Prediction model of remaining operating time until critical state based on engine oil analysis records with data mining solution

One of the important aspects of Condition Based Maintenance (CBM) is the prediction of remaining useful life (RUL) based on past records and current state of the device and lubricant oil analysis is one of the methods of CBM which due to its direct contact with the device, its condition expresses the device's health. In the CBM process a large mass of data is generated and accumulated, but the knowledge included in this data cannot be fully understood and result in the loss of valuable resources. To extract information and knowledge from these data, it is necessary to use methods such as data mining. In this study, based on the definition of RUL, the best prediction model of remaining operating time for a bulldozer model until critical state has been created with data mining solution based on engine oil analysis records (dataset with 2700 records and 129 features). To create the best model, regression and neural network models have been created after preparing the proper dataset with 49 records and 4 features. Due to the feasibility of oil change at sampling intervals, the models have been created using two methods of applying independent features values. Based on the performance evaluation of the models, the best model with neural network and the second method of applying independent features values have been created with prediction error 958559.033 +/- 23526.662, which are to use new values (cumulative) of two independent features (Fe, Cu) and the actual value (non-cumulative) of an independent feature (Vis40)