A novel fault diagnosis method based on variational mode decomposition (VMD) and multikernel support vector machine (MKSVM) optimized by Immune Genetic Algorithm (IGA) is proposed to accurately and adaptively diagnose mechanical faults. First, mechanical fault vibration signals are decomposed into multiple Intrinsic Mode Functions (IMFs) by VMD. Then the features in time-frequency domain are extracted from IMFs to construct the feature sets of mixed domain. Next, Semisupervised Locally Linear Embedding (SS-LLE) is adopted for fusion and dimension reduction. The feature sets with reduced dimension are inputted to the IGA optimized MKSVM for failure mode identification. Theoretical analysis demonstrates that MKSVM can approximate any multivariable function. The global optimal parameter vector of MKSVM can be rapidly identified by IGA parameter optimization. The experiments of mechanical faults show that, compared to traditional fault diagnosis models, the proposed method significantly increases the diagnosis accuracy of mechanical faults and enhances the generalization of its application.
Pipeline structures are important structural components that cannot be replaced in actual engineering applications. Damage to a pipeline structure will create substantial safety hazards and economic losses in a project. Therefore, it is extremely important to study damaged pipeline structures. In this paper, L(0,2) mode guided waves are used to identify, locate, and image single and double defects in straight pipe structures. For the case where there is a single defect in the straight pipe section, the influence of different excitation frequencies on the reflection coefficient of L(0,2) modal guided wave is studied, and the optimal excitation frequency of L(0,2) guided wave is 70 kHz when single damage is determined. For the case of double defects in the straight pipe section, the double-defect size, the distance between the defects, and the relative defect positions are studied, and the influence of the defect recognition effect is analyzed. The propagation path of the ultrasonic guided wave in the double-defect pipe section is analyzed. Finally, the effectiveness of the three-point axial positioning method and damage imaging method is verified by the single-defect tube segment ultrasonic guided wave flaw detection experiment.
Three‐dimensional photonic crystals (3D PhCs) enable light manipulations in all three spatial dimensions, however, real world applications are still faced with challenges in fabrication. Here, a facile fabrication strategy for 3D silicon PhCs with a simple cubic (SC) lattice structure is presented, which exhibits a complete photonic bandgap at near‐infrared wavelengths of around 1100 nm. The fabrication process is composed of standard deep ultra‐violet stepper lithography, followed by a single‐run modified plasma etch process. By applying a direct dry etch release step at the end of the 3D structural etch process, the fabricated 3D PhCs can be released and transferred in the form of a membrane onto other substrates such as glass, polymers, or even substrates with engineered surface. The thickness of the demonstrated membranes is around 2 µm and the size can be up to a few millimeters. A high reflectivity is observed at the stop band frequency, and a planar defect is introduced during the etching process resulting in an optical resonance mode with a small linewidth of around 30 nm. The structure constitutes an optical bandpass filter and can be used as a sensor for organic solvents.
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