Abstract
Fast-growing methods of automatic data acquisition allow for collecting various types of data from the production process. This entails developing methods that are able to process vast amounts of data, providing generalised knowledge about the analysed process. Appropriate use of this knowledge can be the basis for decision-making, leading to more effective use of the company’s resources. This article presents the approach for data analysis aimed at determining the operating states of a wheel loader and the place where it operates based on the recorded data. For this purpose, we have used several methods, e.g., for clustering and classification, namely: DBSCAN, CART, C5.0. Our approach has allowed for the creation of decision rules that recognise the operating states of the machine. In this study, we have taken into account the GPS signal readings, and thanks to this, we have indicated the differences in machine operation within the designated states in the open pit and at the mine base area. In this paper, we present the characteristics of the selected clusters corresponding to the machine operation states and emphasise the differences in the context of the operation area. The knowledge obtained in this study allows for determining the states based on only a few selected most essential parameters, even without consideration of the coordinates of the machine’s workplace. Our approach enables a significant acceleration of subsequent analyses, e.g., analysis of the machine states structure, which may be helpful in the optimisation of its use.
Highlights
Nowadays, companies are looking for innovative methods and techniques to maximise the efficiency of their operations and optimise the usage of their fixed assets
This paper proposes a method to identify workstations for a group of heavy vehicles, including wheel loaders, excavators and dump trucks based on Global Positioning System (GPS) data
Ourand dataDiscussion set with 18 variables was analysed with library dbscan [62]
Summary
Companies are looking for innovative methods and techniques to maximise the efficiency of their operations and optimise the usage of their fixed assets. Very detailed and precise machine-specific data are available to companies, characterising the operation of all the main components of the machine, often recorded continuously. This constitutes a valuable opportunity to understand a machine’s performance from a broader perspective, trying to discover new patterns and specific work behaviours based on the analysis of its various parameters. As a result of the acquired knowledge, the company may undertake real changes of unfavourable work parameters and, obtain notable benefits such as reduction of fuel consumption, an extension of the machine’s operating time, or minimisation of extreme and dangerous states of the machine’s operation
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