Assessment of Deep Learning Algorithms for Fault Diagnosis of Solar Thermal Systems
Abstract
Solar hot water systems are viable and sustainable devices for domestic and industrial energy needs. Nevertheless, efficient operation can be compromised if necessary and frequent maintenance measures are not implemented. Degradation of components and malfunction in the thermal system may undergo unnoticed when coupled with traditional auxiliary energy sources. Detailed and continuous monitoring, however, elevates the overall cost of the system and thus, other methods are explored for performance assessment and fault detection. Data-driven techniques have become popular Prognosis and Health Management approaches in mechanical components for detection, diagnostics and prognostics of complex systems. In this context, Deep Learning algorithms, such as ANN, RNN and LSTM, are explored as alternatives for performance prediction and anomalous behavior detection in a solar hot water system. TRNSYS simulation software is used to generate synthetic operation data for the system for nominal operational and fault-induced conditions.