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

The motivation for this study was the need to extend and supplement the previously conducted research on technical objects in the renewable energy sector with analyses of the environmental impact of the production, operation and post-operational development stages of the wind power plant. The main purpose of the work was to investigate, analyze and assess the ecological effects of a real facility, which is a 2 MW Vestas V90/105 m wind farm, throughout its life cycle. The life cycle assessment analysis of the 2 MW wind power plant was performed using Impact 2002+ modeling. The results are presented for all impact levels and categories. The production stage was characterized by the highest total level of harmful effect. The use of recycling reduces the negative impacts of the life cycle by 6.5%. The investigated technical facility has the greatest negative impact during the production stage, especially in the area of depletion of fossil resources and human health.

The article presents a new thermo-mechanical machining method for the manufacture of long low-rigidity shafts which combines straightening and heat treatment operations. A fixture for thermo-mechanical treatment of long low-rigidity shafts was designed and used in tests which involved axial straightening of shafts combined with a quenching operation (performed to increase the corrosion resistance of the steel used as stock material). The study showed that an analysis of the initial deflections of semi-finished shafts of different dimensions and determination of the maximum corrective deflection in the device could be used as a basis for performing axial straightening of shaft workpieces with simultaneous heat treatment and correction of the initial deflection of the workpiece. The deflection is corrected by stretching the fibers of the stock material, at any cross-section of the shaft, up to the yield point and generating residual stresses symmetrical to the axis of the workpiece. These processes allow to increase the accuracy and stability of the geometric shape of the shaft.

Production scheduling is attracting considerable scientific interest. Effective scheduling of production jobs is a critical element of smooth organization of the work in an enterprise and, therefore, a key issue in production. The investigations focus on improving job scheduling effectiveness and methodology. Due to simplifying assumptions, most of the current solutions are not fit for industrial applications. Disruptions are inherent elements of the production process and yet, for reasons of simplicity, they tend to be rarely considered in the current scheduling models. This work presents the framework of a predictive job scheduling technique for application in the job-shop environment under the machine failure constraint. The prediction methods implemented in our work examine the nature of the machine failure uncertainty factor. The first section of this paper presents robust scheduling of production processes and reviews current solutions in the field of technological machine failure analysis. Next, elements of the Markov processes theory and ARIMA (auto-regressive integrated moving average) models are introduced to describe the parameters of machine failures. The effectiveness of our solutions is verified against real production data. The data derived from the strategic machine failure prediction model, employed at the preliminary stage, serve to develop the robust schedules using selected dispatching rules. The key stage of the verification process concerns the simulation testing that allows us to assess the execution of the production schedules obtained from the proposed model.