A Novel Application of Intelligent Algorithms in Fault Detection of Rudder System

The rudder system is extensively used in aerospace, ships, missiles and other safety demanding areas. Therefore, it is paramount to ensure that the performance of the system is optimal. Rudder system testing equipment is a special tool used to diagnose its failure. Traditional ones can only artificially analyze the massive and complex tested data. Due to the low-test degree of automation, the performance of such testing tools is limited. Aiming to address this shortcoming, we developed a new rudder system testing equipment with four independent loading platforms and intelligent data analysis systems. It sufficiently shortens the installation and commission time of pneumatic actuators and the processing time of the testing data which largely improves its performance and accuracy. Given the imbalanced nature of the data an adaptive sampling algorithm considering informative instances (ASCIN) leveraging the Support Vector Machine (SVM) is proposed to process the originally collected data. The optimal parameters in SVM and ASCIN are searched by Whale Optimization Algorithm (WOA). Experiments are designed to assess the performance of ASCIN in comparison with existing approaches in the area of imbalanced data learning. The results show that the algorithm developed in this study has higher performance relative to traditional approaches. The application of these intelligent algorithms in fault detection and location of rudder system overcomes the limitation of traditional testing equipment and provides a new concept for future research into more intelligent one.