Sequestered Shepherded Long-Tailed Intercommunication Eradication using Restraint Adumbration

Abstract

We address the critical task of identifying and rectifying flaws in aerospace machines to streamline the upgrading process. Leveraging Linear Regression algorithms, our model systematically detects defects in machines utilized in aerospace manufacturing, ensuring the continued precision and safety of aircraft, spacecraft, and related components. By analyzing maintenance histories and crucial parameters indicative of potential issues, such as fuselage damages or leakages, we employ linear regression to pinpoint defects. This integration of modern analysis techniques enables aerospace manufacturers to aggressively detect and address flaws in their equipment, thereby enhancing product quality, safety, and efficiency. Our project focuses on detecting machine defects in aerospace manufacturing by analyzing maintenance histories. By employing linear regression, we aim to identify defects based on various approaches and criteria, ensuring a comprehensive evaluation of machines used in aerospace manufacturing industries. Leveraging collected defect data from technicians, our system utilizes linear regression to identify and address machine defects effectively. However, Linear regression suitability for anomaly detection or defect identification in aerospace manufacturing machines may require adaptation