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.

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.