Use of neuronets in problems of forecasting the reliability of electric machines with a high degree of mean time between failures

Abstract

It was investigated the possibility to implement neuronets in reliability models of electric machines with constructive faults. It was grounded the tasks for neuronets during forecasting the failure of main structural units and electric machines in general. The choice of neuronet structure was done as result of analysis of most promising models on the example of evaluation the bearing unit faults. Streszczenie. Przebadano możliwość implementacji sieci neuronowej w modelach niezawodnościowych maszyn elektrycznych z uszkodzeniami konstrukcyjnymi. Postawiono ugruntowane zadania dla sieci neuronowych podczas prognozowania uszkodzeń głównych jednostek konstrukcyjnych. Wyboru struktury sieci neuronowej dokonano w wyniku analizy najbardziej obiecujących modeli na przykładzie oceny uszkodzeń łożyska. (Wykorzystanie sieci neuronowych w prognozowaniu niezawodności maszyn elektrycznych z dużym średnim czasem między uszkodzeniami)

State-of-the-Art on Brain-Computer Interface Technology

“…Machine learning models such as artificial neural networks (ANNs) are used in BCI systems to help interpret and classify the brain signals that they receive [89][90][91]. By utilizing ANNs, a BCI system can be trained to recognize patterns in EEG data, which can then be used to detect changes in states of consciousness or other types of mental activities.…”

Automatic Failure Recovery for Container-Based IoT Edge Applications

“…However, there is currently limited work in applying these techniques to IoT and edge computing. For example, Engelhardt et al [37] investigated the MTBF for repairable systems by considering the reciprocal of the intensity function and the mean waiting time until the next failure; Kimura et al [38] looked at MTBF from an applied software reliability perspective by analysing software reliability growth models as described by non-homogenous Poisson process; in two separate works, Michlin et al [39,40] performed sequential MTBF testing on two systems and compared their performance; Glynn et al [41] proposed a technique for efficient estimation of MTBF in non-Markovian models of highly dependable systems; Zagrirnyak et al [42] discussed the use of neuronets in reliability models of electric machines for forecasting the failure of the main structural units (also based on MTBF); Suresh et al [43] unconvention-ally applied MTBF as a subjective video quality metric, which makes for an interesting evaluation of MTBF in other application areas other than literal system failures.…”

Models for electric machine reliability prediction at variation of the condition of basic structural units

“…It is possible to take into account these interrelations to the full by instruction of neural networks used in combination of separate models of structural units and elements [4]. Besides, this method allows obtaining calculation relations for reliability indices taking into consideration the time variation of EM failures intensity.…”