Until now, there is no effective method to detect the early weak structural damage of the aero engine intershaft bearing. In this article, an online method based on acoustic emission technology is used to detect the early weak structural damage of aeroengine intershaft bearing. The combination of maximum correlated kurtosis deconvolution and bandpass filter is proposed to enhance and denoise fault characteristic signals of the aero engine intershaft bearing. The Hilbert envelope demodulation is used to extracting the bear fault characteristic frequency. Under aeroengine working conditions, the experiment of an intershaft bearing with outer ring fault is carried out. The result shows that bearing outer ring fault characteristic frequency and bearing vibration frequencies clearly appear in the envelope spectrum of the acoustic emission signal. Therefore, online detection of early weak structure damage of aeroengine intershaft bearing is realized by acoustic emission technology under the background of strong noise.

This paper propose a new fault identification method based on variable pooling multiscale CNN (VPMCNN), which solves the bearing industrial problem of huge variable features and inherent multiscale characteristics in acoustic emission (AE) signals. First, the pooling projection components (PPCs) of the signals are obtained through the variable pooling layer. The PPCs consider the curse of invariant feature weight in traditional CNN pooling layer, and select the more weighted features to enhance the classifying quality. Second, an improved multiscale fusion feature module is introduced to further extract the hidden features, which is called fused components (FCs). The FCs aims to automatically extract multiple scale features using different filter sizes from raw acoustic signals. Then the GAP (Global Average Pooling) layer is performed to realize classification. Finally, the fault identification using the proposed algorithm is performed by testing the bearing AE signals with single operating condition and variable operating conditions, and the results show the effectiveness of the proposed method, compared with existing AE based bearing fault identification methods.

A four-loop shaped structure of fiber Bragg grating (FBG) acoustic emission (AE) sensor based on additive manufacturing (AM) technology is proposed in the letter. The finite element analysis (FEA) method was used to model and analyze the sensor structure. We aimed at improving the sensitivity, the static load analysis, and the dynamic response analysis of the normal FBG acoustic emission sensor and the FBG AE sensor with improved structure parameters. We constructed the FBG AE sensor experimental system based on a narrowband laser demodulation method and test on real acoustic emission signals. The results demonstrated that the response sensitivity of the FBG acoustic emission sensor was 1.47 times higher than the sensitivity of the normal FBG sensor. The sensitivity coefficient of PLA-AE-FBG2 sensor was 3.057, and that of PLA-AE-FBG1 was 2.0702. Through structural design and parameter optimization, the sensitivity and stability of the FBG AE sensor are improved. The four-loop shaped sensor is more suitable for the health monitoring in fields such as aero-engine blade, micro-crack of structure, and crack growth in bonded joints. While ensuring the sensing characteristics, sensitivity, and stability of the four-loop shaped sensor have been enhanced. It is possible to apply the FBG AE sensor in some complex engineering environments.

The crack growth process in metals can be continuously monitored and recorded in real time using highly sensitive acoustic emission technology. To predict this complex behavior, an improved largest Lyapunov exponent method, enhanced by a small data optimization approach named the small data method, is applied. Meanwhile, experimental results reveal that the acoustic emission signals collected during the fatigue crack growth process display distinct chaotic characteristics. By employing the small data method and cross-comparison analysis with the traditional Wolf method, the prediction accuracy and efficiency are significantly enhanced, even when only a limited amount of data is available. Compared to conventional techniques, this method demonstrates higher reliability and robustness, offering a powerful tool for early-stage monitoring and life prediction of metallic materials.

Gears, as critical components of rotating machinery, are prone to wear and fracture due to their complex structural dynamics and harsh operating conditions, leading to catastrophic failures, economic losses, and safety risks. AE technology enables real-time fault diagnosis by capturing stress wave emissions from material defects with high sensitivity. However, mechanical background noise significantly corrupts AE signals, while optimal selection of gear health indicators remains challenging, critically impacting fault feature extraction accuracy. This study develops an adaptive feature extraction method for fault diagnosis using AE. Through gear fault simulation experiments, VMD analyzes mode number and penalty factor effects on signal decomposition. Correlation coefficient-based reconstruction optimization is implemented. For feature selection challenges, SVM-RFE enables adaptive parameter ranking. Finally, SVM with optimized kernel parameters achieves effective fault classification. Optimized VMD enhances signal decomposition, while SVM-RFE reduces feature dimensionality, addressing manual selection uncertainty and computational redundancy. Experimental results demonstrate superior accuracy in gear fault classification. This study proposes an AE-based adaptive feature extraction method with three innovations: (1) establishing VMD parameter–decomposition quality relationships; (2) developing an SVM-RFE feature selection framework; (3) achieving high-accuracy gear fault classification. The method provides a novel technical approach for rotating machinery diagnostics with significant engineering value.

With the rapid evolution of artificial intelligence technologies in power systems, data-driven time-series forecasting has become instrumental in enhancing the stability and reliability of power systems, allowing operators to anticipate demand fluctuations and optimize energy distribution. Despite the notable progress made by current methods, they are still hindered by two major limitations: most existing models are relatively small in architecture, failing to fully leverage the potential of large-scale models, and they are based on fixed nonlinear mapping functions that cannot adequately capture complex patterns, leading to information loss. To this end, an LLM-Empowered Kolmogorov–Arnold frequency learning (LKFL) is proposed for time series forecasting in power systems, which consists of LLM-based prompt representation learning, KAN-based frequency representation learning, and entropy-oriented cross-modal fusion. Specifically, LKFL first transforms multivariable time-series data into text prompts and leverages a pre-trained LLM to extract semantic-rich prompt representations. It then applies Fast Fourier Transform to convert the time-series data into the frequency domain and employs Kolmogorov–Arnold networks (KAN) to capture multi-scale periodic structures and complex frequency characteristics. Finally, LKFL integrates the prompt and frequency representations through an entropy-oriented cross-modal fusion strategy, which minimizes the semantic gap between different modalities and ensures full integration of complementary information. This comprehensive approach enables LKFL to achieve superior forecasting performance in power systems. Extensive evaluations on five benchmarks verify that LKFL sets a new standard for time-series forecasting in power systems compared with baseline methods.