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The condition of a machine can automatically be identified by creating and classifying features that summarize characteristics of measured signals. Currently, experts, in their respective fields, devise these features based on their knowledge. Hence, the performance and usefulness depends on the expert's knowledge of the underlying physics, or statistics. Furthermore, if new and additional conditions should be detectable, experts have to implement new feature extraction methods. To mitigate the drawbacks of feature engineering, a method from the sub-field of feature learning, i.e. deep learning, more specifically convolutional neural networks, is researched in this article. The objective of this article is to investigate if and how deep learning can be applied to infrared thermal video to automatically determine the condition of the machine. By applying this method on infrared thermal data in two use cases, i.e. machine fault detection and oil level prediction, we show that the proposed system is able to detect many conditions in rotating machinery very accurately (i.e. 95 % and 91.67 % accuracy for the respective use cases) without requiring any detailed knowledge about the underlying physics, and thus having the potential to significantly simplify condition monitoring using complex sensor data. Furthermore, we show that by using the trained neural networks, important regions in the infrared thermal images can be identified related to specific conditions which can potentially lead to new physical insights.

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