Applications of predictive maintenance techniques in industrial systems

Prognostic methods represent a new methodology for system maintenance which offers significant time and cost savings. The paper offers a short overview of the available prognosis techniques and proposes the implementation of one model-based and one data-driven method. As a representative of the model-based methods the autoregressive moving average (ARMA) modeling approach is chosen. The estimated model parameters are further used for implementing the early change detector which is realized as a Neyman-Pearson hypothesis test. On the other hand, hidden Markov model (HMM) based prognosis illustrates the use of data-driven techniques. Using the cross-correlation input-output functions, HMM prognosis algorithm is proposed, as a suitable way of timely detection. Both techniques were implemented to detect performance changes of the water level sensor in a steam separator system in thermal power plants.

[1]  Kai Goebel,et al.  Modeling Li-ion Battery Capacity Depletion in a Particle Filtering Framework , 2009 .

[2]  Hai Qiu,et al.  Physics-based Remaining Useful Life Prediction for Aircraft Engine Bearing Prognosis , 2009 .

[3]  Jianhui Luo,et al.  Diagnosis knowledge representation and inference , 2006, IEEE Instrumentation & Measurement Magazine.

[4]  R. D. Veaux,et al.  Prediction intervals for neural networks via nonlinear regression , 1998 .

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

[6]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[7]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[8]  G.J. Vachtsevanos,et al.  A particle filtering-based framework for real-time fault diagnosis and failure prognosis in a turbine engine , 2007, 2007 Mediterranean Conference on Control & Automation.

[9]  Luigi Portinale,et al.  Bayesian networks in reliability , 2007, Reliab. Eng. Syst. Saf..

[10]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[11]  Mark Schwabacher,et al.  A Survey of Data -Driven Prognostics , 2005 .

[12]  Z. Djurovic,et al.  Adaptive recursive M-robust system parameter identification using the QQ-plot approach , 2011 .

[13]  M. Farid Golnaraghi,et al.  Prognosis of machine health condition using neuro-fuzzy systems , 2004 .

[14]  Lennart Ljung,et al.  System Identification: Theory for the User , 1987 .

[15]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[16]  Yang Wei-we,et al.  A Review on , 2008 .

[17]  Damian Flynn,et al.  Thermal Power Plant Simulation and Control , 2003 .

[18]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

[19]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .

[20]  N. Zerhouni,et al.  Hidden Markov Models for failure diagnostic and prognostic , 2011, 2011 Prognostics and System Health Managment Confernece.

[21]  Sunil Menon,et al.  Neural Network Models for Usage Based Remaining Life Computation , 2006 .