Deep learning theory has made great progress in the field of intelligent fault diagnosis, and the development of domain adaptation has greatly promoted fault diagnosis under polytropic working conditions (PWC). Extensive studies have been conducted to solve the problem of fault diagnosis under PWC. However, the existing fault diagnosis methods based on domain adaptation have the following shortcomings. First, multisource information fusion is rarely considered. Second, the utilization of inherent labels is also insufficient in classification problems. To deal with the above problem, a novel multisource dense adaptation adversarial network is proposed, which leverages multisensor vibration information and classification label information. Specifically, the frequency spectrum of multisensor data is first employed to make full use of fault information. Afterwards, the dense convolution and fusion convolution blocks are used for deep feature extraction and fusion. Finally, a joint loss function is reconstructed under the framework of unsupervised learning, which considers the distribution differences of the features and the label information simultaneously. The experimental results from various working conditions, including still distant working conditions, all demonstrate that the proposed method can achieve state-of-the-art performances, which has shown great promise as an intelligent fault diagnosis method.

Aluminum Alloy is the main structure material in aerospace industry. On-line defect detection for Aluminum alloy in pulsed gas tungsten arc welding (GTAW) is still challenging, especially for increasing application of robotics. This paper presents an intelligent methodology for real-time evaluation of weld penetration defects based on arc audible sound sensing for aluminum alloy in robotic pulsed GTAW. The generation mechanism of arc sound was investigated using correlation analysis, high-speed camera observing and frequency spectrum analysis before denoising of arc sound. Two feature selection approaches based on Fisher distance and principle component analysis (PCA) were developed to select frequency components related to seam defects, and then, their performance were qualitatively and quantitatively analyzed. Finally, a new classification model integrating support vector machine with grid search optimization and cross-validation (SVM-GSCV) was proposed to identify under penetration, normal penetration and burning through. The proposed methodologies were verified to be effective with high accuracy and robustness. This paper can provide some guidance for condition monitoring of additive manufacturing (AM) or process industry.

In engineering practice, mechanical equipment is usually in polytropic working conditions, where the data distribu-tion of training set and test set is inconsistent, resulting in insuffi-cient generalization ability of the intelligent diagnosis model. Sim-ultaneously, different tasks often need to be modeled separately. Domain adaptation, as one of the research contents of transfer learning, has certain advantages in solving the problem of incon-sistent feature distribution. This paper designs and establishes a Domain Adaptation Framework based on Multi-scale Mixed Do-main Feature (DA-MMDF) for cross-domain intelligent fault di-agnosis of rolling bearings under polytropic working conditions. The proposed method first uses the multi-scale mixed domain fea-ture extractor to obtain features from the collected data, which constructs a complete feature space through Variational Mode De-composition (VMD) and mixed domain feature extraction to fully mine the state information and intrinsic attributes of the vibration signal. Secondly, the dimensionality reduction and optimization of features are achieved through extreme gradient promotion, and meaningful and sensitive features are selected according to the im-portance of features to eliminate redundant information. The op-timized important features are combined with the manifold em-bedded distribution alignment method to realize the distribution alignment of data in different fields and cross-domain diagnosis. In order to verify the effectiveness of the proposed approach, the rolling bearing data sets gathered from the laboratories are em-ployed and analyzed. The analysis result confirms that DA-MMDF is able to achieve effective transfer diagnosis between polytropic working conditions. Compared with traditional intelligent fault di-agnosis methods and domain adaptation methods, the method pro-posed in this article achieved state-of-the-art performances.

18650 Lithium-ion batteries are wildly used as power sources for portable electronics because of the standardized format and economical manufacturing cost. However, commercial 18650 cells come in various designs due to the implementation of different protection devices. The current interrupt device and top vent are mandatory protection devices for all commercial 18650 Li-ion batteries. In contrast, the positive temperature coefficient thermistor, bottom vent, and protection circuit are optional protection devices that can be non-installed, installed separately, or combined in commercial 18650 batteries. Four representative commercial 18650 Li-ion batteries were disassembled and compared in terms of the similarities and differences of the protection devices. This paper overviews the working mechanism, advantages, and drawbacks of each protection device. Recommendations on battery selection and potential future trends are then provided.

Anomaly detection is the cornerstone for the health management of rolling element bearings. The unsupervised learning model for anomaly detection driven only by normal data has received increasing attention in recent years. In this article, an innovative deep-learning-based model, namely, memory residual regression autoencoder (MRRAE), is developed to improve the accuracy of anomaly detection in bearing condition monitoring. The memory module and autoregressive estimator are applied to calculate the probability density distribution of the latent memory residual representation. The reconstruction errors and surprisal values of the proposed model are used to detect the abnormal condition of bearing. To verify the superiority of the proposed method in anomaly detection, two sets of run-to-failure experimental data set gathered from the laboratories are studied and analyzed. The result demonstrates that the proposed MRRAE model achieves superior performance compared with several conventional and deep-learning-based anomaly detection methods. Furthermore, the proposed method pays close attention to the special structure of bearing vibration signal and provides a new way for explaining the decision-making processes of deep neural networks.

In industrial scenarios, mechanical faults are episodic and uncertain. Thus, the monitoring data collected is usually extremely imbalanced, resulting in intelligent diagnostic models that suffer from majority-class dominance, minority-class overfitting, and poor generalization performance. Therefore, a knowledge distillation-guided cost-sensitive ensemble learning framework is proposed. It effectively combines ensemble learning and cost-sensitive learning to fully extract the multiscale features, effectively leverage the critical multidepth features, and emphasize classifying the most confusing classes. Specifically, multiple-scale feature extraction and multiorder fusion are first employed to fully utilize the fault information. Afterward, the complementary diagnostic knowledge at different depths of the network is embedded into a novel ensemble learning process for better integration decisions. Then an improved knowledge distillation method achieves the mutual transfer and sublimation of excellent diagnostic knowledge while focusing on the most confusing fault classes to achieve the effective representation of various types of faults. Finally, a cost-sensitive strategy is applied to further increase attention to minority classes. The experimental results for various complex data imbalance scenarios, including extreme imbalance, step imbalance, continuous imbalance, interclass imbalance, and intraclass imbalance, all indicate that the proposed method can achieve state-of-the-art performance and provide a promising solution for the practical industrial application of intelligent diagnostic methods.