During the movements of aircraft, missiles, and ships, the rudder plays an important role in their direction control. In order to test the parameters of the rudders, we have to manually measure each item one by one in traditional production and manufacturing of rudders, which waste a great quantity of manpower and time. In this paper, we present a new application in rudder fault test by using machine learning technology and recommend a new intelligent method for fault location. The main subject revolves around prediction-oriented problems of multi-dimensional performance parameters data mining and the modeling of classification, including the analysis and processing of data features and the solution of fault location based on classification model. In addition, to improve the accuracy of the classification model, we optimized the random forest (RF) algorithm with the shuffled frog leaping algorithm (SFLA), which we call shuffled frog leaping algorithm-based random forest (SFLA-RF). It effectively solves the problem of voting competition among each tree, which makes the decision of the model more efficient and accurate. In a word, by means of automatic test and intelligent analysis, this new method breaks through the technical bottleneck of low efficiency of parameters test and the shortcomings of traditional rudder fault location.

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Rudders are the important components of aircrafts, missiles and ships, and their traditional test equipment is not intelligent enough, so we have to evaluate their performance by observing every parameter manually. This situation makes it impossible to test the rudders rapidly and in quantity. In this paper, we present a new application in the field of rudder test based on machine learning (ML) and describe new methods for performance evaluation and state prediction. The main topics are concentrated on prediction-oriented problems of multiple performance data mining and modeling: analysis and extraction of data feature, performance scoring based on regression algorithm and cross-validation, screening of defective products and fault location based on classification algorithm and accuracy evaluation. Besides, we propose a new optimized decision tree algorithm (SFLA-MWDT) which solves the common decision difficulty in tree models caused by low-precision decision and high-vote competition. Here, through ‘automatic acquisition + intelligent analysis’, we break through the shortcomings of traditional rudder testing methods and technical bottleneck of low parameter testing efficiency. This test method is applicable to those rudders that have already produced but not yet in use. Also, it provides guidance for the production and practice of rudders.

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. INDEX TERMS Rudder system, fault diagnosis, intelligent algorithm, data analysis.