Artificial neural network approach for locomotive maintenance by monitoring dielectric properties of engine lubricant
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KünyeAltıntaş, O., Aksoy, M., Ünal, E., Akgöl, O., Karaaslan, M. (2019). Artificial neural network approach for locomotive maintenance by monitoring dielectric properties of engine lubricant. Measurement: Journal of the International Measurement Confederation, 145, pp. 678-686. https://doi.org/10.1016/j.measurement.2019.05.087
In this paper, we proposed an approach for locomotive maintenance systems by observing engine lube oil. The mechanical particles in lube oil give information about locomotive engine system condition. The engine lubricant is monthly monitored by a spectral analyzer (SA) to detect engine system failure and routine maintenance time. However, this old fashioned technique has many disadvantages such as non-real time measuring, high cost and time consumption. A novel approach is proposed to eliminate these disadvantages. The new method determines the lubricant sample conditions with respect to electrical characteristics by using artificial neural network (ANN). The study focuses on a relationship between mechanical particles (in ppm) and dielectric characteristics of the lube oil samples. Therefore, ANN method is applied to observe linear relation between observed and predicted dielectric constant and loss factor values of the engine oil samples. The electrical characteristics of the samples are observed at four frequency points (2.40 GHz, 5.80 GHz, 7.40 GHz and 9.60 GHz). ANN studies are realized by using data at these frequency points. The regression (R) coefficients are obtained as 0.7239, 0.7951, 0.8513 and 0.7463 for dielectric constant and 0.7627, 0.7196, 0.8015 and 0.7334 for dielectric loss, respectively. Moreover, the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are calculated and examined. The obtained results are very sufficient and this approach can be applied to a sensor device having low cost and real time working mechanism in the future. (C) 2019 Elsevier Ltd. All rights reserved.