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.
Keywords
- Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
- Machine Learning: Time-series;Data Streams
- Multidisciplinary Topics and Applications: Real-Time Systems
- Machine Learning Applications: Other Applications
- Machine Learning Applications: Applications of Unsupervised Learning
