Abstract
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.
Highlights
In 1980s, the United States took the lead in proposing standardization, serialization and modularization of weapon equipment testing system, forming a series of automatic test equipment, such as integrated testing equipment for the Army (Integrated Family of Test Equipment, IFTE), a joint automatic support system for the Navy (Consolidated Automated Support System, CASS) [1], a joint military electronic combat system testing equipment for the Air Force (Joint Services Electronic Combat System Tester, JSECST) and a third echelon testing system for the Marine Corps (Third Echelon Test Set, TETS) [2]
In order to improve the accuracy of the model in fault location, we propose a new combination algorithm, namely: Shuffled Frog Leaping Algorithm-based Random Forest (SFLA-RF), whose principle is to use the shuffled frog leaping algorithm (SFLA) to assign a weight to each decision tree in the RF, so that the sub-tree with high accuracy has a higher weight, and the weight is depended on the performance of the sub-tree
In this paper, the data processing method of machine learning is added to the traditional rudder test equipment, which changes the current situation of manual observation and judgment, can further save the testing time of rudders and improve the testing efficiency
Summary
In 1980s, the United States took the lead in proposing standardization, serialization and modularization of weapon equipment testing system, forming a series of automatic test equipment, such as integrated testing equipment for the Army (Integrated Family of Test Equipment, IFTE), a joint automatic support system for the Navy (Consolidated Automated Support System, CASS) [1], a joint military electronic combat system testing equipment for the Air Force (Joint Services Electronic Combat System Tester, JSECST) and a third echelon testing system for the Marine Corps (Third Echelon Test Set, TETS) [2]. With the development of computer technology and bus technology, some powerful and automated test equipment have been designed. The stability and reliability of both military and civilian test equipment have been higher. The rudders must be tested before use to ensure that all parameters are in normal condition. The parameters that need to be tested can be roughly divided into state parameters, static characteristics and dynamic characteristics. The test equipment of rudders has been developed for a long time: The testing methods of the state parameters and static characteristics have not changed greatly, whereas the test methods of dynamic parameters have undergone several stages of changes. In the past few years, a set of data needs repeated measurements to be obtained, and frequency characteristic curves are drawn based on multiple sets of experimental data. With the popularity of computers and the emergence of common interfaces, a simple frequency testing system has emerged, and automatic testing has been
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