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Applications of predictive maintenance techniques in industrial systems

Author(s): Marjanović Aleksandra | Kvaščev Goran | Tadić Predrag | Đurović Željko

Journal: Serbian Journal of Electrical Engineering
ISSN 1451-4869

Volume: 8;
Issue: 3;
Start page: 263;
Date: 2011;
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Keywords: condition-based maintenance | prognosis | remaining useful life | data-driven methods | model-based methods | steam separators

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.

Tango Rapperswil
Tango Rapperswil

RPA Switzerland

Robotic Process Automation Switzerland