Abstract
An innovative approach to condition-based maintenance of coal grinding subsystems at thermoelectric power plants is proposed in the paper. Coal mill grinding tables become worn over time and need to be replaced through time-based maintenance, after a certain number of service hours. At times such replacement is necessary earlier or later than prescribed, depending on the quality of the coal and of the grinding table itself. Considerable financial losses are incurred when the entire coal grinding subsystem is shut down and the grinding table found to not actually require replacement. The only way to determine whether replacement is necessary is to shut down and open the entire subsystem for visual inspection. The proposed algorithm supports condition-based maintenance and involves the application ofT2control charts to distinct acoustic signal parameters in the frequency domain and the construction of Hidden Markov Models whose observations are coded samples from the control charts. In the present research, the acoustic signals were collected by coal mill monitoring at the thermoelectric power plant “Kostolac” in Serbia. The proposed approach provides information about the current condition of the grinding table.
Highlights
In today’s industry, fault detection and preventive maintenance are the most important tasks in ensuring reliability and safety of automated and highly complex processes
The dominant frequencies of the acoustic signal were expected to change over time, or it was expected that the grinding table would gradually become worn and that there will be an increasing number of outliers, until the time of utter wear, when most of the points would be beyond the control lines
The paper puts forward an innovative approach to conditionbased maintenance, which was applied to a coal grinding subsystem at the Kostolac TPP in Serbia
Summary
In today’s industry, fault detection and preventive maintenance are the most important tasks in ensuring reliability and safety of automated and highly complex processes. The experience-based prognostic method relies on stochastic models of degradation phenomena, or the life cycle of the components, taking into account the data and knowledge accumulated over the entire service life of the industrial system. Such data are used to adjust the parameters of certain reliability models (Weibull law, exponential law, etc.) [22]. The approach proposed in this paper is new because it offers compromises that exceed existing solutions It is based on the data-driven method since it does not require knowledge of the model, while the nonstationary state of the model is addressed through the introduction of an HMM.
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