gyroscopyГироскопия и навигацияGiroskopiya i Navigatsiya0869-70352075-0927AO «Концерн «ЦНИИ «Электроприбор»10.17285/0869-7035.0024gyroscopy-205Research ArticleСтатьиОбнаружение контекстных неисправностей в беспилотных летательных аппаратах с использованием динамической линейной регрессии и классификации методом k-ближайших соседейDetecting Contextual Faults in Unmanned Aerial Vehicles Using Dynamic Linear Regression and K-Nearest Neighbour ClassifierАлосА.AlosA.

Алос Ахмад. Аспирант, кафедра информатики.

ДахруджЗ.DahroujZ.

Дахрудж Зухайр. Доктор наук, преподаватель, кафедра информатики.

Высший институт прикладных наук и технологий (Дамаск, Сирия).СирияDepartment of Informatics, Higher Institute for Applied Sciences and Technology, Damascus, SyriaSyrian Arab Republic2020281020252816680Copyright © Алос А., Дахрудж З., 20252025Алос А., Дахрудж З.Alos A., Dahrouj Z.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.gyroscopy.ru/jour/article/view/205

Беспилотный летательный аппарат (БПЛА) представляет собой сложную систему, при проектировании которой рассматриваются проблемы управления, аэродинамики и связи. В статье предложен новый метод обнаружения контекстных неисправностей на основе сложных линейных соотношений между параметрами БПЛА (показаниями датчиков и командами). Под контекстными неисправностями подразумеваются вырабатываемые неисправным датчиком значения, недопустимые в контексте других параметров. Предлагаемый подход основан на оценке значений целевого параметра с использованием динамической линейной регрессии, после чего выполняется расчет погрешности оценивания на каждом временном интервале. Путем классификации методом ближайших соседей (K-Nearest Neighbour – K-NN) значения погрешности оценивания разделяются на нормальные и аномальные. Аномальные значения принимаются за потенциально ошибочные. Помимо этого, предложенный метод сравнивается с другими методами поиска аномалий – k-средних (K-Means) и One-Class SVM. Результаты сопоставления продемонстрировали более высокую эффективность нового подхода в большинстве случаев.

Unmanned aerial vehicle (UAV) is a complex system. Its design involves control, aerodynamics, and communication systems. We use the complex linear relationships among UAV attributes (sensor readings, and commands) to propose a new technique to detect contextual faults. The contextual faults mean that a defective sensor shows invalid values concerning the context of other attributes. The proposed approach depends on estimating the values of a focused attribute using dynamic linear regression. Next, it calculates the estimation error at each time step. The values of the estimation error are classified using K-NN (Nearest Neighbour) classifier into two classes (Normal, Abnormal). The abnormal points are flagged as potential faults. Moreover, comparison with other algorithms (K-Means and One-Class SVM) is made. The proposed approach showed better results in most of the cases.

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The authors declare that there are no conflicts of interest present.