Digital image correlation (DIC) measurement methods are widely accepted in the field of aeroengine blade measurements. However, there are many factors affecting the accuracy of DIC in engine blade high-temperature measurements, such as thermal radiation and thermal disturbance. To solve these problems, we propose an image preprocessing method to improve the accuracy of high-temperature DIC measurements. A series of high-temperature experiments are performed. The experimental results show that the proposed method can effectively eliminate the influence of thermal radiation and thermal disturbance caused by the high-temperature environment. The method reduces the displacement error, which can be eliminated by ∼70%. Experimental results verified the effectiveness of the proposed method.
The hyperspectral image (HSI) has the characteristics of high resolution, a large amount of data, and a high correlation of bands. In the many HSI processing technologies, image classification is the most basic one. Supervised classification is the most effective and common classification method. However, to achieve the ideal classification effect, supervised classification needs a large number of labeled samples, which requires a lot of time and labor. To solve this problem, we combine active learning (AL) and transfer learning and propose an iterative weighted framework based on active transfer learning. First, we solve the optimal reconstruction matrix and projection matrix by minimizing the reconstruction error. Then, we project labeled samples from the source and target domains into the common subspace. In the iteration of the common subspace, the classifier performance will be better with the increase of iteration number. In each iteration, the weighted strategy is adopted to weigh the samples of the source domain. In this way, valuable source domain labeled samples will get a larger weight, so as to help the classification of target domain samples better. At the same time, AL is used to screen out a certain number of samples of the target domain for manual labeling, which is added to the labeled samples set. Experiments on three data sets demonstrate the effectiveness and reliability of the proposed method.
In order to accurately monitor the working temperature of turbine blades, a signal processing technology for simulated blades is proposed in the paper. The method includes four steps: obtain the function relationship of voltage and temperature by fitting; segment the waveform of each circle by synchronization signals; filter the noise signal by Fast Fourier Transformation (FFT) and Butterworth low-pass filter; then restructure two-dimensional temperature by mapping temperature data into the polar coordinates. Finally, compared with the true temperature measured by thermocouple, the absolute temperature error after signal processing is no more than 4oC at temperatures range from 550oC to 1000oC, providing valuable guidelines for condition monitoring and fault diagnosis.
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