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AI tasks based on deep neural networks have been widely applied in industrial applications, such as process control, quality inspection or predictive maintenance. Deep neural network classifiers are particularly successful, as they provide powerful and reliable algorithms for many applications such as object recognition and fault diagnosis. However, most deep classifier applications are not able to recognize class samples that are beyond the scope of their training data. Samples of unknown classes (denoted as open set data) lead to significant drops in performance, as the output of deep classifiers is limited to the known classes of the training data (denoted as closed set data). This paper presents a method to recognize open set samples without changing the neural network architecture, the training process, nor the trained models. In our method, we firstly train a neural network for normal closed set fault diagnosis. Then we compare the feature maps of testing samples and known class samples during inference using local outlier factor to recognize open set samples. We evaluate our method with two public datasets and show that our method can increase the overall accuracy by 40% when classifying open set data. Besides, we also compared our method to the state-of-the-art open set recognition approach for fault diagnosis applications and the results show that our method leads to better F1-scores. |
