Browsing by Author "El Mir, Haroun"

Now showing 1 - 4 of 4
  • Thumbnail Image
    ItemOpen Access
    Certification approach for physics informed machine learning and its application in landing gear life assessment
    (IEEE, 2021-11-15) El Mir, Haroun; Perinpanayagam, Suresh
    The efficacy of fatigue life approximation methodologies for Landing Gear systems is studied and compared to the ongoing Structural Health Monitoring techniques being researched, which will forecast failures based on the system’s specific life and withstanding abilities, ranging from creating a digital simulation model to applying neural network technologies, in order to simulate and approximate locations and levels of failure along the structure. Explainable Artificial Intelligence allows for the ease-of-integration of Deep Neural Network data into Predictive Maintenance, which is a procedure focused on the health of a system and its efficient upkeep via the use of sensor-based data. Test data from a flight includes a multitude of conditions and varying parameters such as the surface of the landing strip as well as the aircraft itself, requiring the use of Deep Neural Network models for damage assessment and failure anticipation, where compliance to standards is a major question raised, as the EASA AI roadmap is followed, as well as the ICAO and FAA. This paper additionally discusses the challenges faced with respect to standardizing the Explainable AI methodologies and their parameters specifically for the case of Landing Gear.
  • Thumbnail Image
    ItemOpen Access
    Certification of machine learning algorithms for safe life assessment of landing gear
    (Frontiers, 2022-11-15) El Mir, Haroun; Perinpanayagam, Suresh
    This paper provides information on current certification of landing gear available for use in the aerospace industry. Moving forward, machine learning is part of structural health monitoring, which is being used by the aircraft industry. The non-deterministic nature of deep learning algorithms is regarded as a hurdle for certification and verification for use in the highly-regulated aerospace industry. This paper brings forth its regulation requirements and the emergence of standardisation efforts. To be able to validate machine learning for safety critical applications such as landing gear, the safe-life fatigue assessment needs to be certified such that the remaining useful life may be accurately predicted and trusted. A coverage of future certification for the usage of machine learning in safety-critical aerospace systems is provided, taking into consideration both the risk management and explainability for different end user categories involved in the certification process. Additionally, provisional use case scenarios are demonstrated, in which risk assessments and uncertainties are incorporated for the implementation of a proposed certification approach targeting offline machine learning models and their explainable usage for predicting the remaining useful life of landing gear systems based on the safe-life method.
  • Thumbnail Image
    ItemOpen Access
    Landing gear health assessment: synergising flight data analysis with theoretical prognostics in a hybrid assessment approach
    (PHM Society, 2024-06-27) El Mir, Haroun; King, Stephen; Skote, Martin; Alam, Mushfiqul; Place, Simon
    This study addresses a critical shortfall in aircraft landing gear (LG) maintenance: the challenge of detecting degradation that necessitates intervention between scheduled maintenance intervals, particularly in the absence of hard landings. To address this issue, we introduce a Performance Degradation Metric (PDM) utilising Flight Data Recorder (FDR) output during the touchdown and initial roll phases of landing. This metric correlates time-series accelerometer data from a Saab 340B aircraft’s onboard sensors with non-linear response dynamic models that predict expected LG travel and reaction profiles across a set of ground contact cycles within a single landing. This facilitates the early detection of deviations from standard LG response behaviour, pinpointing potential performance abnormalities. The initiator of this approach is the Landing Sequence Typology, which systematically decomposes each aircraft landing into successive dynamic periods defined by their representative boundary conditions. What follows is the setting of initial parameters for the ordinary differential equations (ODE)s of motion that determine the orientation and impact responses of the most critical components of the LG assembly. Solving these ODEs with the integration of a non-linear representation of an oleo-pneumatic shock absorber model compliant with CS25 aircraft standards produces anticipated profiles of LG travel based on factors such as aircraft weight and speed at touchdown, which are subsequently cross-referenced with real accelerometer data, enhanced by video footage analysis. This footage is crucial for verifying the sequence of LG touchdowns and corresponding accelerometer outputs, thereby bolstering the precision of our analysis. Upon the conclusion of this study, by facilitating the early identification of LG performance deviations in specific landing scenarios, this diagnostic tool shall enable timely maintenance interventions. This proactive approach not only mitigates the risk of damage escalation to other components but also transitions main LG maintenance practices from reactive to proactive.
  • Thumbnail Image
    ItemOpen Access
    Machine learning requirements for the airworthiness of structural health monitoring systems in aircraft
    (ICAF, 2023-06-30) El Mir, Haroun; King, Stephen; Skote, Martin; Perinpanayagam, Suresh
    In the evolving realm of airworthiness and aircraft maintenance task scheduling, the introduction of data-driven Predictive Maintenance (PdM) and Structural Health Monitoring (SHM) has prompted a paradigm shift, which underscores the profound implications of innovative sensing techniques within damage and operational monitoring. Concurrently, the role of avionics in data acquisition and processing has drawn renewed focus, with machine learning (ML) algorithms facilitating pattern recognition, trend analysis, and anomaly detection. This paper discusses the diagnostic sequence in SHM systems, the necessity for damage information, and delves into active and passive sensing techniques within damage and operational monitoring. The role of avionics is also emphasized, especially in data acquisition and processing for operational monitoring. The utilization of ML algorithms for efficient use within SHM is explored, alongside supervised and unsupervised learning methods. The paper underlines how integrating ML in aircraft systems applications can optimize maintenance schedules and lay a solid foundation for SHM integration in aircraft health systems. The study also covers the application of ML techniques for detection, localization, and assessment of structural damage. It reviews research implementations using ML, statistical, and hybrid approaches in monitoring and predicting aircraft damage. The incorporation of non- exclusive ML in SHM to minimize environmental feature uncertainty and enable trackable model behaviour is illustrated. Lastly, the paper discusses evolving regulatory requirements and standards for ML application in aviation SHM, provided by authorities and workgroups like EASA and the SAE G-34 AI in Aviation Committee, respectively, and concludes with an overview of the future trends and standards in this dynamic domain. The aim is to spotlight the transformative potential of PdM and SHM, and their critical roles in boosting the operational efficiency of the aviation industry.