A hybrid degradation tendency measurement method for mechanical equipment based on moving window and Grey–Markov model

Accurate degradation tendency measurement is vital for the secure operation of mechanical equipment. However, the existing techniques and methodologies for degradation measurement still face challenges, such as lack of appropriate degradation indicator, insufficient accuracy, and poor capability to track the data fluctuation. To solve these problems, a hybrid degradation tendency measurement method for mechanical equipment based on a moving window and Grey–Markov model is proposed in this paper. In the proposed method, a 1D normalized degradation index based on multi-feature fusion is designed to assess the extent of degradation. Subsequently, the moving window algorithm is integrated with the Grey–Markov model for the dynamic update of the model. Two key parameters, namely the step size and the number of states, contribute to the adaptive modeling and multi-step prediction. Finally, three types of combination prediction models are established to measure the degradation trend of equipment. The effectiveness of the proposed method is validated with a case study on the health monitoring of turbine engines. Experimental results show that the proposed method has better performance, in terms of both measuring accuracy and data fluctuation tracing, in comparison with other conventional methods.

[1]  Xun Sun,et al.  Compressive sensing-based feature extraction for bearing fault diagnosis using a heuristic neural network , 2017 .

[2]  Siliang Lu,et al.  A computer-vision-based rotating speed estimation method for motor bearing fault diagnosis , 2017 .

[3]  Ziji Ma,et al.  Trend extraction of rail corrugation measured dynamically based on the relevant low-frequency principal components reconstruction , 2016 .

[4]  Huisheng Zhang,et al.  A novel grey prognostic model based on Markov process and grey incidence analysis for energy conversion equipment degradation , 2016 .

[5]  Kodjo Agbossou,et al.  Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data , 2016 .

[6]  O. Kisi,et al.  Application of least square support vector machine and multivariate adaptive regression spline models in long term prediction of river water pollution , 2016 .

[7]  Theocharis Tsoutsos,et al.  Assessment of the safe operation and maintenance of photovoltaic systems , 2015 .

[8]  Wenlong Fu,et al.  A state tendency measurement for a hydro-turbine generating unit based on aggregated EEMD and SVR , 2015 .

[9]  Wei Sun,et al.  Blink Number Forecasting Based on Improved Bayesian Fusion Algorithm for Fatigue Driving Detection , 2015 .

[10]  Tamara Munzner,et al.  Dimensionality reduction for documents with nearest neighbor queries , 2015, Neurocomputing.

[11]  Weijun Gu,et al.  A new method of accelerated life testing based on the Grey System Theory for a model-based lithium-ion battery life evaluation system , 2014 .

[12]  Allan J. Volponi,et al.  Gas Turbine Engine Health Management: Past, Present, and Future Trends , 2014 .

[13]  Gabriel Fedorko,et al.  Degradation and chemical change of longlife oils following intensive use in automobile engines , 2014 .

[14]  Ali Cheknane,et al.  Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models , 2013 .

[15]  Bo-Suk Yang,et al.  Application of grey model for machine degradation prognostics , 2011 .

[16]  Ying Peng,et al.  A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assets , 2011, Expert Syst. Appl..

[17]  Gwo-Hshiung Tzeng,et al.  An integrated MCDM technique combined with DEMATEL for a novel cluster-weighted with ANP method , 2011, Expert Syst. Appl..

[18]  Lee-Ing Tong,et al.  Forecasting time series using a methodology based on autoregressive integrated moving average and genetic programming , 2011, Knowl. Based Syst..

[19]  Sun Jinhua,et al.  Application of Grey-Markov Model in Forecasting Fire Accidents , 2011 .

[20]  Ujjwal Kumar,et al.  Time series models (Grey-Markov, Grey Model with rolling mechanism and singular spectrum analysis) to forecast energy consumption in India , 2010 .

[21]  Okyay Kaynak,et al.  Grey system theory-based models in time series prediction , 2010, Expert Syst. Appl..

[22]  Ying Peng,et al.  Current status of machine prognostics in condition-based maintenance: a review , 2010 .

[23]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[24]  Jianbo Yu,et al.  A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems , 2008, 2008 International Conference on Prognostics and Health Management.

[25]  Abhinav Saxena,et al.  Damage propagation modeling for aircraft engine run-to-failure simulation , 2008, 2008 International Conference on Prognostics and Health Management.

[26]  Robert A. Baurle,et al.  Extraction of One-Dimensional Flow Properties from Multidimensional Data Sets , 2008 .

[27]  Diyar Akay,et al.  Grey prediction with rolling mechanism for electricity demand forecasting of Turkey , 2007 .

[28]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[29]  Rong Li,et al.  Residual-life distributions from component degradation signals: A Bayesian approach , 2005 .

[30]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[31]  Yi Lin,et al.  Theory of grey systems: capturing uncertainties of grey information , 2004 .

[32]  Hong Zhang,et al.  Application of grey modeling method to fitting and forecasting wear trend of marine diesel engines , 2003 .

[33]  Chia-Yon Chen,et al.  Applications of improved grey prediction model for power demand forecasting , 2003 .

[34]  Yi-Fan Wang,et al.  Predicting stock price using fuzzy grey prediction system , 2002, Expert Syst. Appl..

[35]  J. Deng,et al.  Introduction to Grey system theory , 1989 .