A low energy chirp-pulsed radar thermography (CP-RT) was used as a nondestructive inspection technique to detect the subsurface delamination of CFRP composite. Laser intensities were modulated according to a chirp-pulsed radar signal, and the temperature rise is controlled within 2 Degree. The thermal-wave signal was processed by different post-processing characteristic extraction algorithms. These algorithms include time-frequency algorithms (Cross-correlation, CC, fast Fourier transform, FFT, and dual-orthogonal demodulation, DOD) and statistical analysis approaches (Principal component analysis, PCA and PCA based reconstructed independent component analysis, PCA-RICA). The SNRs of defects were employed to evaluate the defect detectability of different post processing algorithms. Two kind of 3D tomography methods were proposed. FFT phase presents a high SNR and has a good correlation with the depth of the defect. FFT based CP-RT has the potential to provide a rapid NDT&E and 3D tomography approach for CFRP with subsurface defects under low energy excitation condition.

Rolling bearings are essential transmission and support components in aircraft engines, playing a critical role in ensuring their safe and stable operation. Rolling bearing faults have a significant impact and should not be ignored. The effective diagnosis of bearing faults has always been a critical requirement for ensuring reliable operation. With the increasing demands of modern manufacturing to reduce costs and improve quality, the development of advanced bearing fault detection methods has become indispensable. This paper presents the brief review of recent trends in research on bearing failure modes, bearing fault detection and diagnosis methods, and development trends and prospects. This article provides a comprehensive review of the existing fault diagnosis methods for rolling bearings in four aspects: the integration of advanced sensor technology and advanced data processing technology, multimodal fusion, intelligent detection, and real-time monitoring. It discusses methods based on vibration analysis, acoustic methods, current-based methods, electromagnetic methods, infrared methods, radiographic methods, visual methods, and intelligent detection methods. This study reveals that the application of intelligent detection technology, multimodal fusion detection technology, and real-time online monitoring technology has achieved favorable results. In the future, bearing fault detection will develop in a more intelligent, integrated, and real-time direction.

In this paper, we demonstrated a novel thermal-wave radar imaging approach with use of a dual-directional (down then up) chirp (or linear frequency modulation, LFM) modulated laser as an external excitation source and signal processing by Fractional Fourier transform (FrFT), which can enhance the defect detectability and extend the depth-resolution dynamic range. The thermal-wave signal was reconstructed by use of dimensionless normalization scaling (DNS) method, and furthermore, it explored the centralized feature of energy spectral density in FrFT domain. The amplitude and phase angle at the peak energy density in FrFT domain were extracted to form the corresponding image and used for the defect detection and identification. The experiments were carried over a carbon fiber reinforced polymer (CFRP) specimen with the artificial flat bottom holes (FBHs) to validate the defect detection capability using FrFT based enhanced TWRI compared to the FFT based TWRI or conventional lock-in thermography (LIT) by taking the defect signal to noise ratio (SNR) into account.

In this study, transverse heat flow suppression (THFS) technique was proposed to enhance the ability of thermal-wave radar thermography (TWRT) to resist lateral thermal diffusion and uneven heating. The enhanced TWRT was used to detect subsurface defects of carbon fiber reinforced plastic (CFRP) laminates. The principle of THFS was described in detail by the optical flow analogy. The three-dimension (3-D) thermal-wave model stimulated by the uneven linear frequency modulation (LFM) thermal flow was introduced. The thermal-wave signal was processed by several different postprocessing characteristic extraction algorithms [Cross-correlation algorithm (CC), dual-orthogonal demodulation algorithm (DOD), fractional Fourier transform (FrFT), and principal component analysis (PCA)]. The comparison between normalized DOD amplitude/phase and normalized DOD-THFS amplitude/phase was carried out. The simulation results depicted THFS can significantly improve the difference between defect location and nondefect location. Nine CFRP specimens with artificial flat-bottom holes (FBHs) were prepared for nondestructive testing and evaluation (NDT&E) by enhanced TWRT. Seventy-two FBHs were prepared to test the probability of detection (PoD) of the enhanced TWRT. Hit/miss method was used to count defect information. The results demonstrated that the enhanced TWRT can realize the effective detection of defects (90% detection probability) with a diameter depth ratio of 5.06 under 95% confidence level. Compared with two state-of-the-art approaches, the proposed DOD-THFS phase has a better defect detection signal-to-noise ratio (SNR).