# An Approach to Supporting the Selection of Maintenance Experts in the Context of Industry 4.0
## 3 An Approach to Selecting a Maintenance Expert  
The proposed approach to presenting the selection of maintenance experts--based on their competences--for repairs within a manufacturing resource provides an opportunity to denote a particular worker as the expert worker within the maintenance department. The approach to selecting a maintenance expert (Figure 1) is in line with the concept of reliability-centered maintenance (RCM). RCM can be treated as the reactive, preventive, and proactive maintenance practices that are introduced within a company [26]. It is also the approach to capturing the reason of downtime using two stages: (1) determine the critical components of the system and (2) application of decision rules to define categories of predictive maintenance (PM) [27]. The construction of the proposed approach corresponds to five stages defined in RCM process [28]:  
* Selection of subsystem: maintenance competence management. * Identification of component: defining the types of failure for each manufacturing resource and each competence, which has a considerable influence on reliability (stages 1-3, Figure 1). * Analysis: defining the importance of each competence for the repair of each type of failure (stage 4, Figure 1). * Optimal maintenance strategy selection: defining the \"state of nature\" and the implementation of maintenance expert selection map (stages 5-6, Figure 1). * Analysis: the selection of this employee to repair a given resource, who guarantees an increase in the reliability level of a given manufacturing resource. Each stage of the proposed approach must be formalised so that it can be computerised according to the Industry 4.0 concept. The construction of the proposed approach is possible due to the acquisition and gathering of knowledge from the database of the information systems implemented within a company (stages 1-2, Figure 1) and the unique knowledge of employees performing activities in the maintenance department (stages 3-4, Figure 1). However, for acquired expert knowledge, so-called, to be useful, it needs to be represented and codified by data with a specified format. Accordingly, it is stated, at each stage of our approach, knowledge is defined, then acquired, and, finally, is then stored. Acquired knowledge must be converted into extracted and explicit knowledge, so that it can be computerised. This can be done [29] by using the frames based systems [30], frame logic [31], semantic networks [32], and conceptual graphs, with these being methods based on concept dictionaries, viz., ontologies [29], and methods that are based on established rules. The rule-based method was selected in order to create a formalised base for the approach (Figure 1). Stages one to four were based on the literature research results [3  
The rule for formalising the acquired data from the information system implemented, is defined in Table 1: If there is a failure in the manufacturing resource then the value of Fj = 1, if not Fj = 0, j = 1,..., 5. In stage 2, (Figure 2) the following parameters of each type of failure Fj, j = 1,..., 5 for each manufacturing resource: Ri, i,j\(\epsilon\)N are defined: P = [P1, P2, P3] (Table 2), where k\(\epsilon\)N  
* P1--time for diagnosing and finding the solution. * P2--maintenance operation time. * P3--time for testing. The rules for formalising the acquired data from the information system implemented is defined (Table 2):  
In stage 3, (Figure 1) the competences of each maintenance worker: Wt, Wt\(\epsilon\) {W1, W2,..., Wt}, teN are defined: C = [C1, C2, C3, C4, C5], where:  
* C1--Hard skills. * C2--Knowledge-based. * C3--Methodical. * C4--Soft Skills. * C5--Experience. Assessing workers' knowledge is not an easy task; moreover, the quality and scope of this knowledge is crucial to it being able to be repeatedly used. The following method for assessing knowledge has been distinguished [37]: questions with a defined set of choices [38; 39], rating grids