Predictive Maintenance Based on Control Charts Applied at Thermoelectric Power Plant

In this chapter, innovative predictive maintenance technique is described with the aim of highlighting the benefits of predictive maintenance compared to time-based maintenance. The proposed technique is applied to a specific problem that occurs when time-based maintenance is applied on grinding tables of the coal mill, in coal-grinding subsystem at the thermoelectric power plant ‘TEKO’, Kostolac, Serbia. Time-based maintenance provides replacement of grinding tables after certain number of working hours, but depending on the quality of the coal and grinding table itself, this replacement sometimes needs to be made before or after planned replacement. The consequences of such maintenance are great material losses incurred because of frequent shutdowns of the entire coal-grinding subsystem, as well as the possibility that the failure occurs before replacement. Innovative predictive maintenance technique described in the chapter is used for solution of this problem.

[1]  Ali Azadeh,et al.  An integrated systemic model for optimization of condition-based maintenance with human error , 2014, Reliab. Eng. Syst. Saf..

[2]  N. Pappa,et al.  Modeling and outlet temperature control of coal mill using Model Predictive Controller , 2013, 2013 IEEE International Conference on Control Applications (CCA).

[3]  Benoît Iung,et al.  Remaining useful life estimation based on stochastic deterioration models: A comparative study , 2013, Reliab. Eng. Syst. Saf..

[4]  Abdessamad Kobi,et al.  Spectral Control Chart , 2005 .

[5]  Silvio Simani,et al.  Model-Based Fault Diagnosis Techniques , 2003 .

[6]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[7]  R. Keith Mobley,et al.  An introduction to predictive maintenance , 1989 .

[8]  L. Cohen,et al.  Time-frequency distributions-a review , 1989, Proc. IEEE.

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

[10]  Naim Baydar,et al.  A comparative study of acoustic and vibration signals in detection of gear failures using Wigner-Ville distribution. , 2001 .

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

[12]  J. Moubray.  Reliability-centred maintenance , 1995 .

[13]  H. Hotelling,et al.  Multivariate Quality Control , 1947 .

[14]  Elijah Kannatey-Asibu,et al.  Hidden Markov model-based tool wear monitoring in turning , 2002 .

[15]  Steven X. Ding,et al.  Model-based fault diagnosis in technical processes , 2000 .

[16]  Dragan Banjevic,et al.  Calculation of reliability function and remaining useful life for a Markov failure time process , 2006 .

[17]  V. Makis,et al.  Recursive filters for a partially observable system subject to random failure , 2003, Advances in Applied Probability.

[18]  Krishna R. Pattipati,et al.  Model-Based Prognostic Techniques Applied to a Suspension System , 2008, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[19]  Michael J. Roemer,et al.  Predicting remaining life by fusing the physics of failure modeling with diagnostics , 2004 .

[20]  Shankar Sankararaman,et al.  Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction , 2015 .

[21]  Željko Đurović,et al.  Application of Control Charts and Hidden Markov Models in Condition-Based Maintenance at Thermoelectric Power Plants , 2015 .

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

[23]  R. Keith Mobley.  Predictive Maintenance Techniques , 2002 .

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

[25]  Seungchul Lee,et al.  Online Degradation Assessment and Adaptive Fault Detection Using Modified Hidden Markov Model , 2010 .

[26]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[27]  Long-Sheng Chen,et al.  Using SVM based method for equipment fault detection in a thermal power plant , 2011, Comput. Ind..

[28]  Yongyong He,et al.  Hidden Markov model-based fault diagnostics method in speed-up and speed-down process for rotating machinery , 2005 .

[29]  John T. Renwick,et al.  Vibration Analysis---A Proven Technique as a Predictive Maintenance Tool , 1985, IEEE Transactions on Industry Applications.

[30]  Lin Ma,et al.  Condition Monitoring in Engineering Asset Management , 2007 .

[31]  Wai-Ki Ching,et al.  Detection of machine failure: Hidden Markov Model approach , 2009, Comput. Ind. Eng..

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

[33]  Rick L. Edgeman,et al.  Multivariate Statistical Process Control with Industrial Applications , 2004, Technometrics.

[34]  Heinz P. Bloch,et al.  Machinery failure analysis and troubleshooting , 1983 .

[35]  Fred Spiring,et al.  Introduction to Statistical Quality Control , 2007, Technometrics.

[36]  Lee,et al.  [American Institute of Aeronautics and Astronautics 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Austin, Texas ()] 46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference - Aeroelastic Studies on a Folding Wing Configuration , 2005 .

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

[38]  Shahrul Kamaruddin,et al.  An overview of time-based and condition-based maintenance in industrial application , 2012, Comput. Ind. Eng..

[39]  S. K. Yang.  A condition-based preventive maintenance arrangement for thermal power plants , 2004 .