Skip to main content
SpringerLink
Log in

Detecting cracks in reciprocating compressor valves using pattern recognition in the pV diagram

  • Industrial and Commercial Application
  • Published:
Pattern Analysis and Applications Aims and scope Submit manuscript

Abstract

We present a novel approach to detecting leaking reciprocating compressor valves based on the idea that a leaking valve affects the shape of the pressure-volume diagram (pV diagram). This effect can be observed when the valves are closed. To avoid disturbances due to the load control, we concentrate on the expansion phase, and linearize it using the logarithmic pV diagram. The gradient of the expansion phase serves as an indicator for the fault state of the valve. Since the gradient is also affected by the pressure conditions, both are used as features in our approach. After feature extraction, classification is performed using several established approaches and a one-class classification method based on linearizing the classification boundary and thresholding. The method was validated using real-world data, and the results show high classification accuracy for varying compressor loads and pressure conditions as well as different valve types.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Bloch HP (2006) A practical guide to compressor technology. Wiley, Hoboken

    Book  Google Scholar 

  2. Huschenbett M, Will G (2005) Thermodynamic simulation of reciprocating compressors to enable diagnostics based on measured temperatures and pressures. In: Proceedings of the 4th conference of the European forum of reciprocating compressors

  3. Lin YH, Wu HC, Wu CY (2006) Automated condition classification of a reciprocating compressor using time-frequency analysis and an artificial neural network. Inst Phys Publ Smart Mater Struct 15:1576–1584

    Article  Google Scholar 

  4. Lin YH, Liu HS, Wu CY (2009) Automated valve condition classification of a reciprocating compressor with seeded faults: experimentation and validation of classification strategy. Inst Phys Publ Smart Mater Struct 18:095020

  5. Tiwari A, Yadav P (2008) Application of ANN in condition monitoring of a defective reciprocating air compressor. J Instrum Soc. India 38(1):13–20

    Google Scholar 

  6. Ren Q, Ma X, Miao G (2005) Application of support vector machines in reciprocating compressor valve fault diagnosis. In: Wang L, Chen K, Ong YS (Eds) Advances in natural computation. Springer, Berlin Heidelberg, pp 81–84

  7. Vapnik V (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  8. Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20:273–297

    MATH  Google Scholar 

  9. Drewes E (2002) Condition monitoring for reciprocating compressors. Hydrocarb Process 81(9):77–79

  10. Zhang Y, Jiang J, Flatley M, Hill B (2001) Condition monitoring and fault detection of a compressor using signal processing techniques. In: Proceedings of the American control conference, Arlington, VA. vol 6, pp 4460–4465

  11. Elhaj M, Almrabet M, Rgeai M, Ehtiwesh I (2010) A combined practical approach to condition monitoring of reciprocating compressors using IAS and dynamic pressure. World Acad Sci Eng Technol 63:186–192

    Google Scholar 

  12. Wang F, Song L, Zhang L, Li H (2010) Fault diagnosis for reciprocating air compressor valve using p-V indicator diagram and SVM. In: Proceedings of the 3rd international symposium on information science and engineering, pp 255–258

  13. Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification. Department of Computer Science, National Taiwan University, Technical report

    Google Scholar 

  14. Schoelkopf B, Smola AJ (2002) Learning with kernels-support vector machines, regularization, optimization and beyond. MIT Press, London

    Google Scholar 

  15. Ma Y, Guo G (2014) Support vector machines applications. Springer, New York

    Book  Google Scholar 

  16. Wu X, Kumar V, Quinlan JR, Gosh J, Yang Q, Motoda H, MacLachlan GJ, Ng A, Liu B, Yu PS, Zhou ZH, Steinbach M, Hand DJ, Steinberg D (2006) Top 10 algorithms in data mining. Knowl Inf Syst 14(1):1–37

    Article  MATH  Google Scholar 

  17. Bishop CM (2007) Pattern recognition and machine learning. Springer, New York

    Google Scholar 

  18. Lewicki MS (1998) A review of methods for spike sorting: the detection and classification of neural action potentials. Network 9:R53–78

    Article  MATH  Google Scholar 

  19. Bloch HP, Hoefner JJ (1996) Reciprocating compressors: operation and maintenance. Gulf Publishing Company, Houston

    Google Scholar 

  20. Machu EH (1996) Reciprocating compressor diagnostics, detecting abnormal conditions from measured indicator cards. International compressor engineering conference, School of Mechanical Engineering, Purdue University

  21. Draper NR, Smith H (1998) Applied regression analysis. Wiley, New York

    Book  MATH  Google Scholar 

  22. Haykin S (1999) Neural networks: a comprehensive foundation. Prentice Hall, Englewood

    MATH  Google Scholar 

  23. Bauer F, Lukas M (2011) Comparing parameter choice methods for regularization of ill-posed problems. Math Comput Simul 81(9):1795–1841

    Article  MATH  MathSciNet  Google Scholar 

  24. Lughofer E, Kindermann S (2009) SparseFIS: data-driven learning of fuzzy systems with sparsity constraints. IEEE Trans Fuzzy Syst 18(2):396–411

  25. Dy JG, Brodley CE (2004) Feature selection for unsupervised learning. J Mach Learn Res 5:845–889

  26. Lughofer E (2011) On-line incremental feature weighting in evolving fuzzy classifiers. Fuzzy Sets Syst 163(1):1–23

  27. Snyman JA (2005) Practical mathematical optimization: an introduction to basic optimization theory and classical and new gradient-based algorithms. Springer Science+Business Media, New York

    Google Scholar 

  28. Manevitz LM, Yousef M (2001) One-class SVMs for document classification. J Mach Learn Res 2:139–154

    Google Scholar 

  29. Tax DMJ, Duin RPW (2004) Support vector data description. Mach Learn 54(1):45–66

    Article  MATH  Google Scholar 

  30. Mahadevan S, Shah SL (2009) Fault detection and diagnosis in process data using one-class support vector machines. J Process Control 19(10):1627–1693

    Article  Google Scholar 

  31. Shalchyan V, Jensen W, Farina D (2012) Spike detection and clustering with unsupervised wavelet optimization in extracellular neural recordings. IEEE Trans Biomed Eng 59(9):2576–2585

    Article  Google Scholar 

  32. Tax DMJ (2001) One-class classifcation. PhD thesis, Delft University of Technology, Netherlands

Download references

Acknowledgments

This work has been supported by the Austrian COMET-K2 programme of the Linz Center of Mechatronics (LCM), and was funded by the Austrian federal government and the federal state of Upper Austria.

Author information

Authors and Affiliations

  1. Information Analysis and Fault Diagnostics, Linz Center of Mechatronics GmbH, Linz, Austria

    Kurt Pichler, Markus Pichler & Thomas Buchegger

  2. Department of Knowledge-based Mathematical Systems, Johannes Kepler University Linz, Linz, Austria

    Edwin Lughofer & Erich Peter Klement

  3. Compression Technology, Hoerbiger Service America, Inc., Greenwood Village, CO, USA

    Matthias Huschenbett

Authors
  1. Kurt Pichler
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Edwin Lughofer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Markus Pichler
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Thomas Buchegger
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Erich Peter Klement
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Matthias Huschenbett
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kurt Pichler.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pichler, K., Lughofer, E., Pichler, M. et al. Detecting cracks in reciprocating compressor valves using pattern recognition in the pV diagram. Pattern Anal Applic 18, 461–472 (2015). https://doi.org/10.1007/s10044-014-0431-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10044-014-0431-5

Keywords

Search

Navigation