Benefiting from the progress of artificial intelligence, the deep learning-based data-driven fault diagnosis procedure has gradually become a preferred and reliable option. Although this kind of end-to-end diagnosis mode can achieve satisfactory results in most conventional conditions, the existing methods may not be able to guarantee the diagnostic accuracy when noise is a prominent issue. To address the above problem, the deep rational attention network (DRANet) is developed in this article. In the DRANet, a pseudo soft threshold function is developed to further modulate the attention weights in a convolutional neural network. Besides, the threshold is automatically determined during network optimization. The attention weight modulate strategy driven by the pseudo soft threshold function accords with the idea of threshold denoising in signal processing and thus endows the deep architecture with the ability to further neglect the features with little contribution. Experimental vibration signals coupled with different types of noise are employed to evaluate the efficacy of the proposed DRANet. Results show that in comparison with other methods, the method of this work holds a more outstanding diagnostic performance, especially in the case of low SNR.

Herein, we report a facile strategy for the synthesis of dual-functionalized graphene hybrids (A–H–GNs) based on interactions among graphene, N-(aminobutyl)-N-(ethylisoluminol) (ABEI) and hemin. The multilayers of a chemiluminescence reagent (ABEI) and a catalyst (hemin) were accumulated on the graphene surface, resulting in high chemiluminescence performance, good solubility and stability in aqueous solution.

Condition monitoring and fault diagnosis are of great significance to the development of modern industry, for they enable enterprises to avoid unexpected interruptions or severe accidents, and extracting the fault-related features from vibration signals is a critical step to achieve accurate diagnosis. Among diverse of feature extraction approaches, symbolic aggregate approximation (SAX) is a promising one that has been introduced into fault diagnosis recently. Nevertheless, when dealing with the sampled vibration signals, the SAX ignores the change of signal frequency characteristics, which eventually leads to information aliasing and cannot ensure the information validity. In this work, the information aliasing is analyzed from the perspective of signal processing, and the extremum symbolic aggregate approximation (ESAX) is developed as a substitution on the premise of maintaining the validity of the information. Subsequently, to convert the symbol strings generated by the ESAX into usable digital feature vectors, the bag-of-words (BoW) model in natural language processing (NLP) is employed to perform the counting statistics of the fault-related words, and the Laplacian score (LS) algorithm is then utilized to rerank the statistical results, thereby realizing the extraction of mechanical fault feature. The superiority of the developed method is verified by experiments.