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Nonlinear Trend Analysis of Mill Fan System Vibrations for Predictive Maintenance and Diagnostics

Hadjiski M. B., Doukovska L. A., Kojnov S. L.

International Journal of Electronics and Telecommunications

2012

Vol. 58, No. 4

351-356

Present paper considers nonlinear trend analysis for diagnostics and predictive maintenance. The subject is a device from Maritsa East 2 thermal power plant a mill fan. The choice of the given power plant is not occasional. This is the largest thermal power plant on the Balkan Peninsula. Mill fans are main part of the fuel preparation in the coal fired power plants. The possibility to predict eventual damages or wear out without switching off the device is significant for providing faultless and reliable work avoiding the losses caused by planned maintenance. This paper addresses the needs of the Maritsa East 2 Complex aiming to improve the ecological parameters of the electro energy production process.

Recurrent Neural Networks for Predictive Maintenance of Mill Fan Systems

Koprinkova-Hristova P. D., Hadjiski M. B., Doukovska L. A., Beloreshki S. V.

International Journal of Electronics and Telecommunications

2011

Vol. 57, No. 3

401-406

In the present paper we focus on online monitoring system for predictive maintenance based on sensor automated inputs. Our subject was a device from Maritsa East 2 power plant - a mill fan. The main sensor information we have access to is based on the vibration of the nearest to the mill rotor bearing block. Our aim was to create a (nonlinear) model able to predict on time possible changes in vibrations tendencies that can be early signal for system work deterioration. For that purpose, we compared two types of recurrent neural networks: historical Elman architecture and a recently developed kind of RNN named Echo stet networks (ESN). The preliminary investigations showed better approximation and faster training abilities of ESN in comparison to the Elman network. Direction of future work will be increasing of predications time horizon and inclusion of our predictor at lower level of a complex predictive maintenance system.