Federated Learning and Neural Circuit Policies: A Novel Framework for Anomaly Detection in Energy-Intensive Machinery

Abstract

In the realm of predictive maintenance for energy-intensive machinery, effective anomaly detection is crucial for minimizing downtime and optimizing operational efficiency. This paper introduces a novel approach that integrates federated learning (FL) with Neural Circuit Policies (NCPs) to enhance anomaly detection in compressors utilized in leather tanning operations. Unlike traditional Long Short-Term Memory (LSTM) networks, which rely heavily on historical data patterns and often struggle with generalization, NCPs incorporate physical constraints and system dynamics, resulting in superior performance. Our comparative analysis reveals that NCPs significantly outperform LSTMs in accuracy and interpretability within a federated learning framework. This innovative combination not only addresses pressing data privacy concerns but also facilitates collaborative learning across decentralized data sources. By showcasing the effectiveness of FL and NCPs, this research paves the way for advanced predictive maintenance strategies that prioritize both performance and data integrity in energy-intensive industries.

Keywords

• federated learning
• anomaly detection
• predictive maintenance
• neural circuit policies
• long short-term memory (LSTM)
• energy-intensive machinery