The Early Fault Prediction of Industrial Equipment Based on Random Forest: A Case Study of Wind Turbine SCADA Data

Using computational intelligence to describe complex systems, predictive maintenance of industrial equipment is a major issue. This paper describes an interpretable computational intelligence framework based on random forests for generalized prediction of early failures of industrial equipment. To verify the efficacy of this framework, a typical complex system, wind turbine units, was taken up as a case for validation. Through SCADA data feature engineering, a model implemented Successful warnings of faults. The experimental results indicate that the framework has achieved a mean accuracy rate over 99% for those three types of major fault above. The main contribution of this work is to offer a computational intelligence approach that is high-precision, highly interpretable, and easily deployable. The possible applications of this solution are not limited to wind turbine units alone; its methodology can also be easily generalized to health management of other industrial equipment such as industrial robots and CNC machine tools to provide a new technical path for enhancing the autonomy of complex electromechanical systems.