Imagine if an aircraft component could inform us when it needs replacement or repair. Given the low fault tolerance of aircraft engines, even minor faults can lead to catastrophic outcomes. Thus, accurate and real-time information about engine condition is essential. This can be done through ongoing data gathering, real-time monitoring, and smart analytics. In the aviation industry, predictive maintenance enhances reliability, optimizes the supply chain, and improves operational performance. The primary objective is to ensure engines operate correctly under all conditions, minimizing failure risks. Effective failure prediction methods can significantly enhance maintenance practices. Data on engine health is primarily gathered during flights, including variables like fan speed, core speed, oil quantity and pressure, fuel flow, and environmental factors such as temperature, aircraft speed, and altitude. Real-time sensor data can model component deterioration. This study investigates the use of LSTM networks to predict maintenance needs in aircraft engines The LSTM model handles sequential input data, with training conducted on a high-performance processing engine. Combining machines, data, ideas, and people is crucial to understanding the value of predictive maintenance and achieving meaningful business results.
Yashoda et al. (Sat,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: