Improved Random Forest Fault Diagnosis Model Based on Fault Ratio

Abstract With the rapid development of information technology, informatization, integration and complexity more large equipment are increasing day by day, so it is very important to carry out fault diagnosis for such complex equipment. In traditional way, expert system technology usually used However, with data information, methods cannot solve requirements in case a amount data. Therefore, data-driven method can this problem, The carrier engineering data, its focus explore new from historical paper, classical random forest algorithm selected as basic model, aiming at imbalance improved voting mechanism based on ratio proposed optimize which makes final model accuracy than 95%, has good application value.