8th European Conference of the Prognostics and Health Management Society - PHME24, Praga (República Checa). 03-05 julio 2024
Resumen:
This paper aims to explore the use of recent approaches of deep learning techniques for anomaly detection of potential failure modes in a cooling water pump working in a gas-combined cycle in a power plant. Two different deep learning techniques have been tested: neural networks and reinforcement learning. Two virtual digital twins were developed with each family of deep learning techniques, able to simulate the behavior of the cooling water pump in the absence of pump failure modes. Each virtual digital twin consists of several models for predicting the expected evolution of significant behavior variables when no anomalies exist. Examples of these variables are bearing temperatures or vibrations in different pump locations. All the data used comes from the SCADA system. The main features and hyperparameters in the virtual digital twins are presented, and demonstration examples are included.
Resumen divulgativo:
Este artículo tiene como objetivo explorar el uso de enfoques recientes de técnicas de aprendizaje profundo para la detección de anomalías de posibles modos de fallo en una bomba de agua de circulación de una central de ciclo combinado. Se han probado dos técnicas diferentes de aprendizaje profundo: redes neuronales y aprendizaje por refuerzo.
Palabras clave: Deep learning, reinforcement learning, anomaly detection, digital twin
DOI: 
https://doi.org/10.36001/phme.2024.v8i1.4004
Publicado en PHME 2024, vol: Vol 8, N.1, pp: 187-195, ISBN: 978-1-936263-40-0
Fecha de publicación: 2024-06-27.
Cita:
M.A. Sanz-Bobi, S. Orbach, F.J. Bellido-López, A. Muñoz, D. González-Calvo, T. Álvarez Tejedor, Anomaly detection of a cooling water pump of a power plant based on its virtual digital twin constructed with deep learning techniques, 8th European Conference of the Prognostics and Health Management Society - PHME24, Praga (República Checa). 03-05 julio 2024. En: PHME 2024: Proceedings of the 8th European Conference of the Prognostics and Health Management Society 2024, vol. Vol 8, N.1, ISBN: 978-1-936263-40-0
