Data Analytic Approaches for Mining Process Improvement—Machinery Utilization Use Case

Abstract

This paper investigates the application of process mining methodology on the processes of a mobile asset in mining operations as a means of identifying opportunities to improve the operational efficiency of such. Industry 4.0 concepts with related extensive digitalization of industrial processes enable the acquisition of a huge amount of data that can and should be used for improving processes and decision-making. Utilizing this data requires appropriate data processing and data analysis schemes. In the processing and analysis stage, most often, a broad spectrum of data mining algorithms is applied. These are data-oriented methods and they are incapable of mapping the cause-effect relationships between process activities. However, in this scope, the importance of process-oriented analytical methods is increasingly emphasized, namely process mining (PM). PM techniques are a relatively new approach, which enable the construction of process models and their analytics based on data from enterprise IT systems (data are provided in the form of so-called event logs). The specific working environment and a multitude of sensors relevant for the working process causes the complexity of mining processes, especially in underground operations. Hence, an individual approach for event log preparation and gathering contextual information to be utilized in process analysis and improvement is mandatory. This paper describes the first application of the concept of PM to investigate the normal working process of a roof bolter, operating in an underground mine. By applying PM, the irregularities of the operational scheme of this mobile asset have been identified. Some irregularities were categorized as inefficiencies that are caused by either failure of machinery or suboptimal utilization of the same. In both cases, the results achieved by applying PM to the activity log of the mobile asset are relevant for identifying the potential for improving the efficiency of the overall working process.

A Knowledge Graph System for the Maintenance of Coal Mine Equipment

“…e output of the BiLSTM layer is used as the input of the CRF layer, which makes the whole model consider the correlation among class labels. Get the edge E p corresponding to k p (7) Determine whether E p exists in the database; if not, then create Rel (E p ) (8) Judge the traversal result: if i ≥ n, end; if i < n, go to the second step (9) Complete the storage of the CMEM ontology content ALGORITHM 1: Knowledge storage graph algorithm.…”

“…To solve the above problems effectively, at present, some foreign experts and scholars have analyzed and studied the big data of CMEM. Edyta Brzychczy applied the concept of PM for the first time to study the normal working process of roof bolters working in underground mines so as to improve the operation efficiency of assets [8]. Kinga Stecuła used the intelligent informatics module for registration and assessment of causes of breaks in selected mining machines [9].…”

The Status Quo of Process Mining in the Industrial Sector

“…The production operators decide which machines take part in the assembling. Bhogal and Garg [40] Brzychczy et al [41] Celonis AG [42] Celonis AG [43] Denno et al [44] Abonyi and Dörgő [45] Engel and Bose [46] ER et al [47,48] Fleig et al [49] Fluxicon [22,50] Ho and Lau [51] Ho et al [52] Knoll et al [53] Koosawad et al [54] Lee et al [32,55]; Park et al [33,56] Meincheim et al [57] Nagy et al [58,59] Park et al [10] Paszkiewicz [60] Pospíšil et al [34] QPR Software Plc [61] QPR Software Plc [62] Rozinat et al [30,63] Ruschel et al [31] Saravanan and Rama Sree [64] Ulsan Institute [65]; Son et al […”

“…Computer parts D, C [65,66] Automotive parts D, C [43] Mining machine operation D [41] Semiconductors D [67] Electronic chips D, C [36][37][38] Oil refinement D [45] Automotive assembly D [44] Coils D [58,59] Fig. 1.…”

“…These cases employ different discovery strategies extended by stochastic approaches, time data, and sequence alignment to generate high-quality process models. One study investigates the application of process mining in mining operations to identify opportunities for operational improvement [41]. It describes the first application of process mining to investigate the working process of a roof bolter operating in an underground mine.…”