Continuous production maintenance cost is among one of the highest operational expenses for manufacturing companies. Proper planning of maintenance interventions results in optimized equipment use, higher product quality, and reduced costs. For a belt drive usefulness, it is important that it is properly stretched and has no defects. However, manual condition assessment requires a production line stop, which in turn causes production to stop with associated consequences. Continuous fault diagnosis for anomalies is a fundamental step in estimating a component’s remaining service life and then obtaining a reliable predictive maintenance system that reduces production costs. The presented work presents an approach to anomaly detection based on the vibrations obtained from the operation of the belt transmission.
This paper presents the application of an artificial neural network with a genetic algorithm for identifying the selected specification parameters of a voltagecontrolled oscillator (VCO). In modern electronics, the complexity of the production process may cause errors in analogue and mixed-signal electronic circuits, and inaccuracies in this technological process have a direct impact on the specification parameters of a VCO. The modern market requires that the production process has to be as quick as possible, and therefore testing systems should be fast and have the highest efficiency of parameter identification. In the following paper, a genetic algorithm is used to optimise the number of output signal measurement points, which allows them to be identified by the specification parameters of the VCO that are selected by an artificial neural network. The proposed method is characterised by shortening the test time of the system while maintaining a high efficiency in the identification of the selected design specification parameters.
For a long time, scientific and technical work has been focused on production management, which affects both the correctness of the process and the costs generated. One of the integral elements of the production process management is energy, which has an impact on the organization of work, operation of machines or production. Predicting the energy consumption of smart facilities is crucial for implementing energy-efficient management systems, the area of this problem is a key aspect of smart grids whereby loads must be planned in real time. One of the main tasks of intelligent systems is to optimize the energy demand and costs to maximize energy efficiency of the facility. According to forecasting requirements, the following article presents several approaches to prediction of energy consumption models for production engineering systems. The proposed models were adopted and analyzed in terms of their usability and were trained and validated with the use of real data collected from the electrical installation of some company using the APA IPOE system.
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