Concentrated solar power (CSP) is one of the growing technologies that is
leading the process of changing from fossil fuels to renewable energies. The
sophistication and size of the systems require an increase in maintenance tasks
to ensure reliability, availability, maintainability and safety. Currently,
automatic fault detection in CSP plants using Parabolic Trough Collector
systems evidences two main drawbacks: 1) the devices in use needs to be
manually placed near the receiver tube, 2) the Machine Learning-based solutions
are not tested in real plants. We address both gaps by combining the data
extracted with the use of an Unmaned Aerial Vehicle, and the data provided by
sensors placed within 7 real plants. The resulting dataset is the first one of
this type and can help to standardize research activities for the problem of
fault detection in this type of plants. Our work proposes supervised
machine-learning algorithms for detecting broken envelopes of the absorber
tubes in CSP plants. The proposed solution takes the class imbalance problem
into account, boosting the accuracy of the algorithms for the minority class
without harming the overall performance of the models. For a Deep Residual
Network, we solve an imbalance and a balance problem at the same time, which
increases by 5% the Recall of the minority class with no harm to the F1-score.
Additionally, the Random Under Sampling technique boost the performance of
traditional Machine Learning models, being the Histogram Gradient Boost
Classifier the algorithm with the highest increase (3%) in the F1-Score. To the
best of our knowledge, this paper is the first providing an automated solution
to this problem using data from operating plants