Intelligent Diagnostic System for Mechanical Fault Detection Using Deep Learning

Advanced diagnostic tools are essential for aerospace transportation systems and automotive industries and industrial manufacturing facilities since operational efficiency requirements and safety needs demand failure prediction tools. Systems that use traditional diagnostic methods depend on centralized architectures that show limitations regarding scalability while being unable to overcome subsystem failure events effectively. The research presents Gossip Neural Network (GNN) as a decentralized deep learning (DL) system which determines Remaining Useful Life (RUL) duration in distributed mechanical engine systems. The GNN combines Convolutional Neural Networks (CNNs) and Long short- term Memory (LSTM) network layers to identify short-term sensor anomalies in addition to capturing long-term sensor degeneration patterns in sensor data. A gossip-based protocol allows the GNN to facilitate distributed engine subsystems which train a shared model together through peer-to-peer collaborations without needing central control. The assessment of the proposed framework using CMAPSS data proves its exceptional capability for RUL prediction alongside reliable accuracy and low error rates. The GNN demonstrated excellence in different datasets through R² results between 92.43% and 94.57% and RMSE results within 12.77 to 12.87 which demonstrates its effectiveness in handling realistic operational environments. The GNN provides an encouraging solution for time-sensitive fault detection in distributed systems which facilitates efficient predictive maintenance across large engineering applications.