  Anomaly detection plays a crucial role in industrial settings, particularly
in maintaining the reliability and optimal performance of cooling systems.
Traditional anomaly detection methods often face challenges in handling diverse
data characteristics and variations in noise levels, resulting in limited
effectiveness. And yet traditional anomaly detection often relies on
application of single models. This work proposes a novel, robust approach using
five heterogeneous independent models combined with a dual ensemble fusion of
voting techniques. Diverse models capture various system behaviors, while the
fusion strategy maximizes detection effectiveness and minimizes false alarms.
Each base autoencoder model learns a unique representation of the data,
leveraging their complementary strengths to improve anomaly detection
performance. To increase the effectiveness and reliability of final anomaly
prediction, dual ensemble technique is applied. This approach outperforms in
maximizing the coverage of identifying anomalies. Experimental results on a
real-world dataset of industrial cooling system data demonstrate the
effectiveness of the proposed approach. This approach can be extended to other
industrial applications where anomaly detection is critical for ensuring system
reliability and preventing potential malfunctions.
