Process-Oriented Approach for Analysis of Sensor Data from Longwall Monitoring System

Brzychczy, Edyta; Trzcionkowska, Agnieszka

doi:10.1007/978-3-319-97490-3_58

Cited by 15 publications

(5 citation statements)

References 19 publications

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“…Several studies [22,23] have demonstrated the creation of event logs from low-level data from mine monitoring systems and domain knowledge to overcome this challenge. Brzychczy and Trzcionkowska [24] presented a methodology for the generation of appropriate event logs from sensors in underground mines, given the low level of abstraction of the data available in the monitoring systems of the longwall.…”

Section: Process Mining In the Mining Industrymentioning

confidence: 99%

Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study

et al. 2023

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Inefficiencies in mine equipment maintenance processes result in high operation costs and reduce mine sustainability. However, current methods for process optimization are limited due to a lack of access to structured data. This research aims to test the hypothesis that process mining techniques can be used to optimize workflow for mine equipment maintenance processes using low-level data. This is achieved through a process-oriented analysis where low-level data are processed as an event log and used as input for a developed process model. We present a Discrete-Event Simulation of the maintenance process to generate an event log from low-level data and analyze the process with process mining. A case study of the maintenance process in an underground block caving mine is used to gain operational insight. The diagnosis of the mine’s maintenance process showed a loss of 23,800 equipment operating hours per year, with a non-production cost of about 1.12 MUSD/year. Process mining obtained a non-biased representation of the maintenance process and aided in identifying bottlenecks and inefficiencies in the equipment maintenance processes.

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Section: Process Mining In the Mining Industrymentioning

confidence: 99%

Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study

et al. 2023

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“…This is because the technology enables a section of the mine workings to be recreated, where the consequences of noncompliance with health and safety regulations can be demonstrated including a realistic visualisation of the damage caused during an accident [96]. Of importance is the degree and quality of the representation of virtual reality in the eyes of the user and the level of its sensation, including touch [97], and the possibility of integrating into this reality data from measurement sensors located in the excavation site [98]. One of the limitations of implementing VR and AR in mining is the lack of a 3D model of the workings.…”

Section: Augmented Reality and Virtual Realitymentioning

confidence: 99%

Mining Geomatics

Krawczyk

2023

IJGI

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This paper attempts to define a name for an area of science and technology that encompasses the acquisition, processing and application of spatial data in the mining industry. A comparative study of the evolution of spatial data exchange methods between Geographic Information Systems (GISs) and General Mining Planning (GMP) software is carried out to define the problem and name it. Subsequent modifications of the acronym GIS towards the specialisation of its application in mining are then reviewed. This is followed by the identification of three terminological postulates designed to formulate constraints and rules for the creation of a new definition. The subsequent analysis identifies the nomenclatural basis of the research area of geomatics and determines its applicability in the context of mining. The results of the research made it possible to formulate a new definition of “mining geomatics”. The final section of the article presents an initial proposal for an inventory of the basic concepts of mining geomatics in the form of a Body of Knowledge for mining geomatics.

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“…But they did not allow to discover meaningful activities for our data set. For unlabelled training sets, related approaches suggest to use a time-based label refinement [24] or locations [25] as characteristics in order to segment the event log and to abstract activities out of it. However, the methods already expects particular representations of traces.…”

Section: Related Workmentioning

confidence: 99%

Process Model Discovery from Sensor Event Data

Janssen

Mannhardt

Koschmider

et al. 2021

Lecture Notes in Business Information Processing

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Virtually all techniques, developed in the area of process mining, assume the input event data to be discrete, and, at a relatively high level (i.e., close to the business-level). However, in many cases, the event data generated during the execution of a process is at a much lower level of abstraction, e.g., sensor data. Hence, in this paper, we present a novel technique that allows us to translate sensor data into higher-level, discrete event data, thus enabling existing process mining techniques to work on data tracked at a sensory level. Our technique discretises the observed sensor data into activities by applying unsupervised learning in the form of clustering. Furthermore, we refine the observed sequences by deducing imperative sub-models for the observed discretised data, i.e., allowing us to identify concurrency and interleaving within the data. We evaluated the approach by comparing the obtained model quality for several clustering techniques on a publicly available data-set in a smart home scenario. Our results show that applying our framework combined with a clustering technique yields results on data that otherwise would not be suitable for process discovery.

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Process-Oriented Approach for Analysis of Sensor Data from Longwall Monitoring System

Cited by 15 publications

References 19 publications

Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study

Towards the Application of Process Mining in the Mining Industry—An LHD Maintenance Process Optimization Case Study

Mining Geomatics

Process Model Discovery from Sensor Event Data

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