A comparative analysis of gaussian and trapezoidal membership functions for the assessment of the technical condition of a locomotive diesel engine based on ANFIS
Keywords:
ANFIS, locomotive diesel engine, technical condition assessment, Gaussian membership function, trapezoidal membership function, Sugeno fuzzy system, technical diagnosticsAbstract
This article reviews the problem of technical condition assessment of locomotive diesel engine using Adaptive Neuro-Fuzzy Inference System (ANFIS). The research is conducted using real operational data from a 2TE25KM series locomotive using the MCS (Microprocessor-based Control System). The following two diagnostic models were developed: oil pressure prediction model as well as exhaust gas temperature-based inter-cylinder imbalance prediction model. Gaussian and trapezoidal membership functions were used individually for each model and compared with the RMSE, MAE and correlation coefficient (R). Both R values were high and almost the same for the oil pressure model but for the exhaust gas model, the R value for the trapezoidal function (R = 0.51) was significantly more stable than the Gaussian function (R =−0.03). This shows trapezoidal membership functions superiority in small volume and noise diagnostic data.
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