A Novel Bearing Fault Diagnosis Method Based on Few-Shot Transfer Learning across Different Datasets.

At present, the success of most intelligent fault diagnosis methods is heavily dependent on large datasets of artificial simulation faults (ASF), which have not been widely used in practice because it is often costly to obtain a large number of samples in reality. Fortunately, various faults can be easily simulated in the laboratory, and these simulated faults contain a lot of fault diagnosis knowledge. In this study, based on a Siamese network framework, we propose a bearing fault diagnosis based on few-shot transfer learning across different datasets (cross-machine), using the knowledge of ASF to diagnose bearings with natural faults (NF). First of all, the model obtains a good feature encoder in the source domain, then defines a fault support set for comparison, and finally adjusts the support set with a very small number of target domain samples to improve the fault diagnosis performance of the model. We carried out experimental verification from many aspects on the ASF and NF datasets provided by Case Western Reserve University (CWRU) and Paderborn University (PU). The results show that the proposed method can fully learn diagnostic knowledge in different ASF datasets and sample numbers, and effectively use this knowledge to accurately identify the health state of the NF bearing, which has strong generalization and robustness. Our method does not need second training, which may be more convenient in some practical applications. Finally, we also discuss the possible limitations of this method.

Anti-IgLON5 disease: a novel topic beyond neuroimmunology.

Anti-IgLON5 disease is a recently defined autoimmune disorder of the nervous system associated with autoantibodies against IgLON5. Given its broad clinical spectrum and extremely complex pathogenesis, as well as difficulties in its early diagnosis and treatment, anti-IgLON5 disease has become the subject of considerable research attention in the field of neuroimmunology. Anti-IgLON5 disease has characteristics of both autoimmunity and neurodegeneration due to the unique activity of the anti-IgLON5 antibody. Neuropathologic examination revealed the presence of a tauopathy preferentially affecting the hypothalamus and brainstem tegmentum, potentially broadening our understanding of tauopathies. In contrast to that seen with other autoimmune encephalitis-related antibodies, basic studies have demonstrated that IgLON5 antibody-induced neuronal damage and degeneration are irreversible, indicative of a potential link between autoimmunity and neurodegeneration in anti-IgLON5 disease. Herein, we comprehensively review and discuss basic and clinical studies relating to anti-IgLON5 disease to better understand this complicated disorder.