# Decision-Making Framework for Aviation Safety in Predictive Maintenance Strategies
### Related Works  
The aviation industry encompasses a diverse array of economic activities, with one of its key segments being the maintenance, repair, and overhaul (MRO) market. This sector accounts for approximately 11-15% of an airline's operating costs [4]. Given the significant impact of MRO on airline profitability, improving efficiency in aircraft maintenance processes has become a focal area. Alongside this, technological advancements in the field have matured to a level that supports the adoption of innovative maintenance strategies and policies, including condition-based maintenance (CBM). CBM is a proactive maintenance policy defined as \"preventive maintenance which includes assessment of physical conditions, analysis and the possible ensuing maintenance actions\" [5]. Alternatively, it is described as \"a maintenance program that recommends maintenance actions based on the information collected through condition monitoring\" [6]. By using real-time condition monitoring, CBM helps identify and prevent unscheduled maintenance events, enables task substitution or extension of maintenance intervals, and optimizes maintenance schedules across an airline's fleet [7;8]. These capabilities position CBM as a promising approach to significantly improve operational efficiency in aviation. CBM is expected to emerge as the dominant maintenance policy in aviation. However, despite the presence of CBM elements in the industry for decades, widespread adoption of this maintenance approach remains elusive, falling short of the ambitious vision set forth by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) [9]. Answering these questions requires a detailed and holistic understanding of CBM that considers all factors influencing its uptake. Historically, most research on CBM has been technologically oriented, focusing on the development of models, algorithms, and methods for the detection, diagnosis, and prognosis of equipment failures. Additionally, there is a substantial body of literature addressing planning and decision-support aspects of CBM, exploring how organizations can utilize diagnostic and predictive data to effectively plan and execute maintenance interventions [10]. Preventive maintenance is widely employed to schedule maintenance actions proactively to prevent equipment failures. While effective, this strategy often incurs higher costs and results in unnecessary component replacements or unforeseen failures [11]. PMoffers a more refined alternative by detecting potential failures in advance through periodic monitoring of the condition of processes, machines, materials, and products within manufacturing systems. This approach enables just-in-time maintenance actions, enhancing equipment availability, quality, and safety while significantly reducing costs associated with breakdowns and excessive maintenance activities [12; 13]. Predictive maintenance can be classified into two distinct subcategories: statistics-based PM and condition-based PM [14]. The integration of deep learning methodologies has enhanced the accuracy of predicting the remaining useful life (RUL) of aircraft engines. Paper [15] introduced innovative models, including convolutional LSTM (long short-term memory) and Recurrent LSTM, demonstrating that the latter achieved superior accuracy, precision, and recall rates in RUL prediction. Paper [16] proposed a deep learning algorithm combining a 1-D convolutional neural network (CNN) with a bidirectional long short-term memory network, effectively predicting engine RUL. The application of artificial intelligence (AI) and machine learning (ML) in PM has also been explored to improve maintenance strategies. A study [17] presented an approach using CNNs to develop a predictive failure model for early detection of engine breakdowns, aiming to transition from corrective to predictive maintenance in the aviation industry.