Resistance spot welding is an important process in the production of body-in-white. The quality of the welded nugget affects the safety performance of the whole vehicle. Currently, the quality of the ...welded nugget is mainly inspected manually, which is labor-intensive and inefficient. Therefore, this paper explores a new method to automatically and efficiently detect the quality of welded nugget by analyzing the vibration excitation response signal of the welded joint. In response to the characteristics of large volume, significant noise, and small discriminability of raw signals, we constructed a deep learning model named Real Spatial–temporal Attention Denoising Network (RSTADN), which consists of a denoising module, spatial–temporal attention modules, and multiple residual modules. The denoising module uses global absolute average pooling (GAAP) to maximize the retention of the original signal characteristics while aggregating global information from each channel. It generates appropriate soft thresholds to eliminate noise and enhance the feature recognition ability of the model. The spatial–temporal attention modules delve into the spatiotemporal correlation features of the signal from different perspectives, including real spatial scale, short-term dependency, and global temporal interaction, to enhance the model's feature extraction ability. Multiple residual modules further extract signal features to achieve precise alignment between features and nugget quality states. The experimental results indicate that the accuracy of RSTADN in the task of detecting the quality of welded nugget reaches as high as 94.35%, which is at least 1.31% higher than that of existing models.
Viscoelastic sandwich structure plays an important role in mechanical equipment, nevertheless viscoelastic material inevitably suffers from gradual aging. For guaranteeing the operation safety of ...mechanical equipment, it is urgent to perform the aging state detection of viscoelastic sandwich structure with vibration response signal analysis. However, the structural vibration response signal is non-stationary and its variation caused by the structural aging state change is very puny, and the abnormal state samples is lacking. The vibration-based structural aging state detection has become a challenging task. Therefore, a novel method based on redundant second generation wavelet packet transform (RSGWPT) and fuzzy support vector data description (FSVDD) is proposed for this task. For extracting sensitive aging feature information, RSGWPT is introduced to process the structural vibration response signal, and multiple energy features are extracted from the frequency-band signals to reflect structural aging state change. For accurate and automatic aging state identification, by fusing fuzzy theory, FSVDD only uses the normal state samples for training and can identify the abnormal severity degrees is developed to identify the structural aging states. The proposed method is applied on a viscoelastic sandwich structure to validate its effectiveness, and different structural aging states are created through the accelerated aging of viscoelastic material. The analysis results show the outstanding performance of the proposed method.
