Sensors, Vol. 22, Pages 130: Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine

Sensors, Vol. 22, Pages 130: Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine Sensors doi: 10.3390/s22010130 Authors: Omar Rodríguez-Abreo Juvenal Rodríguez-Reséndiz L. A. Montoya-Santiyanes José Manuel Álvarez-Alvarado Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement ...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research