Magnetic cooling and heating presents a large potential to phase out existing refrigerants and related technologies in many different domains and different market niches. Substantially increased research activities in the last two decades led to development of about 40 prototypes worldwide. Despite strong RandD efforts are still required, the technology is ready to be developed for certain special market niches. Existing limits of the technology may be surpassed by the focus on particular domains and operation characteristics. One of such are embedded energy systems. The embedded energy system presents an intelligent grid of integrated devices and subsystems. These are interconnected by the mechanical, fluidic and electric interfaces, strongly supported by the information and the communication technology. The article deals with different possible applications for the magnetocaloric technology in embedded energy systems. The results give an important basis for the further research and development toward earlier possible market applications.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 11955483In order to detect incipient failures in large-size low-speed rolling bearings where subjective influence would play a minor role, a non-linear multivariate and multiscale statistical process monitoring and signal denoising method has been developed. The method which combines the strengths of the non-linear multivariate Kernel Principal Component Analysis (KPCA) monitoring approach with the benefits of Ensemble Empirical Mode Decomposition (EEMD) to handle multiscale system dynamics was named the EEMD-based multiscale KPCA (EEMD-MSKPCA). The multiscale and non-linear nature of the developed approach makes the method convenient for dealing with data emanating from complex real-world applications which usually represent the cumulative effect of many underlying phenomena occupying different regions in the time-frequency plane and also reflect non-linear characteristics. Its efficiency and applicability for the task of bearing fault detection and signal denoising were tested on simulated as well as actual vibration and acoustic emission (AE) signals measured on a purpose-built large-size low-speed bearing test stand. The positive results obtained indicate that AE and vibration signals carry sufficient information on the bearing condition and that the developed EEMD-MSKPCA method enables detection of small, yet significant events on individual scales and thus ensures high-reliability bearing fault detection.
B.03 Paper at an international scientific conference
COBISS.SI-ID: 11954971