Abstract
Monitoring industrial infrastructures are undergoing a critical transformation with industry 4.0.
Monitoring solutions must follow the system behavior in real time and must adapt to its continuous change. We propose in this paper an autoencoder model-based approach for tracking abnormalities in industrial application. A set of sensors collects data from turbo-compressors and an
original two-level machine learning LSTM autoencoder architecture defines a continuous nominal vibration model. Normalized thresholds (ISO 20816)
between the model and the system generates a possible abnormal situation to diagnose. Experimental
results, including hyper-parameter optimization on
large real data and domain expert analysis, show
that our proposed solution gives promising results