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Condition monitoring of a steam turbine generator using wavelet spectrum based control chart

Author

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  • Bae, Suk Joo
  • Mun, Byeong Min
  • Chang, Woojin
  • Vidakovic, Brani

Abstract

Condition-based maintenance (CBM) is designed to take maintenance actions only when there is an imminent evidence of failure for a monitoring system. The parameters indicating health status of the system are continuously monitored in CBM. This article proposes a condition monitoring scheme based on energy profiles generated from wavelet spectrum analysis. The energy of time series is represented by a wavelet spectrum in scale representations of signals. After deriving wavelet spectrums using a discrete wavelet transform at pre-specified windows, we aim to monitor the system based on multivariate T2 chart for the parameters in the linear energy profiles. The monitoring scheme is applied to temperature signals measured from a steam turbine generator. The proposed T2 chart based on the energy profiles shows a potential in early detecting the abnormality of a monitoring system which is not clearly detectable in original time scales.

Suggested Citation

  • Bae, Suk Joo & Mun, Byeong Min & Chang, Woojin & Vidakovic, Brani, 2019. "Condition monitoring of a steam turbine generator using wavelet spectrum based control chart," Reliability Engineering and System Safety, Elsevier, vol. 184(C), pages 13-20.
    • Handle: RePEc:eee:reensy:v:184:y:2019:i:c:p:13-20
    DOI: 10.1016/j.ress.2017.09.025
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    References listed on IDEAS

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    1. Nicolis, Orietta & Ramírez-Cobo, Pepa & Vidakovic, Brani, 2011. "2D wavelet-based spectra with applications," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 738-751, January.
    2. Christer, A. H. & Wang, W., 1995. "A simple condition monitoring model for a direct monitoring process," European Journal of Operational Research, Elsevier, vol. 82(2), pages 258-269, April.
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    Cited by:

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    2. Asgari, Ali & Si, Wujun & Yuan, Liang & Krishnan, Krishna & Wei, Wei, 2024. "Multivariable degradation modeling and life prediction using multivariate fractional Brownian motion," Reliability Engineering and System Safety, Elsevier, vol. 248(C).
    3. Quintanilha, Igor M. & Elias, Vitor R.M. & da Silva, Felipe B. & Fonini, Pedro A.M. & da Silva, Eduardo A.B. & Netto, Sergio L. & Apolinário, José A. & de Campos, Marcello L.R. & Martins, Wallace A., 2021. "A fault detector/classifier for closed-ring power generators using machine learning," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    4. Chen, Zhen & Zhou, Di & Zio, Enrico & Xia, Tangbin & Pan, Ershun, 2023. "Adaptive transfer learning for multimode process monitoring and unsupervised anomaly detection in steam turbines," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    5. Zhang, Sen-Ju & Kang, Rui & Lin, Yan-Hui, 2021. "Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    6. Kampitsis, Dimitris & Panagiotidou, Sofia, 2022. "A Bayesian condition-based maintenance and monitoring policy with variable sampling intervals," Reliability Engineering and System Safety, Elsevier, vol. 218(PA).
    7. Salman Khalid & Jinwoo Song & Izaz Raouf & Heung Soo Kim, 2023. "Advances in Fault Detection and Diagnosis for Thermal Power Plants: A Review of Intelligent Techniques," Mathematics, MDPI, vol. 11(8), pages 1-28, April.
    8. Zheng, Niannian & Luan, Xiaoli & Shardt, Yuri A.W. & Liu, Fei, 2024. "Dynamic-controlled principal component analysis for fault detection and automatic recovery," Reliability Engineering and System Safety, Elsevier, vol. 241(C).

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