2. Predictive maintenance

Nowadays, industrial processes are very complex and cannot be imagined without modern technologies, so highly sophisticated and very expensive maintenance strategies are needed. Consequences of inefficient maintenance are large material losses, and because of that it is necessary to constantly develop and improve the existing maintenance programmes.

Maintenance strategies were evolving during time. The first maintenance strategy was the unplanned maintenance or run-to-failure maintenance which implies waiting for failure to occur. It is obvious that with this maintenance strategy catastrophic failures are unavoidable, so very rare this kind of maintenance is sustainable and profitable. Later, preventive maintenance was introduced. Preventive maintenance can be conducted as planned maintenance or time-based maintenance, which is implemented at fixed time intervals, or it can be conducted as predictive maintenance or condition-based maintenance where maintenance activities are realized based on the condition of the system. Although with time-based maintenance equipment failures sometimes can be reduced, it does not eliminate catastrophic failures and causes unnecessary maintenance. In literature, it can be found that in the USA, because of ineffective maintenance, more than 60 billion of dollars are spent every year [3]. Similar situation is in other countries. Namely, the biggest shortcoming of time-based maintenance is too often replacement of system's parts, as well as premature stopping of the system while it is operational, which leads to great material losses. In most situations, predictive maintenance is the best choice, especially when maintenance is very expensive and occurring of failure is unacceptable. The main goal of predictive maintenance is extension of time in which system functions well and at the same time reduction of unnecessary stoppages and failures. Also, the aim of predictive maintenance is to prevent the occurring of catastrophic failures which can produce not only material costs but also loss of lives and environment pollution. List of this kind of accidents is not small and can be found in Ref. [4]. Because of these catastrophic failures which occasionally occur in modern industries, more attention is paid to the improvement of the existing predictive maintenance strategies, as well as to introducing the new ones. If it is regularly established and effectively implemented, predictive maintenance can significantly reduce maintenance expenses through cutting down of unnecessary time-based maintenance operations [5].

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

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

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 thermoelec-

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

Nowadays, industrial processes are very complex and cannot be imagined without modern technologies, so highly sophisticated and very expensive maintenance strategies are needed. Consequences of inefficient maintenance are large material losses, and because of that it is

Maintenance strategies were evolving during time. The first maintenance strategy was the unplanned maintenance or run-to-failure maintenance which implies waiting for failure to occur. It is obvious that with this maintenance strategy catastrophic failures are unavoidable, so very rare this kind of maintenance is sustainable and profitable. Later, preventive maintenance was introduced. Preventive maintenance can be conducted as planned maintenance or

necessary to constantly develop and improve the existing maintenance programmes.

occur before replacement.

28 Recent Improvements of Power Plants Management and Technology

about grinding table condition.

2. Predictive maintenance

tric power plant.

about gain results.

Diagnostics and prognostics are two very important aspects in predictive maintenance programme. Diagnostics deals with fault detection, isolation and identification after occurring of the fault. Fault detection indicates when something goes wrong in a monitored system, that is, it detects that failure has occurred. Fault isolation has a task to locate faulty component, whereas fault identification has a task to determine the nature of the fault when the fault is detected. Diagnostics has been developed for years, and today it presents very important area in engineering and automatic control [6, 7].

Prognostics deals with fault prediction, before the fault will occur. In other words, diagnostics is the posterior analysis of events, while prognostic is a priori analysis of events. Prognostics is more efficient in regard to diagnostics for achieving zero-downtime performances. On the other hand, diagnostics is necessary when failure prediction within prognostic fails and fault occurs. References about prognostics can be found in Refs. [8, 9]. Predictive maintenance can be used for diagnostics and prognostics, or both. Some newer references about predictive maintenance can be found in Refs. [10–12]. No matter what is the goal of predictive maintenance, three key steps must be followed for its implementation: (1) data acquisition, (2) data processing and (3) maintenance decision-making.

Data acquisition is the process of data collection from specific physical resources in order to implement predictive maintenance properly. This process is the key step in applying predictive maintenance, both for diagnostics and for prognostics. Collected data can be classified into two major categories: event data and condition-monitoring data. Event data include information about what happened (faults, repairs, what were the causes, etc.). Condition-monitoring data are the measurements about physical resource 'health condition'.

The first step in data processing is data cleaning. This step is very important, because data (especially event data), which are entered manually always, have some mistakes. Without data cleaning, it is possible that diagnostics and prognostics will be inaccurate. The next step in data processing is data analysis. Different models, algorithms and tools for data analysis depend mostly from data type [5]. Condition-monitoring data can be classified into three categories: (1) value type, (2) waveform type and (3) multidimensional type.

The last step in predictive maintenance programme is decision-making. Techniques for decisionmaking can be divided into two categories: diagnostics and prognostics. It is obvious that prognostics is superior in regard to diagnostics, because it can prevent failure to occur, and if it is possible it provides spare parts and planned human resources for problems that will occur. In this way, it is possible to reduce material losses and avoid catastrophic failures. However, prognostics cannot replace diagnostics completely, because in practice there will be always some unpredictable faults.

Here, we focus on prognostics. There are two types of prediction when we talk about failure prognostic. The first type is the prediction of how much time is left before failure will occur (one or more failures) depending on the current state of the machine and past operation profile. Time that is left before the fault is noticed is called remaining useful life (RUL). In some situations, especially when failure is catastrophic (e.g. nuclear plant), it is much a preferable second type of failure prognostic, that is, prediction of probability that the machine will work until some future time (e.g. until next interval when inspection is needed) depending on the current state of the machine and past operation profile. Actually, in any situation, it is good to know the probability that a machine will work without failure until the next inspection or condition monitoring. Most papers deal with the first type of failure prognostic, that is, with RUL estimation [13, 14]. Only few papers can be found that deal with the second type of prognostic [15]. According to Ref. [8], failure prediction can be divided into three different categories:


Every one of these approaches has some advantages and limitations. Combinations of these approaches are different according to their applicability, price, precision and complexity [20].
