Aircraft maintenance is a complex, multifaceted process that greatly benefits from IT systems designed to improve supervision, record keeping, and task management. This study focuses on the role of a dedicated mobile application, integrated into the broader IT Aircraft Maintenance Support System, which supports maintenance operations for the M-346 BIELIK training aircraft. Aircraft maintenance is a highly intricate and multifaceted process that significantly benefits from advanced IT systems designed to enhance supervision, streamline record keeping, and optimize task management. This study explores the pivotal role of a dedicated mobile application integrated into the broader IT Aircraft Maintenance Support System, specifically tailored to support the maintenance operations of the M-346 BIELIK training aircraft. By focusing on the analysis of Intelligent Transportation Systems (ITSs), the research highlights how the application contributes to maintenance reliability and operational efficiency, with sustainability considerations in mind. The ITS-based approach assesses maintenance scheduling, tracking, and resource optimization, thereby enhancing the reliability of aircraft operations while reducing unnecessary resource consumption. This alignment with sustainable practices not only improves reliability characteristics and exploitation rates but also positively impacts the efficiency and effectiveness of aviation training. By accurately estimating the time requirements of specific maintenance tasks during periodic inspections, the application aids in identifying and addressing organizational bottlenecks, ultimately supporting both operational sustainability and improved task reliability across maintenance activities.

This article presents the selected results of analytical and structural work conducted at the Air Force Institute of Technology (pl. ITWL) in the field of building a measuring apparatus for non-invasive monitoring of the technical condition of aircraft power units. Presented innovative FAM-C and FDM-A methods allow for observation of frequency modulation parameters as well as identification and diagnostic classification of particular mechanical subassemblies supplying the on-board generator and thus enable non-invasive monitoring of technical condition of the aircraft power unit and the aircraft propulsion system. The main purpose of this article is to present the measurement apparatus errors that occur in the signal conditioning system in the FAM-C and FDM-A methods. In spite of the fact that the measuring instrument was built on the basis of digital technology and that it uses typical solutions of electronic frequency measurement, due to the specificity of the applied diagnostic method there occur specific measuring errors which are presented in this article.

Iterative position estimation algorithms based on Time Difference of Arrival (TDOA) are widely used in passive localization systems, including underwater acoustic networks and wireless sensor networks. A critical but often overlooked factor in their practical deployment is the selection of the initial estimate, which directly determines whether the iterative algorithm converges to the correct solution. This paper presents a case-specific approach to initial estimate selection in passive TDOA-based iterative position estimation algorithms. The study evaluates two proposed methods against a common baseline approach, where the initial guess is placed at the center of the sensor formation. Simulations were conducted in Python for both 2D and 3D scenarios, with sensors arranged in two different geometric configurations. A grid-based analysis over a 2 × 2 km area was used to assess performance under both noise-free and noisy TDOA conditions, with Gaussian-distributed error introduced at varying standard deviations. The results demonstrate that in regions where convergence is sensitive to initialization, the proposed Method 1 significantly improves reliability, especially for asymmetric sensor configurations. These findings highlight the importance of initial estimate selection to enhance position estimation accuracy and robustness, particularly in passive systems with limited prior information.