Narrow Results

Aerospace systems are highly complex and critical, with stringent reliability requirements to ensure safe operations. The maintenance and management of these systems are of paramount importance, as any fault or failure can lead to significant consequences. Traditional fault prediction methods rely on manual inspection and rule-based systems, which are inefficient, error-prone, and reactive. The primary objective is to develop an intelligent fault prediction and reliability optimization framework for aerospace systems using DL techniques. The data are gathered from various aerospace systems, including sensor data, maintenance logs, and failure reports, sourced from operational aircraft and aerospace machinery over several years. Data were preprocessed using data cleaning and normalization methods. The research proposed a novel intelligent beluga whale optimized Elman neural network (IBWO-ENN) to identify and predict faults in aerospace systems. IBWO is used for feature selection, and ENN is employed to predict faults in aerospace systems. The aerospace propulsion system demonstrates the efficacy of the suggested model, achieving significant improvements in fault prediction accuracy (98.39%), precision (98.54%), and F1-score (98.23%) and system reliability compared to traditional methods. The research contributes to the urgent demand for improved safety and operational efficiency in increasingly complex aircraft settings by presenting a unique DL-based framework for fault prediction and reliability improvement of aerospace systems.

Today’s development environment has changed drastically; the development periods are shorter than ever and the number of team members has increased. Consequently, controlling the activities and predicting when a development will end are difficult tasks. To adapt to changes, we propose a generalized software reliability model (GSRM) based on a stochastic process to simulate developments, which include uncertainties and dynamics such as unpredictable changes in the requirements and the number of team members. We assess two actual datasets using our formulated equations, which are related to three types of development uncertainties by employing simple approximations in GSRM. The results show that developments can be evaluated quantitatively. Additionally, a comparison of GSRM with existing software reliability models confirms that the approximation by GSRM is more precise than those by existing models.