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• A digital twin-assisted dual transfer method was proposed with information and model adaptation for bearing fault diagnosis. • DTa-DT aims to simultaneously synthesize information transfer and model transfer for domain transfer error minimization. • DTd-IT method was proposed to information transfer with low distribution difference of twin data adapted. • DAd-MT method, composed of DBTN, was proposed to model transfer with feature distribution difference of domain-model adapted. • Experiments and ablation study verified that the proposed DTa-DT method can be well applied to small sample learning. Rolling bearing fault diagnosis is of great importance to the safety management of mechanical equipment. The scarcity of labelled fault data makes it difficult to adequately perform the training process of intelligent diagnosis models, and this will result in these intelligent models not being effectively and widely used in practice. Although some recent studies have verified that the addition of dynamic model response to the training process will greatly improve the ability of the model with low cost and high efficiency, it is still stuck in poor effect caused by large information distribution difference between dynamic model response and real measured data. Focusing on this issue, a digital twin-assisted dual transfer (DTa-DT) method with information and model adaptation was proposed for rolling bearing fault diagnosis. Different from the traditional digital-analogue driven transfer methods, the proposed DTa-DT aims to simultaneously synthesize data information transfer and feature model transfer together with domain transfer error minimization. In particular, it should be noted that the DTa-DT architecture consists of a dual transfer learning process, including digital twin-driven information transfer (DTd-IT) and digital-analogue-driven model transfer (DAd-MT), where the information is collaborated with the model to improve the integrated transfer diagnosis effect under sampling. On one aspect, with the employment of bearing dynamic model responses, DTd-IT is innovatively designed to establish the transfer of dynamic information and measured information. The information distribution difference between these twin data and real measured data is effetely adjusted with the introduced actual inference components, where the twin data with low information distribution difference can be well fusion generated by the information transfer digital twin (ITDT) model. On the other aspect, considering the truth that there are still small sample cases of real measured data and information distribution differences will affect the quality of the twin data, a digital-analogue driven model transfer (DAd-MT) method is further proposed, where the deep branch transfer network (DBTN) model with improved convolutional neural network (CNN) is used to achieve an accurate fault diagnosis effect with the help of digital twin data. Experiments and wear analysis verified that the proposed DTa-DT can significantly reduce the distribution difference between the dynamic model response and the real measured data, thus achieving low-cost and efficient rolling bearing transfer diagnosis compared to other ten state-of-the-art deep learning models. It can be predicted that the proposed dual transfer architecture provides more opportunities for the practical application of intelligent fault diagnosis under small sample sizes. [ABSTRACT FROM AUTHOR]

• The proposed method also considers the noise and integrity of fault information. • VMD decomposition is employed to decompose the each measurement point signal. • CEF coefficient and confidence distance are utilized to evaluate IMFs and raw signal respectively. • The proposed method has advantages over the popular data-level and decision-level fusion methods. Spline tooth root crack is a kind of critical faults for clutch friction disc. Most traditional crack diagnosis methods based on single-sensor or fusion may be degraded due to incomplete fault information or low signal-to-noise ratio (SNR). Aiming at enhancing the ratio of fault-related information in the fusion signal, a new reweighted variational mode decomposition (VMD) multi-point fusion method is proposed. With the proposed method, each measurement point vibration acceleration signal is decomposed by VMD firstly, and the intrinsic mode functions (IMFs) containing different sensitivities information are obtained. Then, correlation energy fluctuation (CEF) and confidence distance are utilized to evaluate the IMFs and raw signal respectively. A new dual-weight strategy is proposed to restructure the IMFs. Finally, convolutional neural network (CNN) is used to identify spline tooth early crack faults. Experimental results demonstrate that the proposed method has greater fault recognition rate than single point and other fusion methods. [ABSTRACT FROM AUTHOR]