In recent years, artificial neural networks have been widely used in the fault diagnosis of rolling bearings. To realize real-time diagnosis with high accuracy of the fault of a rolling bearing, in this paper, a bearing fault diagnosis model was designed based on the combination of VMD and ANN, which ensures a higher fault prediction accuracy with less computational time. This paper works from two aspects, including fault feature extraction and neural network structural parameter optimization to obtain an ANN bearing fault diagnosis model with high performance. The raw vibration signals of 10 fault types were divided into training, verification and testing datasets by the random step increment slip method. The variational mode decomposition method was used to decompose the raw vibration signal into several intrinsic mode functions. A new definition of the energy of each intrinsic mode function based on discrete Fourier transform and information entropy method were used as the input for the artificial neural network. Furthermore, the structural parameters of the artificial neural network were designed to obtain a high-performance neural network. The artificial neural network used in this paper had three hidden layers and 13 neurons in each hidden layer. Compared with several machine and deep learning algorithms, the artificial neural network can better fulfill the classification task of rolling bearing fault types with a mean prediction accuracy of 99.3% and computation time of 2.4 s based on a small training dataset.
Based on the small time scale method, the influence of grain boundary on the fatigue crack growth of 7050-T7451 has been investigated. The interaction between fatigue crack and grain boundary was investigated by in situ SEM testing. Results showed that the fatigue crack growth will be retarded by grain boundary when the angle between fatigue crack and grain boundary is greater than 90 degrees. Mechanism analysis showed that the fatigue crack tip would not be able to open until the loading reached the 55% of maximum load, and the fatigue crack had been closed completely before the loading was not reduced to the minimum value, which led to the crack growth retardation. When the 7050-T7451 aluminum alloy suffered from fatigue loading with constant amplitude, a behavior of unstable fatigue crack growth could be observed often, and results indicated that the bridge linked mechanism led to the behavior. The grain boundary was prone to fracture during fatigue loading, and it became the best path for the fatigue crack growth. The fatigue crack tip would be connected with fractured grain boundary eventually, which led to the fast crack growth in different loading stage.
The filtered-x recursive least square (FxRLS) algorithm is widely used in the active noise control system and has achieved great success in some complex de-noising environments, such as the cabin in vehicles and aircraft. However, its performance is sensitive to some user-defined parameters such as the forgetting factor and initial gain. Once these parameters are not selected properly, the de-noising effect of FxRLS will deteriorate. Moreover, the tracking performance of FxRLS for mutation is still restricted to a certain extent. To solve the above problems, this paper proposes a new proportional FxRLS (PFxRLS) algorithm. The forgetting factor and initial gain sensitivity are successfully reduced without introducing new turning parameters. The de-noising level and tracking performance have also been improved. Moreover, the momentum technique is introduced in PFxRLS to further improve its robustness and de-noising level. To ensure stability, its convergence condition is also discussed in this paper. The effectiveness of the proposed algorithms is illustrated by simulations and experiments with different user-defined parameters and time-varying noise environments.
Purpose Crack damage detection for aluminum alloy materials using fiber Bragg Grating (FBG) sensor is a kind of structure health monitoring. In this paper, the damage index of full width at half maximum (FWHM) was extracted from the distorted reflection spectra caused by the crack-tip inhomogeneous strain field, so as to explain the crack propagation behaviors. Design/methodology/approach The FWHM variations were also investigated through combining the theoretical calculations with simulation and experimental analyses. The transfer matrix algorithm was developed to explore the mechanism by which FWHM changed with the linear and quadratic strain. Moreover, the crack-tip inhomogeneous strain field on the specimen surface was computed according to the digital image correlation measurement during the experiments. Findings The experimental results demonstrated that the saltation points in FWHM curve accorded with the moments of crack propagation to FBG sensors. Originality/value The interpretation of reflected spectrum deformation mechanism with crack propagation was analyzed based on both simulations and experiments, and then the performance of potential damage features – FWHM were proposed and evaluated. According to the correlation between the damage characteristic and the crack-tip location, the crack-tip of the specimen could be measured rapidly and accurately with this technique.
Abstract. This paper presents an application of Structural Health Monitoring System based on Fiber Bragg Grating sensors (FBGs) dedicated to wing-bending moment. A numerical calculation of bending moment is proposed to the application of real-time wing-bending moment monitoring. With the advantage of anti-electromagnetic interference, small size and light weight, Fiber Bragg grating (FBG) sensors have been applied in structural health monitoring system (SHMS). An experiment was performed in full-scale fatigue test of an aircraft, and the wings of aircraft were subjected to specific loading conditions, and the strain data was collected by FBGs' demodulation. The relationship matrix between the strain and the wing-bending moment was established. It is a new approach for the wing-bending moment real-time monitoring with the simple FBG strain collection modulation.
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