Abstract
The execution of production processes in real manufacturing systems is associated with the occurrence of numerous disruptions, which predominantly revolve around technological machine failure. Therefore, various maintenance strategies are being developed, many of which tend to emphasise effective preventive measures, such as the Time-Based Maintenance (TBM) discussed in this paper. Specifically, this publication presents the time-based machine failure prediction algorithm for the multi-machine manufacturing environment. The Introduction section outlines the body of knowledge related to typical strategies applied in maintenance. The next part describes an approach to failure prediction that treats processing times as makespan and is followed by highlighting the key role of historical data in machine failure management, in the subsequent section. Finally, the proposed time-based machine failure prediction algorithm is presented and tested by means of a two-step verification, which confirms its effectiveness and further practical implementation
Highlights
The reality of the production environment is inseparably connected with disruptions, which negatively impact the executed processes, leading to disorganisation [14]
It is crucial that these tools employ effective prediction algorithms, drawing from reliable historical data and providing the basis for a reliable analysis of machine failure and proper adjustment of maintenance activities [6, 13, 40]
The investigation works reported in this paper confirm the effectiveness of the developed prediction algorithm and indicate the need
Summary
The reality of the production environment is inseparably connected with disruptions, which negatively impact the executed processes, leading to disorganisation [14]. This study provides a novel approach to machine failure prediction in multi-machine manufacturing systems that employs an algorithm performing an in-depth, elaborate analysis of actual production data, enabling the prediction of future machine breakdowns and implementation of effective preventive measures. This method constitutes an alternative to those characterised in the preceding paragraphs as it makes use of data obtained from maintenance services to achieve the intended objective – identification of the potential moment of failure. The innovation of our method consists in its incorporation of elements of survival analysis theory in technological machine failure analysis enabling statistical inference based on historical data
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