Traditional data-driven intelligent fault diagnosis methods have been successfully developed under the closed set assumption (CSA). CSA-based fault diagnosis assumes that the fault types in the test set are consistent with that in the training set, which can achieve high accuracy, but this is generally not valid in real-world industrial applications where the collection of data in industrial applications is often limited. As it is unrealistic to assume that the training set will cover all fault types, the application of the fault classifier may fail when the test set contains unknown fault types because the probability of input samples belonging to unknown types cannot be obtained. To solve the problem of how unknown fault types may be accurately identified, this paper further studies the open set assumption (OSA) fault diagnosis. We propose an open set convolutional neural network (OS-CNN) method and apply our OS-CNN model to an improved OpenMax method as a deep network to accurately detect unknown fault types. The overall performance was significantly improved as our OS-CNN model was able to effectively tighten the boundary of known classes and limit the open-space risk for the OpenMax method based on distance modeling. The overall effectiveness of the proposed method was verified by experimental studies based on four different bearing datasets. Compared with state-of-the-art OSA fault diagnosis method, our method cannot only realize the correct classification of the known fault classes, but it can also accurately detect the unknown fault classes.

To address the difficulty of obtaining real fault data from elevator traction drive systems and the potential overestimation caused by random window splitting, this study uses two public motor-drive datasets to examine how validation protocols, signal modalities, and noise conditions affect diagnostic evaluation, rather than to claim direct validation of field performance in actual elevator systems. A PMSM inverter-drive fault diagnosis dataset is used as the main dataset to evaluate multiclass classification performance based on electrical, thermal, and derived features. A multimodal MOTOR dataset is used as an independent secondary dataset to analyze the effects of validation protocols, signal modalities, and noise disturbance on model performance. The results show that Random Forest achieves a Macro-F1 of 0.9901 under random splitting on the PMSM dataset. On the MOTOR dataset, the Macro-F1 reaches 0.9682 under random splitting but decreases to 0.5856 under strict block-split validation, indicating that random window splitting may substantially overestimate generalization performance for continuous signal data. The modality ablation results show that the vibration-only modality performs best under strict block-split validation, with a Macro-F1 of 0.6420, whereas the noise analysis indicates that this modality is sensitive to disturbance. The results show that public motor-drive data can provide a reproducible methodological test bed for studying evaluation bias and signal reliability, but they should not be interpreted as direct evidence of diagnostic performance in actual elevator systems.