Research on Algorithm Assisted Fault Detection and Diagnosis in Aircraft Engine Air Systems

The implementation of designed algorithms-assisted in the diagnosis and fault detection in aircraft engine air systems remains critical in optimizing operational efficiency and maintaining safety. Given this, modern aircraft engines and systems are often complex, establishing the reasons why they rely on different subsystems that work in total harmony. Am aircrafts air system, widely responsible for the regulation of airflow as well as the maintenance of optimal performance of the engine remains critical. Given this, fault detection algorithms widely rely on real-time data and information from sensors fitted on the engines to help in the identification of anomalies and the indication of the systems potential failures. The use of these algorithms widely relies on machine learning algorithms and techniques that include decision trees and neural networks in the analysis of the systems patterns and the prediction of faults before resulting in significant issues. Once faults are detected, a diagnostic algorithm is then determined in establishing the root cause of a fault in the engine through an analysis of the deviations from the operating issues and conditions. This process involves a comparison of the system with current data against the systems historical data in order to establish deviations from the normal operating systems and conditions. This article therefore sought to explore and conduct research on algorithm assisted fault detection and diagnosis in Aircraft Engine Air Systems (AEAS). Findings revealed that the integration and implementation of algorithm assisted fault detection and diagnosis systems in AEAS provides for accurate and real-time adjustments in the mitigation of the effects of detected faults, thus ensuring a safe and continuous operation.