By using the comment function on degruyter.com you agree to our Privacy Statement. A respectful treatment of one another is important to us. Therefore we would like to draw your attention to our House Rules.