Currently, measuring either vehicle’s mileage or duration or either one does maintain lubricant viscosity. However, these judgments are inaccurate because there are many other factors like conductivity, humidity and viscosity that may affect the oil quality. This paper proposed one theory of monitoring viscosity quality with Neural Network (NN) modelling by introducing factors like temperature, shear stress and pressure. One deterministic objective will be highlighted that is to develop NN modelling based on those three factors. This research also introduces normalization approach called Along Channel and logarithmic function due to various range of data input. NN modelling, an off-line system is explicitly designed with Backpropagation Algorithm and Multilayer Feedforward Network for learning process while its weight is calculated based on Nguyen Widrow number and Genetic Algorithm. There were 310 sample data, which divided into two sets; 149 data for training set and the rest for testing and vice versa. The application performance has achieved up to 85.91% result approaching real viscosity value.
['Siti Norul Huda Sheikh Abdullah', 'Khairuddin Omar', 'Siti Rozaimah Sheikh Abdullah', 'Norsalina Harun', 'Mohd Syarif Afriansyah Lubis', 'Che Hassan Che Haron', 'Kamsuriah Ahmad', 'Mohd Zakree Ahmad Nazri', 'Mohammad Faidzul Nasrudin', 'Lee Chin Sin', 'Abdul Sahli Fakhrudin', 'Mohd Esa Baruji']
Recognizing vehicle lubricant oil quality via neural network