Possibilistic Bayes modelling for predictive analytics

Studies in the process industry (and also common sense) show that the most cost effective way to keep production processes running is through predictive maintenance, i.e. to carry out optimal maintenance actions just in time before a process fails. Modern processes are highly auto-mated; data is collected with sensor technology that forms a big data context and offers challenges to identify coming failures from very large sets of data. Modern analytics develops algorithms that are fast and effective enough to create possibilities for optimal JIT (Just-in Time) maintenance decisions.

[1]  Christer Carlsson,et al.  Probabilistic versus possibilistic risk assessment models for optimal service level agreements in grid computing , 2013, Inf. Syst. E Bus. Manag..

[2]  W. Ashby,et al.  An Introduction to Cybernetics , 1957 .

[3]  Viktor Mikhaĭlovich Glushkov,et al.  An Introduction to Cybernetics , 1957, The Mathematical Gazette.

[4]  Bilal M. Ayyub,et al.  Fuzzy regression methods - a comparative assessment , 2001, Fuzzy Sets Syst..

[5]  Junzo Watada,et al.  Building a type II fuzzy qualitative regression model , 2012 .

[6]  Ning Wang,et al.  Fuzzy nonparametric regression based on local linear smoothing technique , 2007, Inf. Sci..

[7]  Galit Shmueli,et al.  Predictive Analytics in Information Systems Research , 2010, MIS Q..

[8]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[9]  Krassimir T. Atanassov,et al.  Intuitionistic fuzzy linear regression analysis , 2012, Fuzzy Optimization and Decision Making.

[10]  Christer Carlsson,et al.  Fuzzy reasoning in decision making and optimization , 2001, Studies in Fuzziness and Soft Computing.

[11]  Phil Diamond,et al.  Fuzzy least squares , 1988, Inf. Sci..

[12]  G. Nolan,et al.  Computational solutions to large-scale data management and analysis , 2010, Nature Reviews Genetics.

[13]  Elham Hosseinzadeh,et al.  A weighted goal programming approach to fuzzy linear regression with crisp inputs and type-2 fuzzy outputs , 2015, Soft Comput..

[14]  Shih-Pin Chen,et al.  A variable spread fuzzy linear regression model with higher explanatory power and forecasting accuracy , 2008, Inf. Sci..

[15]  Junzo Watada,et al.  Building a Type-2 Fuzzy Qualitative Regression Model , 2012, Journal of Advanced Computational Intelligence and Intelligent Informatics.

[16]  Eyke Hüllermeier,et al.  Learning from imprecise and fuzzy observations: Data disambiguation through generalized loss minimization , 2013, Int. J. Approx. Reason..

[17]  Chiang Kao,et al.  Entropy for fuzzy regression analysis , 2005, Int. J. Syst. Sci..

[18]  Dursun Delen,et al.  Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud , 2013, Decis. Support Syst..

[19]  Jeanne G. Harris,et al.  Competing on Analytics: The New Science of Winning , 2007 .