Metaheuristic Algorithm-Based Vibration Response Model for a Gas Microturbine.

Data acquisition and processing are areas of research in fault diagnosis in rotating machinery, where the rotor is a fundamental component that benefits from dynamic analysis. Several intelligent algorithms have been used to optimize investigations of this nature. However, the Jaya algorithm has only been applied in a few instances. In this study, measurements of the amplitude of vibration in the radial direction in a gas microturbine were analyzed using different rotational frequency and temperature levels. A response surface model was generated using a polynomial tuned by the Jaya metaheuristic algorithm applied to the averages of the measurements, and another on the whole sample, to determine the optimal operating conditions and the effects that temperature produces on vibrations. Several tests with different orders of the polynomial were carried out. The fifth-order polynomial performed better in terms of MSE. The response surfaces were presented fitting the measured points. The roots of the MSE, as a percentage, for the 8-point and 80-point fittings were 3.12% and 10.69%, respectively. The best operating conditions were found at low and high rotational frequencies and at a temperature of 300 ∘C. High temperature conditions produced more variability in the measurements and caused the minimum value of the vibration amplitude to change in terms of rotational frequency. Where it is feasible to undertake experiments with minimal variations, the model that uses only the averages can be used. Future work will examine the use of different error functions which cannot be conveniently implemented in a common second-order model. The proposed method does not require in-depth mathematical analysis or high computational capabilities.

Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine.

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 concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85-93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.

Regression models to predict the behavior of the coefficient of friction of AISI 316L on UHMWPE under ISO 14243-3 conditions.

Friction is the natural response of all tribosystems. In a total knee replacement (TKR) prosthetic device, its measurement is hindered by the complex geometry of its integrating parts and that of the testing simulation rig operating under the ISO 14243-3:2014 standard. To develop prediction models of the coefficient of friction (COF) between AISI 316L steel and ultra-high molecular weight polyethylene (UHMWPE) lubricated with fetal bovine serum dilutions, the arthrokinematics and loading conditions prescribed by the ISO 142433: 2014 standard were translated to a simpler geometrical setup, via Hertz contact theory. Tribological testing proceeded by loading a stainless steel AISI 316L ball against the surface of a UHMWPE disk, with the test fluid at 37 °C. The method has been applied to study the behavior of the COF during a whole walking cycle. On the other hand, the role of protein aggregation phenomena as a lubrication mechanism has been extensively studied in hip joint replacements but little explored for the operating conditions of a TKR. Lubricant testing fluids were prepared with fetal bovine serum (FBS) dilutions having protein mass concentrations of 5, 10, 20 and 36 g/L. The results were contrasted against deionized, sterilized water. The results indicate that even at protein concentration as low as 5 g/L, protein aggregation phenomena play an important role in the lubrication of the metal-on-polymer tribopair. The regression models of the COF developed herein are available for numerical simulations of the tribological behavior of the aforementioned tribosystem. In this case, surface stress rather than film thickness should be considered.

The coefficient of friction of UHMWPE along an entire walking cycle using a ball-on-disc tribometer under arthrokinematics and loading conditions prescribed by ISO 14243-3:2014.

The observation of tribological phenomena occurring in total knee replacement (TKR) simulators may be obscured by the intrinsic complexity of their operation: the dynamics and kinematics prescribed by the ISO 14243-3:2014 standard, and the geometry of the surfaces involved. On the other hand, evaluating the individual performance of the tribosystem elements may be carried out in simpler apparatuses. An experimental method is presented here, by means of which the arthrokinematics and loading conditions prescribed by the said standard are adapted to a ball-on-disc configuration in order to observe the behavior of the coefficient of friction along an entire walking cycle, using the contact point of an AISI 316L stainless steel ball rolling/sliding on an ultra-high molecular weight polyethylene (UHMWPE) disc, lubricated by a solution of fetal bovine serum, at 37°C. The method was tried on two different testing fluids prepared with protein concentrations of 20g/L, according to the said standard, and 36g/L, as received. The statistical model obtained for the behavior of the COF during the entire walking cycle may be used in numerical simulations of UHMWPE wear, under the conditions established by ISO 14243-3:2014.