Damage detection and the classification of carbon fiber-reinforced composites using non-destructive testing (NDT) techniques are of great importance. This paper applies an acoustic emission (AE) technique to obtain AE data from three tensile damage tests determining fiber breakage, matrix cracking, and delamination. This article proposes a deep learning approach that combines a state-of-the-art deep learning technique for time series classification: the InceptionTime model with acoustic emission data for damage classification in composite materials. Raw AE time series and frequency-domain sequence data are used as the input for the InceptionTime network, and both obtain very high classification performances, achieving high accuracy scores of about 99%. The InceptionTime network produces better training, validation, and test accuracy with the raw AE time series data than it does with the frequency-domain sequence data. Simultaneously, the InceptionTime model network shows its potential in dealing with data imbalances.

This study investigated the mechanism of delamination damage in the double cantilever beam (DCB) standard test by the use of the strain energy release rate. The curve of the strain energy release rate was verified by the Rise Angle (RA) method. For this purpose, 24-layer carbon fiber/epoxy multidirectional laminates with interface orientations of 0°, 30°, 45°, and 60° were fabricated according to the standard ASTM D5528(13). In the course of this test, acoustic emission (AE) was used for real-time monitoring, and combined with micro visualization, the damage mechanism of composite multidirectional laminates was studied at multiple scales. Combining the AE detection results with micro visualization, it is found that the AE parameters and the damage to multidirectional laminates could realize a one-to-one correspondence. Through the study of the variation of the RA value, load, and strain energy release rate with the crack length, it is proved that the AE parameters can effectively characterize the initiation of delamination damage.

This study characterized the damage characteristics of carbon fiber composite pressure vessels by the modal acoustic emission (MAE) method. The study showed how to use the extracted damage modal features and established MAE parameters to determine the damage mode of composite pressure vessels. First, the A₀ and S₀ Lamb modes of the AE signal were split through mode separation, and the time window was selected to establish the MAE characteristic parameters. Subsequently, based on the MAE parameters and the damage mode characteristics established from single damage experiments, a damage mode discrimination method was established. A bending test of carbon fiber composite laminates proved that the modal separation method and the MAE parameters establishment are reasonable and effective. The results from the hydraulic test of the graded loading performed on 20 MPa carbon fiber composite pressure vessels showed the accuracy of the damage mode discrimination method, and the damage state of the pressure vessel could be analyzed using the fiber fracture damage threshold according to the MAE parameters.

This paper investigates the failure of a regulating valve stem in a petrochemical plant, which was mainly caused by vibration fatigue under small opening conditions. The fractured valve stem was analyzed using macroscopic analysis, chemical composition analysis, mechanical property analysis, metallographic analysis, fracture surface observation, and energy spectrum analysis. Additionally, fluid-structure interaction (FSI) modal analysis was used to investigate the failure of the regulating valve. The results indicate that the valve opening had a direct impact on the vibration of the valve body, which, when operated at small openings, led to fatigue fracture at the step of variable cross-section. The paper suggests a smooth transition treatment be performed at the variable cross-section of the valve stem to avoid stress concentration. Although this study is limited to a specific case, it provides valuable insights for the failure analysis of valves operating at small openings.

Acoustic emission (AE) technology can capture early weak faults in rolling bearings. However, due to the nonstationary nature introduced by high sampling rates, traditional methods often struggle to extract useful features effectively. Existing models mostly use only one type of domain information and lack multidomain cooperation. To solve this problem, this article proposes a deep learning framework called wide-kernel dual-domain bidirectional residual network (WDBRN). It combines wide kernel convolution with dual-domain adaptive fusion. The framework builds two parallel branches in the time domain and the frequency domain. It uses wide convolutional kernels to get a large receptive field. It also uses a bidirectional gated recurrent unit module to capture temporal feature evolution and spectral correlations. An adaptive gated fusion mechanism is designed to adjust the weights of time and frequency features based on signal quality. This allows the model to use information from both domains together. Experimental results show that the proposed method gets an average diagnosis accuracy of 98.72% under different speed conditions. Notably, WDBRN outperforms convolutional neural network (CNN)-long short-term memory (LSTM) and multi-scale convolution neural network (MSCNN) in both classification accuracy and feature clustering quality. This study provides a practical engineering reference for AE signal-based bearing fault diagnosis in key petrochemical rotating machinery operating under high noise environments.

The pyrolysis furnace, a critical component in a pyrolysis unit, inevitably faces operational challenges during its use. This study investigates a case of pyrolysis furnace failure, particularly focusing on an occurrence at the 90° lug elbow and furnace tube weld. The failure, characterized by a comprehensive fracture of the furnace tube in the circumferential direction along the weld vicinity, transpired within a timeframe significantly shorter than one-third of the design life. To unravel the root cause, a series of experiments was conducted on a sample extracted from the failed tube. These experiments, comprising visual inspection, chemical composition analysis, metallographic examination, microstructure analysis, fracture scanning electron microscopy, and energy spectrum analysis, collectively aimed at a comprehensive understanding of the failure mechanisms. The results disclosed that the fracture between the lug elbow and the inlet pipe stemmed from the presence of porosity and inclusions in the butt weld. The initiation of cracks was traced to the pores and inclusions in the fusion line of the inner wall of the pyrolysis tube, extending to connect with the pores in the heat-affected zone on the side of the pyrolysis tube parent material. Subsequently, under the influence of high temperature and stress, the cracks propagated, crept, and expanded along the circumference of the pyrolysis tube parent material until the final fracture occurred. In light of these findings, practical recommendations are proposed to mitigate the risk of similar failures in the future.