Abstract
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.
Highlights
The rudder system is widely used in aerospace and navigation fields, like in civil aircraft, ship and missile
Our proposed sampling algorithm ASCIN achieves the G-mean of 0.903, reflecting an improvement of 10.34% (0.903–0.444), 57.04% (0.903–0.575), 31.82% (0.903–0.685), 22.19% (0.903–0.739), 29.37% (0.903–0.698) and 15.37% (0.903–0.356) in terms of the average G-mean compared to the ones obtained with Synthetic Minority Oversampling Technique (SMOTE), Borderline Synthetic Minority Oversampling Technique (BSMOTE), LSMOTE, Clusteringbased Synthetic Minority Oversampling Method (CSMOTE), Adaptive Synthetic Sampling (ADASYN) and MWMOTE respectively
The algorithm ASCIN achieves the F-measure of 0.964, reflecting an improvement of 9% (0.964–0.884), 7% (0.964–0.901), 6.3% (0.964–0.907), 5.6% (0.964–0.913), 5.4% (0.964–0.915) and 9.7% (0.964–0.879) in terms of the average F-measure compared to the ones obtained using SMOTE, BSMOTE, Safe-Level Synthetic Minority Oversampling Technique (SLSMOTE), CSMOTE, ADASYN and MWMOTE, respectively
Summary
The rudder system is widely used in aerospace and navigation fields, like in civil aircraft, ship and missile. It is important to develop effective data analysis tools and learning methods for processing the features collected from the rudder system testing facility for better performance. Li et al.: Novel Application of Intelligent Algorithms in Fault Detection of Rudder System rudders and corresponding private circuit boards is highly controlled, the number of unqualified products is relatively small For this reason, the raw data collected from such testing facilities is imbalanced, with the number of normal features outnumbering that of fault features. The application of intelligent data analysis in rudder system testing equipment improves the degree of automation and the accuracy of diagnosing different types of faults It guides the production of steering gears and corresponding circuit boards. This function depends on the application of machine learning, and the detail are described as follows
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