本文开发了集辅助激光成像系统、紫外/可见波段视觉成像系统（波长小于780 nm）、光谱测量仪、光电传感器的多传感器系统，用以观察和分析激光焊接过程中的焊接状态信息。本文采用小波包分解方法对通过光电传感器获得的可见光波段传感信号和激光反射传感信号进行分解并提取相关特征。光谱仪采集到的信号的主要波长为400~900 nm，将其分为25个子带并采用统计方法来提取光谱信号特征。利用紫外/可见波段视觉成像系统采集的图像获取金属蒸气和飞溅的特征，而辅助照明视觉传感器系统主要是用来采集匙孔特征。本文基于上述焊接过程的实时量化特征建立了焊接状态监测的深度信念网络（DBN），并采用遗传算法对所提出的DBN模型参数进行优化。与传统的反向传播神经网络（BPNN）模型相比，本文建立的DBN模型在焊接状态监测方面具有更高的精度和鲁棒性。最后，本文通过三个附加焊接试验验证了该方法在激光焊接状态监控中的有效性和泛化能力。

In this research, an auxiliary illumination visual sensor system, an ultraviolet/visible (UVV) band visual sensor system (with a wavelength less than 780 nm), a spectrometer, and a photodiode are employed to capture insights into the high-power disc laser welding process. The features of the visible optical light signal and the reflected laser light signal are extracted by decomposing the original signal captured by the photodiode via the wavelet packet decomposition (WPD) method. The captured signals of the spectrometer mainly have a wavelength of 400–900 nm, and are divided into 25 sub-bands to extract the spectrum features by statistical methods. The features of the plume and spatters are acquired by images captured by the UVV visual sensor system, and the features of the keyhole are extracted from images captured by the auxiliary illumination visual sensor system. Based on these real-time quantized features of the welding process, a deep belief network (DBN) is established to monitor the welding status. A genetic algorithm is applied to optimize the parameters of the proposed DBN model. The established DBN model shows higher accuracy and robustness in monitoring welding status in comparison with a traditional back-propagation neural network (BPNN) model. The effectiveness and generalization ability of the proposed DBN are validated by three additional experiments with different welding parameters.