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Fault diagnosis of rolling bearings has attracted extensive attention in industrial fields, which plays a vital role in guaranteeing the reliability, safety, and economical efficiency of mechanical systems. Traditional data-driven fault diagnosis methods require obtaining a dataset of full failure modes in advance as the training data. However, this kind of dataset is not always available in some critical industrial scenarios, which impairs the practicability of the data-driven fault diagnosis methods for various applications. A digital twin, which establishes a virtual representation of a physical entity to mirror its operating conditions, would make fault diagnosis of rolling bearings feasible when the fault data are insufficient. In this paper, we propose a novel digital twin-driven approach for implementing fault diagnosis of rolling bearings with insufficient training data. First, a dynamics-based virtual representation of rolling bearings is built to generate simulated data. Then, a Transformer-based network is developed to learn the knowledge of the simulated data for diagnostics. Meanwhile, a selective adversarial strategy is introduced to achieve cross-domain feature alignments in scenarios where the health conditions of the measured data are unknown. To this end, this study proposes a digital twin-driven fault diagnosis framework by using labeled simulated data and unlabeled measured data. The experimental results show that the proposed method can obtain high diagnostic performance when the real-world data is unlabeled and has unknown health conditions, proving that the proposed method has significant benefits for the health management of critical rolling bearings. • A digital twin-driven intelligent diagnosis method is developed. • A high-fidelity digital twin model is built for the rolling bearing. • A partial domain adaptation algorithm is introduced for bearing condition assessment. • One test is conducted to validate the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

Deep learning-based fault diagnosis methods have made tremendous progress in recent years; however, most of these methods are coarse grained and data demanding that cannot find the root causes of mechanical system failures at a finer granularity with limited fault data. Therefore, in this study, we first investigate the few-shot fine-grained fault diagnosis (FSFGFD) problem, with the aim of identifying novel fine-grained faults under different working conditions using only few samples from each class. To address the difficulties of fine-grained fault feature extraction and poor model generalization to unseen few-shot faults in FSFGFD tasks, a novel attention-based deep meta-transfer learning (ADMTL) method is proposed. First, the failure modes under different working conditions are considered as fine-grained faults, and their raw signals are transformed into time–frequency images. Based on this, an attention mechanism is introduced to guide the feature extractor of the ADMTL on what information to learn. The ADMTL then follows a three-stage learning process of pre-training, meta-transfer, and meta-adaptation to achieve fast adaptation to new fine-grained faults using a priori knowledge gained from known faults. Furthermore, a parameter modulation strategy is employed to adaptively update the pre-trained network during the meta-transfer process. The comprehensive experimental results of three case studies demonstrate the superiority of our method over state-of-the-art methods. The proposed method achieves excellent performance with an average accuracy of 99.08%, 95.86%, and 77.74% for FSFGFD tasks when performing meta-transfer within the same machine and between different machines, respectively. [ABSTRACT FROM AUTHOR]

Fretting wear has a considerable effect on the fatigue properties of clinched joints and is the most important cause of fatigue failure in them. Foam metal is a new type of structural material that can be sandwiched into a clinched joint to improve its mechanical properties with respect to fretting wear. In the work reported here, clinched single-lap and cross-lap joints with and without foam copper layers were prepared and subjected to static load testing. Fatigue tests were conducted on the single-lap joints to analyse the fatigue properties and failure modes of the aluminium-alloy clinched and foam metal sandwich clinched joints. The damage caused in single-lap joints by fretting wear was assessed by examining joint cross-sections before completely fatigue broken. Scanning electron microscopy and energy spectrometry were used to examine the failure fractures of the two kinds of joints and to investigate the fretting wear failure mechanism. The incorporation of the foam copper layer increased the shear strength of the clinched joint by 5% but had little effect on the peeling strength. It also increased the energy absorption capacity of the joint under a shear force by 33%, but reduced it under a peeling load by 27%. The fretting wear of the clinched joint occurred mainly at the contact surface of the two sheets in the lap area and at the clinched neck. Fretting wear generated oxide wear debris and caused micro-cracks on the sheet surface and these micro-cracks then continuously expanded until sheet fracture failure occurred. The foam copper layer effectively reduced the fretting wear between the upper and lower sheets and greatly enhanced the fatigue life of the aluminium-alloy clinched joint. [ABSTRACT FROM AUTHOR]