In the event of mechanical equipment failure, the fault may not belong to any known category, and existing deep learning methods often misclassify such faults into a known class, leading to erroneous fault diagnosis. In order to address the challenge of identifying new types of faults in mechanical equipment fault diagnosis, this paper proposes a novelty detection and fault diagnosis method for bearing faults based on a hybrid deep autoencoder network. Firstly, a hybrid deep autoencoder network with one input and two outputs was constructed. The original data were then fed into the network to obtain its low-dimensional representation and reconstructed data. By setting a threshold based on the reconstruction error, novel class faults can be detected, while known faults can be classified based on low-dimensional features. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 98.59% (100%) for novel class identification (known fault classification) on the CWRU bearing dataset, 96.79% (98.53%) on the Paderborn dataset, and 84.34% (97.03%) on the MFPT dataset. Therefore, the hybrid deep autoencoder network not only accurately detects unknown types of faults but also effectively classifies known fault types, demonstrating excellent fault identification and classification capabilities.

Industrial defect detection has great significance in product quality improvement, and deep learning methods are now the dominant approach. However, the volume of industrial products is enormous and mainstream detectors are unable to maintain a high accuracy rate during rapid detection. To address the above issues, this paper proposes AMFF-YOLOX, an improved industrial defect detector based on YOLOX. The proposed method can reduce the activation function and normalization operation of the bottleneck in the backbone network, and add an attention mechanism and adaptive spatial feature fusion within the feature extraction network to enable the network to better focus on the object. Ultimately, the accuracy of the prediction is enhanced without excessive loss of speed in network prediction, with competitive performance compared to mainstream detectors. Experiments show that the proposed method in this paper achieves 61.06% (85.00%) mAP@0.5:0.95 (mAP@0.5) in the NRSD-MN dataset, 51.58% (91.09%) is achieved in the PCB dataset, and 49.08% (80.48%) is achieved in the NEU-DET dataset. A large number of comparison and ablation experiments validate the effectiveness and competitiveness of the model in industrial defect detection scenarios.

Aiming at the problem of current equipment fault diagnosis models based on deep learning being unable to automatically identify new class faults according to the updated fault data, in this paper we propose an incremental single-class fault diagnosis method based on an online sequential extreme learning machine (OS-ELM). In addition to detecting new types of faults, this method can perform class-incremental learning based on new-class fault data, treating the new-class faults as known faults for ongoing fault detection and diagnosis tasks. This approach first constructs a feature extraction network with a dual-encoder structure to extract data features. Subsequently, the extracted features are used to build a fault diagnosis network based on OS-ELM, where the novelty of new batches of data is determined by the update magnitude of OS-ELM. When a new-class fault is detected, a new OS-ELM representing the current new class is constructed using the new batch of data and added to the fault diagnosis network, thereby achieving incremental model updates. The proposed method is validated through experiments on the CWRU dataset and MFPT dataset. The results demonstrate that the accuracy of this method on the CWRU dataset is 99.62%, while on the MFPT dataset it reaches 98.80%. Compared to other incremental single-class models, this method exhibits excellent fault recognition and diagnosis capabilities.

In order to realize rapid fault detection and early warning, a fault detection method based on normal operation data is proposed. Firstly, the fault detection model is constructed based on the improved deep neural network of the auto-encoder. Secondly, the unsupervised pretraining and supervised fine-tuning of the network are finished through the operation data in a normal state to solve the contradiction between the small fault sample and the large training sample required by the deep network model. The adaptive threshold of reconstruction error is used as the evaluation index of the fault state to reduce the influence of environmental factors. Experimental results show that the proposed method can detect faults effectively.

Deep learning uses mechanical time-frequency signals to train deep neural networks, which realizes automatic feature extraction and intelligent diagnosis of fault features and gets rid of the dependence on a large number of signal processing technology and experience. Aiming at the problem of misclassification of similar samples, a fault diagnosis algorithm based on adaptive hierarchical clustering and subset (AHC-SFD) is proposed to extract features and applied to gearbox fault diagnosis. Firstly, the adaptive hierarchical clustering algorithm is used to analyze the characteristics of different data, and then the data set is clustered into multiple feature groups; finally, according to the feature group, the SubCNN model is established for multiscale feature extraction, so as to carry out fault diagnosis. The test results show that the fault recognition rate achieved by the proposed method is more than 99.7% on the gearbox dataset, and the method has better generalization ability.