Autoencoder-based Fault Diagnosis for Hydropower Plants

Industrial 4.0 is a technological revolution that encompasses innovative concepts including: the Internet of Things (IoT), cloud computing, data analytics, Artificial intelligence (AI), and machine learning algorithms. The benefits of industry 4.0 provide innovative approaches to increase the efficiency and performance of industrial equipment, including classic maintenance practices, which have recently evolved into predictive maintenance, and early fault detection techniques. In this paper, we focus on hydropower plants as one of the most valuable renewable energy resources and by using autoencoders networks, we obtain a smart model for fault detection that is convenient for mitigating the occurrence of potential damages or the length of maintenance closures. Finally, we validate the performance of this model, utilizing the well-known multi-variable control T2 Hotelling chart. The proposed auto encoder-based fault detection model successfully identifies potential faults in two hydropower plants.