Low-Altitude Status Monitoring and Fault Diagnosis Algorithm for Aircraft Equipment Based on Big Data Mining and Analysis

Authors

  • Wenjing Yin School of Transportation Engineering, Weifang Engineering Vocational College, Qingzhou 262500, Shandong, China
  • Sichao Lv School of Transportation Engineering, Weifang Engineering Vocational College, Qingzhou 262500, Shandong, China
  • Xiaofei Li School of Marxism, Weifang Engineering Vocational College, Qingzhou 262500, Shandong, China

Abstract

Themonitoring and fault diagnosis of aircraft equipment at low-altitudes refers to the monitoring of the relevant parameters of various operating states of aircraft equipment, effectively determining its operational status based on existing data, and the diagnosis of any faults, so that appropriate technical remedial measures can be taken. This study examines the analysis and application of aircraft equipment low-altitude state monitoring and fault diagnosis methods based on big data mining (DM), and establishes relevant systems. The parameters of aircraft equipment were analyzed by applying association rules, whereby the rules governing the operation of equipment were found. The verification analysis and experimental data of the system
showed that in the test set, when the number of aircraft equipment fault samples was 200, the correct number of fault samples for the traditional system and the system proposed in this paper was 171 and 191 respectively. The fault diagnosis time of the two systems was 1.127s and 0.562s respectively. This indicates that the monitoring and fault diagnosis based on big DM analysis of aircraft equipment in the low-altitude condition has certain feasibility and merits further promotion and application.

Keywords: aircraft equipment, big data mining analysis, fault diagnosis algorithm, low altitude condition monitoring

Cite As

W. Yin, S. Lv, X. Li, "Low-Altitude Status Monitoring and Fault Diagnosis Algorithm for Aircraft Equipment Based on Big Data Mining and Analysis", Engineering Intelligent Systems, vol. 34 no. 3, pp. 375-384, 2026.

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Published

2026-05-01

Issue

Vol. 34 No. 3 (2026): International Journal of Engineering Intelligent Systems

Section

Article

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