Diagnosis for improved maintenance services: Analysis of standards related to condition based maintenance

  • Luca Fumagalli
  • Erkki Jantunen
  • Marco Garetti
  • Marco Macchi


Depending on the maintenance strategy there can be enormous differences how much energy the machinery uses and how much waist it produces. It has become a common practise to study the efficiency of production machinery together with the quality of production and availability of this machinery i.e. the overall effectiveness is studied. In order to reach high efficiency, high availability and good quality, the production machinery has to be in the condition to fulfil these goals. In principle there are two questions that need to be answered when maintenance is planned for tackling the above described situation: 1) What do we have to do? 2) When do we need to take action? The maintenance strategy that has been developed to answer these questions in an optimal way is Condition Based Maintenance (CBM) i.e. the maintenance actions are based on the need of the machinery. Up to this level everything is very logical and clear but unfortunately the current reality in the industry is far from optimal. It is not an easy task to define the condition of production machinery and it is not easy to say what needs to be done and when. This paper is oriented to help to answer the What question i.e. diagnosis of the condition of machinery and When question i.e. prognosis of wear development is not discussed in detail. However, the What question as such is already very demanding. The reason for this is that automatic diagnosis should be based on measurements of the condition and this becomes very difficult in practise due to the differences in the production machinery and the difficulty in separating the condition information from information that is related to the production parameters.

The paper provides an ontology of diagnosis in order to support the building of diagnostic tools. The motivation for relying on defining ontology is based on the fact that it takes a lot of work to define a reliable diagnosis tool and it also is very demanding to be able to keep the system working when changes in the machinery or the software environment take place. The ontology is built in such a way that diagnosis of similar machinery can be identified and new type of machinery as well, based on the similarity of components. One of the most important findings, in the development of this process has been that in order to make the development of ontology possible in practise and in order to be able to keep the system up to date, is to rely on available definitions in the form of standards and practices that other developers are prepared to support and update.

The main focus of this paper is in defining the environment that supports the development of the ontology of diagnosis of the condition of rotating machinery. To build the ontology both references related with standards for exchange of information in a CBM system [1] and upper ontologies are analysed. Upper ontologies define toplevel classes such as physical objects and activities from which more specific classes and relations can be defined. For example following the scope of this paper ISO 15926-2 [2] is analysed. The using of upper ontologies to develop a more specific domain ontology enables to define concepts based on the more general concepts provided by the upper ontology, avoiding reinventing the wheel, while having better integration [3] and standardization.


Failure Mode Condition Monitoring Physical Object Maintenance Action Monitoring Technique 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    MIMOSA, OSA-CBM Primer, August 2006, www.mimosa.orgGoogle Scholar
  2. 2.
    ISO 15926, www.iso.orgGoogle Scholar
  3. 3.
    R. Batres, M. West, D. Leal, D. Price, M. Katsube, Y. Shimada, T. Fuchino. An upper ontology based on ISO 15926. Comp. & Chem. Eng., 31, 5-6 (2007) 519–534CrossRefGoogle Scholar
  4. 4.
    Brundtland Commission, 1987, Our Common Future, Report of the World Commission on Environment and Development, World Commission on Environment and Development, Published as Annex to General Assembly document A/42/427, Development and International Co-operation: Environment, August 2.Google Scholar
  5. 5.
    Grall, A., Dieulle, L., Berenguer, C. and Roussignol, M. (2002), “Continuous time predictive maintenance scheduling for a deteriorating system”, IEEE Transactions on Reliability, Vol. 51 No. 2, pp. 141-50.CrossRefGoogle Scholar
  6. 6.
    Chen, D. and Trivedi, K.S. (2002), “Closed-form analytical results for condition-based maintenance”, Reliability Engineering and System Safety, Vol. 76 No. 1, pp. 43-51.Google Scholar
  7. 7.
    Marseguerra, M., Zio, E. and Podofillini, L. (2002), “Condition based maintenance optimization by means of genetic algorithms and Monte Carlo simulation”, Reliability Engineering and System Safety, Vol. 77 No. 2, pp. 151-65.CrossRefGoogle Scholar
  8. 8.
    Jamali, M.A., Ait-Kadi, D., Cle´roux, R. and Artiba, A. (2005), “Joint optimal periodic and conditional maintenance strategy”, Journal of Quality in Maintenance Engineering, Vol. 11 No. 2, pp. 107-14.CrossRefGoogle Scholar
  9. 9.
    Rosqvist T., K. Laakso, M. Reunanen, Value-driven maintenance planning for a production plant (2009), Reliability Engineering and System Safety 94, 97–110.CrossRefGoogle Scholar
  10. 10.
    Higgs A., Parkin R., Jackson M., Al-Habaibeh A., Zorriassatine F. e Coy J. (2004),"A survey on condition monitoring systems in industry", Proceedings of: ESDA 2004, 7th Biennal ASME Conference Engineering Systems Design and Analysis, July 19-22, Manchester, UK.Google Scholar
  11. 11.
    www.skf.comGoogle Scholar
  12. 12.
    Nunnari J. J. ; Dalley R. J., An overview of ferrography and its use in maintenance, 1991. Tappi journal ISSN 0734-1415, vol. 74, no8, pp. 85-94.Google Scholar
  13. 13.
    Jantunen E., Adgar A., Arnaiz A. (2008), “Actors and roles in e-maintenance”, Proceedings of the 5th International Conference on Condition Monitoring and Machine Failure Prevention Technologies.Google Scholar
  14. 14.
    Ierace S., Carminati V, (2007). Application of thermography to Condition Based Maintenance: a case study in a manufacturing company, In: Proc. of 3rd International Conference on Maintenance and Facility Management, pp. 159-166, Roma, Italy, September 27 -28, 2007.Google Scholar
  15. 15.
    Mobley R. Keith (2002), "An introduction to predictive maintenance", Butterworth-HeinemannGoogle Scholar
  16. 16.
    Garetti M., Taisch M. (1997), Automatic production systems, in italian, original title: Sistemi di produzione automatizzati, 2nd ed., CUSL, MilanGoogle Scholar
  17. 17.
    Alberts, (1994) `Ymir: A sharable ontology for the formal representation of engineering design knowledge' Design Methods for CAD,, pp. 3-32.Google Scholar
  18. 18.
    Storey, V.C. Ullrich, H. Sundaresan, S. (1997) `An ontology for database design automation' Proceedings of the 16th International Conference of Conceptual Modelling, pp. 2-15.Google Scholar
  19. 19.
    Visser, P.R.S. and T.J.M. Bench-Capon (1998) A comparison of four ontologies for the design of legal knowledge systems, Artificial Intelligence and Law, vol. 6, n 1, pp. 25-57.CrossRefGoogle Scholar
  20. 20.
    Tassey, G., 1992. Technology Infrastructure and Competition Position. Kluwer, Norwell, MA.Google Scholar
  21. 21.
    ISO 17359, www.iso.orgGoogle Scholar
  22. 22.
    ISO 13373-1, www.iso.orgGoogle Scholar
  23. 23.
    ISO 13379, www.iso.orgGoogle Scholar
  24. 24.
    ISO 18436-2, www.iso.orgGoogle Scholar
  25. 25.
    ISO 13380, www.iso.orgGoogle Scholar
  26. 26.
    Thermography analysis on gas turbine systems. Inprotec MCM Milano April 2007Google Scholar
  27. 27.
    Moss, T.R. and Andrews, J.D., 1996. Factors influencing rotating machinery reliability. IN: Proceedings of the European Safety and Reliability Data Association Conference, 10th ESReDA, Chamonix, France, pp 149-171.Google Scholar
  28. 28.
    Neapolitan Engineering Association, Navy Commission. Remote monitoring of ships and maintenance strategies. Eng. Fabio SpetriniGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Luca Fumagalli
    • 1
  • Erkki Jantunen
    • 2
  • Marco Garetti
    • 1
  • Marco Macchi
    • 1
  1. 1.Department of Management, Economics and Industrial EngineeringPolitecnico di MilanoMilanoItaly
  2. 2.TT Technical Research Centre of FinlandVTTFinland

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