A review of wind speed and wind power forecasting with deep neural networks

Automatic and reliable fault diagnosis of rotating machinery cross working conditions is of practical importance. For this purpose, ensemble transfer convolutional neural networks (CNNs) driven by multi-channel signals are proposed in this paper. Firstly, a series of source CNNs modified with stochastic pooling and Leaky rectified linear unit (LReLU) are pre-trained using multi-channel signals. Secondly, the learned parameter knowledge of each individual source CNN is transferred to initialize the corresponding target CNN which is then fine-tuned by a few target training samples. Finally, a new decision fusion strategy is designed to flexibly fuse each individual target CNN to obtain the comprehensive result. The proposed method is used to analyze multi-channel signals measured from rotating machinery. The comparison result shows the superiorities of the proposed method over the existing deep transfer learning methods.

Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions

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A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

Recent progress on intelligent fault diagnosis (IFD) has greatly depended on deep representation learning and plenty of labeled data. However, machines often operate with various working conditions, or the target task has different distributions with the collected data used for training (the domain shift problem). Besides, the newly collected test data in the target domain are usually unlabeled, leading to unsupervised deep transfer learning (UDTL)-based IFD problem. Although it has achieved huge development, a standard and open source code framework and a comparative study for UDTL-based IFD are not yet established. In this article, we construct a new taxonomy and perform a comprehensive review of UDTL-based IFD according to different tasks. Comparative analysis of some typical methods and datasets reveals some open and essential issues in UDTL-based IFD, which is rarely studied, including transferability of features, the influence of backbones, negative transfer, physical priors, and so on. To emphasize the importance and reproducibility of UDTL-based IFD, the whole test framework will be released to the research community to facilitate future research. In summary, the released framework and comparative study can serve as an extended interface and basic results to carry out new studies on UDTL-based IFD. The code framework is available at https://github.com/ZhaoZhibin/UDTL .

Xianzhu Zhao

Papers

Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions

Support tensor machine with dynamic penalty factors and its application to the fault diagnosis of rotating machinery with unbalanced data

A novelty detection scheme for rolling bearing based on multiscale fuzzy distribution entropy and hybrid kernel convex hull approximation

A rotating machinery fault diagnosis method using composite multiscale fuzzy distribution entropy and minimal error of convex hull approximation