Work

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

Year: 2025

Type: article

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.

Source: Energies

DOI: 10.3390/en18040936

Authors: Giulia Palma, Giovanni Geraci, Antonio Rizzo

Institution: University of Siena

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137