**Predictive Maintenance Based on Control Charts Applied at Thermoelectric Power Plant**

Emilija Kisić, Željko Đurović and Vera Petrović

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.68685

### Abstract

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.

Keywords: predictive maintenance, T<sup>2</sup> control chart, hidden Markov model, thermoelectric power plant, statistical process control

## 1. Introduction

In today's industry, application of the best maintenance strategies is a very important task in ensuring stability and reliability of technical systems. Numerous papers and books about different maintenance strategies can be found in literature, and almost everywhere the merits of predictive maintenance in regard to time-based maintenance are emphasized [1]. Predictive maintenance extends the period of time during which the system functions well, decreases unnecessary shutdowns, reduces material losses and prevents catastrophic failures. Although this field of research is very much advanced with the development of highly sophisticated technologies, there is still a lot of room for improvement of the existing techniques and the development of new ones.

© 2017 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

In this research, an innovative technique of predictive maintenance is proposed and applied to a specific problem that occurs at the thermoelectric power plant 'TEKO', Kostolac, Serbia. Namely, one of the key thermoelectric power plant components is the coal-grinding subsystem. When time-based maintenance is applied on grinding tables of the coal mill, grinding tables are replaced after certain number of working hours. Depending on the quality of the coal and grinding table itself, this replacement sometimes needs to be made before or after planned replacement. The only way to determine the condition of the grinding table is visual inspection, which implies the shutting down of the whole subsystem. Consequences of grinding table replacement after fixed time intervals are great material losses incurred because of frequent shutdowns of the entire coal-grinding subsystem. Also, there is a possibility that the failure will occur before replacement.

There is an 'urban legend' that experienced operators in industrial plants, such as thermoelectric power plants, can 'hear' the sounds in sound content from operational drives. Based on these sounds, they can recognize the detritions of specific elements that can wear out, such as mill-grinding tables. Also, in literature one can find that 99% of mechanical failures are foregone by some very noticeable indicators [2]. Because of these facts, the idea came up for the recording of acoustic signals while coal-grinding subsystem is operational. In this way, it is easy to obtain condition-monitoring data which can be applied for predictive maintenance, and there is no need for shutting down the whole subsystem for obtaining the information about grinding table condition.

The proposed method is a trade-off between solutions already offered in the literature, and originality of the proposed algorithm is based on the selection of failure prognostic technique. The main goal of the proposed algorithm is the increase of energy efficiency at the thermoelectric power plant.

This chapter is organized as follows: In the next section, we describe the concept of predictive maintenance in detail. In Section 3, a description of the coal-grinding subsystem in thermoelectric power plant will be given. In Section 4, we present a new predictive maintenance technique. Section 5 contains the results. The last section is the conclusion, with the discussion about gain results.
