1. bookVolume 25 (2017): Issue 3 (September 2017)
Journal Details
License
Format
Journal
eISSN
2450-5781
First Published
30 Mar 2017
Publication timeframe
4 times per year
Languages
English
access type Open Access

Intelligent Performance Analysis with a Natural Language Interface

Published Online: 01 Aug 2017
Volume & Issue: Volume 25 (2017) - Issue 3 (September 2017)
Page range: 168 - 175
Received: 01 Oct 2016
Accepted: 01 Apr 2017
Journal Details
License
Format
Journal
eISSN
2450-5781
First Published
30 Mar 2017
Publication timeframe
4 times per year
Languages
English
Abstract

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.

Keywords

[1] S. Lahdelma and E.K. Juuso, ”Advanced signal processing and fault diagnosis in condition monitoring”, Insight, vol. 49, no. 12, pp. 719-725, 2007.10.1784/insi.2007.49.12.719Search in Google Scholar

[2] 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, 2011.10.1784/204764211798303805Search in Google Scholar

[3] 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, 2011.10.1784/204764211798303814Search in Google Scholar

[4] 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.Search in Google Scholar

[5] 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, 2013.10.1108/JQME-05-2013-0026Search in Google Scholar

[6] C. Olsson and T. Svantesson, “Harmonised maintenance and reliability indicators - compare apples to apples”, Maintworld, vol. 1, no. 1, pp. 9-11, 2009.Search in Google Scholar

[7] C. Idhammar, “The first world class maintenance organization”, Maintworld, vol. 2, no. 2, pp. 52-53, 2010.Search in Google Scholar

[8] A. Parida and U. Kumar, “Maintenance performance measurement - methods, tools and applications”, Maintworld, vol. 1, no. 1, pp. 30-33, 2009.Search in Google Scholar

[9] N.A. Al-Shammasi and S.S. Al-Shakhoyry, “Improving maintenance performance in Saudi Aramco”, Maintworld, vol. 2, no. 2, pp. 6-9, 2010.Search in Google Scholar

[10] SCEMM Keep It Running - Industrial Asset Management, Painoyhtymä, Loviisa, 1998.Search in Google Scholar

[11] B. Hägg, “Maintenance - an investment in higher profitability”, in Proc. of The Int. Conf. in Oulu, Oulu, Finland, 2010, pp. 7-14.Search in Google Scholar

[12] P. Willmott, “Post the streamlining - ‘where’s your maintenance strategy now?”, Maintworld, vol. 2, no. 1, pp. 16-22, 2010.Search in Google Scholar

[13] 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, 2003.10.1016/S0098-1354(02)00214-4Search in Google Scholar

[14] 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, 1990.10.1016/0098-1354(90)87023-ISearch in Google Scholar

[15] 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.Search in Google Scholar

[16] 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.Search in Google Scholar

[17] W. Wang, “A two-stage prognosis model in condition based maintenance”, European Journal of Operational Research, vol. 182, no. 3, pp. 1177-1187, 2007.Search in Google Scholar

[18] W. Schütz, “A history of fatigue, Engineering Fracture Mechanics“, vol. 54, no. 2, pp. 263-300, 1996.10.1016/0013-7944(95)00178-6Search in Google Scholar

[19] A. Palmgren, “Die Lebensdauer von Kugellagern”, Verfahrenstechnik, vol. 68, pp. 339-341, 1924.Search in Google Scholar

[20] M.A. Miner, “Cumulative damage in fatigue”, ASME Journal of Applied Mechanics, vol. 67, pp. 159-164, 1945.10.1115/1.4009458Search in Google Scholar

[21] L.A. Zadeh, “Fuzzy sets”, Information and Control, vol. 8, pp. 338-353, 1965.10.1016/S0019-9958(65)90241-XSearch in Google Scholar

[22] E.K. Juuso, “Intelligent Methods in Modelling and Simulation of Complex Systems”, Simulation Notes Europe SNE, vol. 24, no. 1, pp. 1-10.10.11128/sne.24.on.10221Search in Google Scholar

[23] 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.Search in Google Scholar

[24] 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. 2016.10.1784/204764216819257150Search in Google Scholar

[25] 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, 2004.10.1016/j.ijar.2003.08.008Search in Google Scholar

[26] H.J. Zimmermann, Fuzzy set theory and its applications. Dordrecht: Kluwer Academic Publishers, 1992.10.1007/978-94-015-7949-0Search in Google Scholar

[27] 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. 161-170.10.1007/978-3-642-04921-7_17Search in Google Scholar

[28] 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, 2007.10.15837/ijccc.2007.2.2344Search in Google Scholar

[29] VDI 2056 Beurteilungsmaβstäbe für mechanische Schwingungen von Maschinen, VDI-Richtlinien, Oktober 1964.Search in Google Scholar

[30] R.A. Collacott, Mechanical Fault Diagnosis and condition monitoring. London: Chapman and Hall, 1977.10.1007/978-94-009-5723-7Search in Google Scholar

[31] 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.Search in Google Scholar

[32] 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.Search in Google Scholar

[33] 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.Search in Google Scholar

[34] 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.Search in Google Scholar

[35] 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.Search in Google Scholar

[36] 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.Search in Google Scholar

[37] 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. 979-984.10.3182/20120215-3-AT-3016.00173Search in Google Scholar

[38] 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. 162-167.10.1109/UKSIM.2011.39Search in Google Scholar

[39] E.K. Juuso, “Informative process monitoring with a natural language interface”, in 18th Int. Conf. on Modelling and Simulation, Rome, Italy 2016, pp. 105-110.10.1109/UKSim.2016.37Search in Google Scholar

[40] 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.Search in Google Scholar

[41] 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.Search in Google Scholar

[42] E.K. Juuso, ”Generalised statistical process control (GSPC) in stress monitoring”, IFAC-Papers OnLine, vol. 28, no. 17, pp. 207-212, 2015.10.1016/j.ifacol.2015.10.104Search in Google Scholar

[43] 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.Search in Google Scholar

[44] M. De Cock and E.E. Kerre, “Fuzzy modifiers based on fuzzy relations”, Information Sciences, vol. 160, no. 1-4, pp. 173-199, 2004.10.1016/j.ins.2003.09.002Search in Google Scholar

[45] R.E. Moore, Interval Analysis. Englewood Cliffs (NJ): Prentice Hall, 1966.Search in Google Scholar

[46] J.J. Buckley and T. Feuring, “Universal approximators for fuzzy functions”, Fuzzy Sets and Systems, vol. 113, pp. 411-415, 2000.10.1016/S0165-0114(98)00069-4Search in Google Scholar

[47] J.J. Buckley and Y. Hayashi, “Can neural nets be universal approximators for fuzzy functions?”, Fuzzy Sets and Systems, vol. 101, pp. 323-330, 1999.10.1016/S0165-0114(97)00069-9Search in Google Scholar

[48] 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, 1990.10.1016/0165-0114(90)90204-JSearch in Google Scholar

[49] J.M. Mendel, “Advances in type-2 fuzzy sets and systems”, Information Sciences, vol. 177, no. 1, pp. 84- 110, 2007.10.1016/j.ins.2006.05.003Search in Google Scholar

[50] 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.Search in Google Scholar

[51] 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.Search in Google Scholar

[52] 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.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo