4.3. Experiment Results We followed the approach proposed in [22] and, in the final experiment, 10 runs were conducted for each deep learning and machine learning algorithm. The primary objective in this drying hopper case was to accurately detect events for predictive maintenance purposes. Since the number of non-events was significantly higher, correctly identifying non-events automatically resulted in high accuracy regardless of whether the model was able to identify the events or not.
