Deep neural networks present very competitive results in mechanical fault diagnosis. However, training deep models require high computing power while the performances of deep models often suffer from the lack of sufficient training data. In this paper, a transferable convolutional neural network (TCNN) is proposed to improve the learning of target tasks. Firstly, one-dimension CNN is constructed and pre-trained based on large source task datasets. Then a transfer learning strategy is adopted to build a deep model on target tasks by reusing the pre-trained network. Thus, the proposed method not only utilizes the learning power of deep network but also leverages the prior knowledge from the source task. Four case studies are performed, and the effects of transfer layers and training sample size on classification effectiveness are investigated. Results show that the proposed method exhibits better performance compared with other algorithms.

Recently, deep learning-based intelligent fault diagnosis techniques have obtained good classification performance with amount of supervised training data. However, domain shift problem between training and testing data usually occurs due to variation of operating conditions and interferences of environment noise. Transfer learning provides a promising tool for handling the cross-domain diagnosis problems by leveraging knowledge from source domain to help learning in target domain. Most existing studies attempt to learn both domain features in a common feature space to reduce domain shift, which are not optimal on specific discriminative tasks and can be limited to small shifts. This paper proposes a novel Domain Adversarial Transfer Network (DATN), exploiting task-specific feature learning networks and domain adversarial training techniques for handling large distribution discrepancy across domains, Firstly, two asymmetric encoder networks integrating deep convolutional neural networks are designed for learning hierarchical representations from source domain and target domain, respectively. Then, the network weighs learned in source tasks are transferred to improve training on target tasks. Finally, domain adversarial training with inverted label loss is introduced to minimize the difference between source and target distributions. To validate the effectiveness and superiority of the proposed method in the presence of large domain shifts, two fault datasets from different test rigs are investigated, and different fault severities, compound faults and data contaminated by noise are considered. Experimental results demonstrate that the proposed method achieves the average accuracy of 96.45% on the bearing dataset and 98.92% on the gearbox dataset, which outperforms other algorithms.

Recently, domain adaptation techniques have achieved great attentions in solving domain shift problems of mechanical fault diagnosis. However, existing methods mostly work under assumption that source domain and target domain share identical label spaces, which fail to handle those issues, where a large set of source data classes are available and target data only cover a subset of classes. To address this problem, a novel Weighted Adversarial Transfer Network (WATN) is proposed for partial domain fault diagnosis. Adversarial training is introduced to learn both class discriminative and domain invariant features, and a weighting learning strategy is adopted to weigh their contributions to both source classifier and domain discriminator. As such, the irrelevant source examples can be identified and filtered out, and the distribution discrepancy of shared classes between domains can be reduced. Experimental on two diagnosis datasets demonstrate that the proposed WATN achieves satisfactory performance and outperforms state-of-the-art methods.

In real industries, there often exist application scenarios where the target domain holds fault categories never observed in the source domain, which is an open-set domain adaptation (DA) diagnosis issue. Existing DA diagnosis methods under the assumption of sharing identical label space across domains fail to work. What is more, labeled samples can be collected from different sources, where multisource information fusion is rarely considered. To handle this issue, a multisource open-set DA diagnosis approach is developed. Specifically, multisource domain data of different operation conditions sharing partial classes are adopted to take advantage of fault information. Then, an open-set DA network is constructed to mitigate the domain gap across domains. Finally, a weighting learning strategy is introduced to adaptively weigh the importance on feature distribution alignment between known class and unknown class samples. Extensive experiments suggest that the proposed approach can substantially boost the performance of open-set diagnosis issues and outperform existing diagnosis approaches.

Generally, high performance of deep learning (DL)-based machinery fault diagnosis methods relies on abundant labeled fault samples under various working conditions, while they are usually stored by different users and not communicated with each other due to data privacy protection. Federated learning (FL) is a possible solution, but the traditional federated averaging (FedAvg) algorithm in FL ignores the potential domain shift of different FL participants, which limits its further application. Therefore, a federated transfer learning (FTL) framework with discrepancy-based weighted federated averaging (D-WFA) is proposed to train the good global diagnosis model collaboratively as well as protect data privacy. First, local labeled source domain data and unlabeled target domain data are utilized to update multiple local models with generalization ability. Then, a maximum mean discrepancy (MMD)-based dynamic weighted averaging algorithm is designed to aggregate the updated local models with automatically learned weight. The proposed D-WFA overcomes the disadvantage of the traditional FedAvg algorithm, which assumes that all clients have the same contribution in constructing the global model during FL training. Experiment results on a bearing dataset show that the proposed D-WFA outperforms the traditional FedAvg and relative FTL method, which offers a promising solution in privacy-preserving machine learning for fault diagnosis.