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The complex operating environment of gearboxes and the easy interference of early fault feature information make fault identification difficult. This paper proposes a fault diagnosis method based on a combination of whale optimization algorithm (WOA), variational mode decomposition (VMD), and deep transfer learning. First, the VMD is optimized by using the WOA, and the minimum sample entropy is used as the fitness function to solve for the K value and penalty parameter α corresponding to the optimal decomposition of the VMD, and the correlation coefficient is used to reconstruct the signal. Second, the reconstructed signal after reducing noise is used to generate a two-dimensional image using the continuous wavelet transform method as the transfer learning target domain data. Finally, the AlexNet model is used as the transfer object, which is pretrained and fine-tuned with model parameters to make it suitable for early crack fault diagnosis in gearboxes. The experimental results show that the method proposed in this paper can effectively reduce the noise of gearbox vibration signals under a complex working environment, and the fault diagnosis method of using transfer learning is effective and achieves high accuracy of fault diagnosis. [ABSTRACT FROM AUTHOR]

Gearboxes have developed rapidly, and studies on gearbox degradation assessment are of great significance. In the interest of obtaining an effective gearbox degradation feature, a new methodology based on sparse representation and compressive sensing theory is proposed. The wavelet packet transform is an excellent time-frequency analysis method, and the subbands with the largest energy are selected for training the sparse dictionaries. Furthermore, the overcomplete dictionaries of each degradation state can well reflect the signal structure by calculating the compressive sensing reconstruction residuals. Based on the sparse representation feature, a Euclidean distance technique is performed to calculate the assessment rate for each degradation state. The validity and superiority of the proposed assessment method are validated by crack failure data and broken tooth failure data under three kinds of work loads. A comparison of the sparse representation feature with the features of wavelet packet energy, kurtosis, and Renyi entropy is also performed. [ABSTRACT FROM AUTHOR]

In view of the characteristics of multi-parameter for the 1-dimensional convolutional neural network (CNN), a parameter optimization method combining orthogonal test and pastille swarm optimization algorithm is proposed and applied to the fault diagnosis of compressed vibration signals. Compressive sensing theory breaks through the limitation of Nyquist sampling theorem and provides an effective way for the collection and transmission of a large number of vibration signals. Firstly, a CNN fault diagnosis model based on compressed signal is established by using the "end-to-end" feature of CNN. The orthogonal test is used to make a rough evaluation of the parameter range, and the best scheme is selected. For each parameter in the scheme, the multi-objective particle swarm optimization algorithm is used to refine it, and the optimal value of each parameter is obtained. The measured signal of gear box and bearing signal from Case Western Reserve University are selected as the research objects. The experimental results show that after optimization, the output characteristic classification of non-inferior parties is obvious, and the CNN diagnosis rate is significantly improved. The results also prove the feasibility of direct fault diagnosis for compressed signals. [ABSTRACT FROM AUTHOR]

The Fontan operation is the current standard of care for single-ventricle congenital heart disease. Individuals with a Fontan circulation (FC) exhibit central venous hypertension and face life-threatening complications of hepatic fibrosis, known as Fontan-associated liver disease (FALD). The fundamental biology and mechanisms of FALD are little understood. Here, we generated a transcriptomic and epigenomic atlas of human FALD at single-cell resolution using multiomic snRNA-ATAC-seq. We found profound cell type–specific transcriptomic and epigenomic changes in FC livers. Central hepatocytes (cHep) exhibited the most substantial changes, featuring profound metabolic reprogramming. These cHep changes preceded substantial activation of hepatic stellate cells and liver fibrosis, suggesting cHep as a potential first "responder" in the pathogenesis of FALD. We also identified a network of ligand-receptor pairs that transmit signals from cHep to hepatic stellate cells, which may promote their activation and liver fibrosis. We further experimentally demonstrated that activins A and B promote fibrotic activation in vitro and identified mechanisms of activin A's transcriptional activation in FALD. Together, our single-cell transcriptomic and epigenomic atlas revealed mechanistic insights into the pathogenesis of FALD and may aid identification of potential therapeutic targets. Editor's summary: Fontan surgery is a reconstructive heart surgery performed on individuals, often children, with a form of congenital heart disease. For unclear reasons, the procedure commonly results in liver fibrosis that is morphologically distinct from that observed in metabolic-associated liver injury. Hu et al. did single-nucleus transcriptome and epigenome profiling of human Fontan-associated liver disease to interrogate this disease pathology and found early metabolic reprogramming in central hepatocytes and signs of profibrotic signaling to hepatic stellate cells by this subpopulation of hepatocytes. This work forms an atlas for the basis of future work on this poorly understood condition. —Catherine Charneski [ABSTRACT FROM AUTHOR]