The realization of accurate fault diagnosis is crucial to ensure the normal operation of machines. At present, an intelligent fault diagnosis method based on deep learning has been widely applied in mechanical areas due to its strong ability of feature extraction and accurate identification. However, it often depends on enough training samples. Generally, the model performance depends on sufficient training samples. However, the fault data are always insufficient in practical engineering as the mechanical equipment often works under normal conditions, resulting in imbalanced data. Deep learning-based models trained directly with the imbalanced data will greatly reduce the diagnosis accuracy. In this paper, a diagnosis method is proposed to address the imbalanced data problem and enhance the diagnosis accuracy. Firstly, signals from multiple sensors are processed by the wavelet transform to enhance data features, which are then squeezed and fused through pooling and splicing operations. Subsequently, improved adversarial networks are constructed to generate new samples for data augmentation. Finally, an improved residual network is constructed by introducing the convolutional block attention module for enhancing the diagnosis performance. The experiments containing two different types of bearing datasets are adopted to validate the effectiveness and superiority of the proposed method in single-class and multi-class data imbalance cases. The results show that the proposed method can generate high-quality synthetic samples and improve the diagnosis accuracy presenting great potential in imbalanced fault diagnosis.

A novel cell-balancing algorithm which was used for cell balancing of battery management system (BMS) was proposed in this paper. Cell balancing algorithm is a key technology for lithium-ion battery pack in the electric vehicle field. The distance-based outlier detection algorithm adopted two characteristic parameters (voltage and state of charge) to calculate each cell’s abnormal value and then identified the unbalanced cells. The abnormal and normal type of battery cells were acquired by online clustering strategy and bleeding circuits (R= 33 ohm) were used to balance the abnormal cells. The simulation results showed that with the proposed balancing algorithm, the usable capacity of the battery pack increased by 0.614 Ah (9.5%) compared to that without balancing.

Lane detection is a significant technology for autonomous driving. In recent years, a number of lane detection methods have been proposed. However, the performance of fast and slim methods is not satisfactory in sophisticated scenarios and some robust methods are not fast enough. Consequently, we proposed a fast and robust lane detection method by combining a semantic segmentation network and an optical flow estimation network. Specifically, the whole research was divided into three parts: lane segmentation, lane discrimination, and mapping. In terms of lane segmentation, a robust semantic segmentation network was proposed to segment key frames and a fast and slim optical flow estimation network was used to track non-key frames. In the second part, density-based spatial clustering of applications with noise (DBSCAN) was adopted to discriminate lanes. Ultimately, we proposed a mapping method to map lane pixels from pixel coordinate system to camera coordinate system and fit lane curves in the camera coordinate system that are able to provide feedback for autonomous driving. Experimental results verified that the proposed method can speed up robust semantic segmentation network by three times at most and the accuracy fell 2% at most. In the best of circumstances, the result of the lane curve verified that the feedback error was 3%.

Semi-supervised learning (SSL) is effective to address the scarcity of label information in the fault diagnosis of rotating machinery. However, existing SSL methods generally assume that the labeled and unlabeled fault samples share a consistent label space, which is difficult to guarantee in practical applications and limits the applicability of SSL. To address this issue, this study presents a method denoted as open-set semi-supervised learning (O3SL) for intelligent fault diagnosis. First, an orthogonal projection network (OPN) is proposed to classify fault samples based on feature projection. Moreover, an open-set recognition module based on feature projection residual is further presented to discover potential unknown fault types in unlabeled fault samples. Finally, a pseudo label correction updating module is developed to further dynamically generate corrected pseudo labels, and thus to improve the performance of OPN. Extensive experiments on two gearbox fault datasets have validated the effectiveness of the proposed OPN-based O3SL method.