LACSF framework deployment for fault detection and mitigation

Abstract

AI-driven failure detection is becoming essential in industrial manufacturing systems where conventional diagnostic methods often fall short in reliability and live feedback. This paper presents the integration of a modular artificial intelligence framework adapted to overcome these challenges by enabling intelligent fault monitoring systems. The proposed architecture consists of four integrated layers: a Sensor-Derived Adaptive Envelope Layer (SDAEL) for preprocessing and converting complex input signals; an Intelligent Deviation Mapping Engine (IDME), which utilizes a integration of One-Class Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) networks for failure detection; a Failure Insight Notification Core (FINC) for real-time fault categorization; and a Federated Learning and Recovery Engine (FLARE) that enables secure, decentralized data updates across interlinked nodes. This framework can enables early fault detection in various manufacturing machine sectors. To validate this approach, an experimental deployment of the framework on a plastic bottle manufacturing machine that operated under changing conditions was conducted. The experimental evaluation showed improved detection accuracy and faster responsiveness compared to conventional diagnostic methods. Also Comparative benchmarking demonstrated that FLARE achieved a higher Precision of 0.80, Recall of 0.85, and an F1-score of 0.85, outperforming isolated LACSF models (F1-score 0.72) and LSTM-autoencoders (F1-score 0.62) by 13 and 23 points, respectively.This highlights the framework’s potential to improve fault diagnostics.