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Equipment health diagnosis and prognosis using hidden semi-Markov models

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Abstract

In this paper, the development of hidden semi-Markov models (HSMMs) for equipment health diagnosis and prognosis is presented. An HSMM is constructed by adding a temporal component into the well-defined hidden Markov model (HMM) structures. The HSMM methodology offers two significant advantages over the HMM methodology in equipment health diagnosis and prognosis: (1) it overcomes the modeling limitation of HMM due to the Markov property and therefore improves the power in diagnosis, and (2) it can be directly used for prognosis. The application of the HSMMs to equipment health diagnosis and prognosis is demonstrated with the fault classification application of UH-60A Blackhawk main transmission planetary carriers and prognosis of a hydraulic pump health monitoring application. The effectiveness of the HSMMs is compared with that of the HMMs. The results of the application testing have shown that the HSMMs are capable of identifying the faults under both test cell and on-aircraft conditions while the performance of the HMMs is not comparable with that of the HSMMs. Furthermore, the HSMM-based methodology can be used to estimate the remaining useful life of equipment.

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References

  1. Atlas L, Ostendorf M, Bernard GD (2000) Hidden Markov models for monitoring machining tool-wear. ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing–Proceedings, vol 6, pp 3887–3890

  2. Baruah P, Chinnam RB (2003) HMMs for diagnostics and prognostics in machining processes. Proc. of the 57th Society for Machine Failure Prevention Technology Conference, Virginia Beach, VA, 14–18 April

  3. Begg CD, Merdes T, Byington C, Maynard K (1999) Dynamic modeling for mechanical diagnostics and prognostics. Maintenance and Reliability Conference (MARCON99)

  4. Bunks C, Mccarthy D, Tarik A (2000) Condition based maintenance of machines using hidden Markov models. Mech Syst Signal Process 14(4):597–612

    Article  Google Scholar 

  5. Chen MY, Kundu A, Zhou J (1994) Off-line handwritten work recognition using a hidden Markov model type stochastic network. IEEE Trans Pattern Anal Mach Intell 16(5):481–496

    Article  Google Scholar 

  6. Chen MY, Kundu A, Srihari SN (1995) Variable duration hidden Markov model and morphological segmentation for handwritten word recognition. IEEE Trans Image Process 4(12):1675–1688

    Article  Google Scholar 

  7. Dong M, He D (2004) Hidden semi-Markov models for machinery health diagnosis and prognosis. Trans NAMRI/SME XXXII:199–206

    Google Scholar 

  8. Ertunc HM, Loparo KA (2001) A decision fusion algorithm for tool wear condition monitoring in drilling. Int J Mach Tools Manuf 41:1347–1362

    Article  Google Scholar 

  9. Ertunc HM, Loparo KA, Ocak H (2001) Tool wear condition monitoring in drilling operations using hidden Markov models (HMMs). Int J Mach Tools Manuf 41:1363–1384

    Article  Google Scholar 

  10. Kannan A, Ostendorf M (1993) Comparison of trajectory and mixture modeling in segment-based word recognition. Proceedings-ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing, vol 2, Speech Processing, pp 327–330

  11. Keller JA, Grabill P (2003) Vibration monitoring of UH-60A main transmission planetary carrier fault. American Helicopter Society 59thAnnual Forum, Phoenix, AZ, 6–8 May 2003

  12. Ljolie A, Levinson SE (1991) Development of an acoustic-phonetic hidden Markov model for continuous speech recognition. IEEE Trans Signal Process 39(1):29–39

    Article  Google Scholar 

  13. Ostendorf M (1989) Stochastic segment model for phoneme-based continuous speech recognition. IEEE Trans Acoustics Speech Signal Process 37(12):1857–1869

    Article  Google Scholar 

  14. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  15. Roemer MJ, Kacprzynski GJ (2000) Advanced diagnostics and prognostics for gas turbine engine risk assessment. Proceedings of the 2000 IEEE Aerospace Conference, Big Sky, MT, 18–25 March

  16. Roemer MJ, Nwadiogbu EO, Bloor G (2001) Development of diagnostic and prognostic technologies for aerospace health management applications. Proceedings of the 2001 IEEE Aerospace Conference, Big Sky, MT, 10–17 March

  17. Wang L, Mehrabi MG , Kannatey-Asibu E Jr (2002) Hidden Markov model-based tool wear monitoring in machining. ASME J Manuf Sci Eng 124:651–658

    Article  Google Scholar 

  18. Russell MJ, Moore RK (1985) Explicit modeling of state occupancy in hidden Markov models for automatic speech recognition. Proc. of ICASSP ’85, Tampa, FL, pp 5–8

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Authors and Affiliations

  1. Department of Industrial Engineering and Management, Shanghai Jiao Tong University, 1954 Hua Shan Road, Xu-hui District, 200030, Shanghai, PR China

    Ming Dong

  2. Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, 842 West Taylor Street, Chicago, IL, 60607, USA

    David He & Prashant Banerjee

  3. U.S. Army RDECOM, Aviation Engineering Directorate, Redstone Arsenal, AL, 35898, USA

    Jonathan Keller

Authors
  1. Ming Dong
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  2. David He
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  3. Prashant Banerjee
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  4. Jonathan Keller
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Correspondence to Ming Dong.

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Dong, M., He, D., Banerjee, P. et al. Equipment health diagnosis and prognosis using hidden semi-Markov models. Int J Adv Manuf Technol 30, 738–749 (2006). https://doi.org/10.1007/s00170-005-0111-0

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  • DOI: https://doi.org/10.1007/s00170-005-0111-0

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