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Fault diagnosis is vital for improving the reliability and safety of mechanical equipment. Existing fault diagnosis methods require a large number of samples for model training. However, in real-world environments, mechanical equipment usually operates under healthy conditions during most of its service life, resulting in a scarcity of fault samples. To solve this problem, a novel multilayer fusion correntropy representation method combined with a support vector machine is proposed for the fault diagnosis of mechanical equipment. First, the monitoring signal is expanded into multilayer signal components using wavelet packet decomposition. Then, the correlation between the signal components of each layer is expressed by correntropy, and the corresponding correntropy matrix is constructed. After performing the matrix logarithm operator, all correntropy matrices composed of correntropy values are fused into a vector, which is viewed as a feature of the signal. Finally, a support vector machine is established using small samples to realize fault classification. The effectiveness of the proposed method is validated on four public datasets. The results indicate that compared with other methods, the proposed method has advantages in terms of diagnosis accuracy and noise immunity ability. [ABSTRACT FROM AUTHOR]

[Objectives] The bench test is an important means for the design and verification of steam systems,and the accuracy of the boundary condition is a critical factor for studying the characteristics of the bench test.[Methods] In order to propose a feasible dynamic flow boundary control method,based on the change requirements of steam inlet flow in the bench test of a steam system,the two schemes of PID closed-loop control and open-loop control are proposed,and simulation comparison and analysis are carried out. In the GSE simulation system,JTopmeret modules are employed to establish the whole steam test system,the optimization of the modules and correction of parameters are processed on the basis of the data detected in the steam test,and the simulation data is compared with the experimental data to verify the accuracy and validity of the simulation model.[Results] For the control requirements of reducing steam flow linearly by 20 t/h within 10 s,the simulation results show that the open-loop control scheme allows the control of boundary conditions to be achieved after several iterations,while using the traditional PID closed-loop control scheme makes it difficult to control the boundary as required. The test results show that the flow change at the steam inlet meets the test requirements of the bench test,and is consistent with the simulation results.[Conclusions] The results of this research can provide references for the boundary control of steam system bench tests.

Neuron-specific enolase (NSE) has gained extensive attention as a reliable target for detecting small cell carcinoma of lungs. In this paper, an electrochemical immunoassay method based on molybdenum disulfide (MoS2) is proposed to detect NSE sensitively. By an in-situ growth method, MoS2 and Au nanoclusters (Au NCs) were composited to form a MoS2@Au nanozyme, and then the secondary antibodies were modified. Primary antibodies were immobilized on amino-reduced graphene oxides to capture NSE. The flower-like MoS2 nanozyme provided abundant sites to load Au NCs and catalyze the decomposition of H2O2, which were beneficial to amplify an amperometric response as well as build up sensitivity. Under optimum conditions, the detection range of this strategy was 0.1 pg·mL−1–10 ng·mL−1 and the limit of detection was 0.05 pg·mL−1. This sensing strategy achieved the prospect of sensitively detecting NSE. Moreover, the prepared electrochemical immunosensor provides a theoretical basis and technical support for the detection of other disease markers. [ABSTRACT FROM AUTHOR]

As an inflammatory marker, procalcitonin (PCT) is more representative than other traditional inflammatory markers. In this work, a highly efficient photoelectrochemical (PEC) immunosensor was constructed based on the photoactive material Bi2S3/Ag2S to realize the sensitive detection of PCT. Bi2S3 was prepared by a hydrothermal method, and Ag2S quantum dots were deposited on the ITO/Bi2S3 surface via in situ reduction. Bi2S3 is a kind of admirable photoelectric semiconductor nanomaterial on account of its moderate bandgap width and low binding rate of photogenerated electron holes, which can effectively convert light energy into electrical energy. Therefore, based on the energy level matching principle of Bi2S3 and Ag2S, a labeled Bi2S3/Ag2S PEC immunosensor was constructed, and the sensitive detection of PCT was successfully established. The linear detection range of the PEC immunosensor was 0.50 pg∙mL−1 to 50 ng∙mL−1, and the minimum detection limit was 0.18 pg∙mL−1. Compared with the traditional PEC strategy, the proposed PEC immunosensor is simple, convenient, and has good anti-interference, sensitivity, and specificity, which could provide a meaningful theoretical basis and reference value for the clinical detection of PCT. [ABSTRACT FROM AUTHOR]