Performance improvement is taken as the primary goal in the asset management. Advanced data analysis is needed to efficiently integrate condition monitoring data into the operation and maintenance. Intelligent stress and condition indices have been developed for control and condition monitoring by combining generalized norms with efficient nonlinear scaling. These nonlinear scaling methodologies can also be used to handle performance measures used for management since management oriented indicators can be presented in the same scale as intelligent condition and stress indices. Performance indicators are responses of the process, machine or system to the stress contributions analyzed from process and condition monitoring data. Scaled values are directly used in intelligent temporal analysis to calculate fluctuations and trends. All these methodologies can be used in prognostics and fatigue prediction. The meanings of the variables are beneficial in extracting expert knowledge and representing information in natural language. The idea of dividing the problems into the variable specific meanings and the directions of interactions provides various improvements for performance monitoring and decision making. The integrated temporal analysis and uncertainty processing facilitates the efficient use of domain expertise. Measurements can be monitored with generalized statistical process control (GSPC) based on the same scaling functions.
If the inline PDF is not rendering correctly, you can download the PDF file here.
 S. Lahdelma and E.K. Juuso, ”Advanced signal processing and fault diagnosis in condition monitoring”, Insight, vol. 49, no. 12, pp. 719-725, 2007.
 S. Lahdelma and E.K. Juuso, “Signal processing and feature extraction by using real order derivatives and generalised norms. Part 1: Methodology”, The International Journal of Condition Monitoring, vol. 1, no. 2, pp. 46-53, 2011.
 S. Lahdelma and E.K. Juuso, “Signal processing and feature extraction by using real order derivatives and generalised norms. Part 2: Applications”, The International Journal of Condition Monitoring, vol. 1, no. 2, pp. 54-66, 2011.
 E.K. Juuso and S. Lahdelma, “Intelligent scaling of features in fault diagnosis”, in 7th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technol- ogies, Stratford-upon-Avon, United Kingdom, vol. 2, 2010, pp. 1358-1372.
 E.K. Juuso and S. Lahdelma, “Intelligent performance measures for condition-based maintenance”, Journal of Quality in Maintenance Engineering, vol. 19, no.3, pp. 278-294, 2013.
 C. Olsson and T. Svantesson, “Harmonised maintenance and reliability indicators - compare apples to apples”, Maintworld, vol. 1, no. 1, pp. 9-11, 2009.
 C. Idhammar, “The first world class maintenance organization”, Maintworld, vol. 2, no. 2, pp. 52-53, 2010.
 A. Parida and U. Kumar, “Maintenance performance measurement - methods, tools and applications”, Maintworld, vol. 1, no. 1, pp. 30-33, 2009.
 N.A. Al-Shammasi and S.S. Al-Shakhoyry, “Improving maintenance performance in Saudi Aramco”, Maintworld, vol. 2, no. 2, pp. 6-9, 2010.
 B. Hägg, “Maintenance - an investment in higher profitability”, in Proc. of The Int. Conf. in Oulu, Oulu, Finland, 2010, pp. 7-14.
 P. Willmott, “Post the streamlining - ‘where’s your maintenance strategy now?”, Maintworld, vol. 2, no. 1, pp. 16-22, 2010.
 S. Dash, R. Rengaswamy and V. Venkatasubramanian, “Fuzzy-logic based trend classification for fault diagnosis of chemical processes”, Computers and Chemical Engineering, vol. 27, pp. 347-362, 2003.
 J.T.-Y. Cheung and G. Stephanopoulos, “Representation of process trends - part I. A formal representation framework”, Computers and Chemical Engineering, vol. 14, no. 4-5, pp. 495-510, 1990.
 A.K.S. Jardine, D. Lin and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance”, Mechanical Systems and Signal Processing, vol. 20, no. 7, pp. 1483-1510, 2006.
 A.H. Christer and W. Wang, “A model of condition monitoring inspection of production plant”, International Journal of Production Research, vol. 30, no. 9, pp. 2199-2211, 1992.
 W. Wang, “A two-stage prognosis model in condition based maintenance”, European Journal of Operational Research, vol. 182, no. 3, pp. 1177-1187, 2007.
 W. Schütz, “A history of fatigue, Engineering Fracture Mechanics“, vol. 54, no. 2, pp. 263-300, 1996.
 A. Palmgren, “Die Lebensdauer von Kugellagern”, Verfahrenstechnik, vol. 68, pp. 339-341, 1924.
 M.A. Miner, “Cumulative damage in fatigue”, ASME Journal of Applied Mechanics, vol. 67, pp. 159-164, 1945.
 L.A. Zadeh, “Fuzzy sets”, Information and Control, vol. 8, pp. 338-353, 1965.
 E.K. Juuso, “Intelligent Methods in Modelling and Simulation of Complex Systems”, Simulation Notes Europe SNE, vol. 24, no. 1, pp. 1-10.
 E.K. Juuso and D. Galar, ”Intelligent real-time risk analysis for machines and process devices”, in Current Trends in Reliability, Availability, Maintainability and Safety: An Industry Perspective, K. Uday, A. Alireza, V.A. Kumar, V. Prabhakar, Eds. Cham: Springer International Publishing AG, pp. 229-240, 2016.
 K. Karioja and E.K. Juuso, “Generalised spectral norms - a new method for condition monitoring”, International Journal of Condition Monitoring, vol. 6, no. 1, pp. 13-16, Mar. 2016.
 E.K. Juuso, “Integration of intelligent systems in development of smart adaptive systems”, International Journal of Approximate Reasoning, vol. 35, no. 3, pp. 307-337, 2004.
 H.J. Zimmermann, Fuzzy set theory and its applications. Dordrecht: Kluwer Academic Publishers, 1992.
 E.K. Juuso, “Tuning of large-scale linguistic equation (LE) models with genetic algorithms”, in Int. Conf. on Adaptive and Natural Computing Algorithms, Kuopio, Finland, 2009, pp. 161-170.
 T. Ahola, E.K. Juuso and K. Leiviskä, “Variable Selection and Grouping in a Paper Machine Application”, International Journal of Computers, Communications & Control, vol. 2, no. 2, pp. 111-120, 2007.
 VDI 2056 Beurteilungsmaβstäbe für mechanische Schwingungen von Maschinen, VDI-Richtlinien, Oktober 1964.
 R.A. Collacott, Mechanical Fault Diagnosis and condition monitoring. London: Chapman and Hall, 1977.
 E.K. Juuso and S. Lahdelma, “Cavitation Indices in Power Control of Kaplan Water Turbines”, in 6th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, Dublin, Ireland, vol. 2, 2009, pp. 830-841.
 E.K. Juuso and M. Ruusunen, ”Fatigue prediction with intelligent stress indices based on torque measurements in a rolling mill”, in 10th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, Krakow, Poland, vol. 1, 2013, pp. 460-471.
 E.K. Juuso, “Intelligent indices for online monitoring of stress and condition”, in 11th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, Manchester, United Kingdom, vol. 1, 2014, pp. 637-648.
 J. Laurila, A. Koistinen, E.K. Juuso and T. Liedes, ”Monitoring of a rod mill using advanced feature extraction methods”, in 12th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, Oxford, United Kingdom, 2015, pp. 580-590.
 A. Koistinen, J. Laurila and E.K. Juuso, ”Rod mill liner monitoring using cumulative stress”, in 13th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, Paris, France, 2016, pp. 131-142.
 J. Nissilä, S. Lahdelma and J. Laurila, “Condition monitoring of the front axle of a load haul dumper with real order derivatives and generalised norms”, in 11th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, Manchester, United Kingdom, vol. 1, 2014, pp. 407-426.
 E.K. Juuso, “Model-based adaptation of intelligent controllers of solar collector fields”, in 7th Vienna Symp. on Mathematical Modelling, Vienna, Austria, vol. 7, 2012, pp. 979-984.
 E.K. Juuso, “Intelligent Trend Indices in Detecting Changes of Operating Conditions”, in 13th Int. Conf. on Computer Modelling and Simulation, Cambridge, United Kingdom, 2011, pp. 162-167.
 E.K. Juuso, “Informative process monitoring with a natural language interface”, in 18th Int. Conf. on Modelling and Simulation, Rome, Italy 2016, pp. 105-110.
 E.K. Juuso, “Recursive Data Analysis and Modelling in Prognostics”, in 12th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, Oxford, UK, 2015, pp. 560-567.
 E.K. Juuso and M. Ruusunen, ”Stress Indices in Fatigue Prediction”, in Maintenance, Condition Monitoring and Diagnostics & Maintenance Performance Measurement and Management, Oulu, Finland, 2015, pp. 89-96.
 E.K. Juuso, ”Generalised statistical process control (GSPC) in stress monitoring”, IFAC-Papers OnLine, vol. 28, no. 17, pp. 207-212, 2015.
 E.K. Juuso, “Integration of knowledge-based information in intelligent condition monitoring”, in 9th Int. Conf. on Condition Monitoring and Machinery Failure Prevention Technologies, London, United Kingdom, vol. 1, 2012, pp. 217-228.
 M. De Cock and E.E. Kerre, “Fuzzy modifiers based on fuzzy relations”, Information Sciences, vol. 160, no. 1-4, pp. 173-199, 2004.
 J.J. Buckley and T. Feuring, “Universal approximators for fuzzy functions”, Fuzzy Sets and Systems, vol. 113, pp. 411-415, 2000.
 J.J. Buckley and Y. Hayashi, “Can neural nets be universal approximators for fuzzy functions?”, Fuzzy Sets and Systems, vol. 101, pp. 323-330, 1999.
 J.J. Buckley and Y. Qu, “On using α-cuts to evaluate fuzzy equations”, Fuzzy Sets and System, vol. 38, no. 3, pp. 309-312, 1990.
 J.M. Mendel, “Advances in type-2 fuzzy sets and systems”, Information Sciences, vol. 177, no. 1, pp. 84- 110, 2007.
 E.K. Juuso, “Development of Multiple Linguistic Equation Models with Takagi-Sugeno Type Fuzzy Models”, in Int. Fuzzy Systems Association WORLD CONGR. & European Society for Fuzzy Logic and Technology CONF., Lisbon, Portugal, 2009, pp. 1779-1784.
 A. Koistinen and E.K. Juuso, ”On-site calculations of generalised norms for maintenance and operational monitoring”, in Maintenance, Condition Monitoring and Diagnostics & Maintenance Performance Measurement and Management, Oulu, Finland, 2015, pp. 107-112.
 A. Koistinen and E.K. Juuso, ”Information from Centralized Database to Support Local Calculations in Condition Monitoring”, in 9th EUROSIM Congr. on Modelling and Simulation, Oulu, Finland, 2016, pp. 1049-1054.