A goal in ultrasonic welding (USW) process monitoring is to accurately predict quality outcomes based on monitored signals. However, in most cases, knowing only that the USW process has failed is insufficient. Modern process automation should assess signal information and intercede to rectify process problems. Identification of when a process signal deviates from an acceptable final quality outcome, i.e., the time at which an abnormal event starts, facilitates control action or root cause analysis to bring it back to compliance. A long short-term memory (LSTM) recurrent neural network is proposed to monitor USW and other time-series signals and identify this point. This deep neural network is trained to classify quality outcomes from continuous signals. The process monitoring signals and their sampling time are divided into finite segments as input to this network. The time segment at which the process signal first converges to the final quality class prediction is identified using cross-entropy of the classification probabilities. This procedure is demonstrated using USW quality monitoring algorithms and robot motion failure detection. The examples show an LSTM network not only provides high accuracy for USW quality prediction, but also that the time of classification convergence is consistent with variance observed in USW weld quality factors. Moreover, classification convergence time was shown to be associated to specific robot motion failures, useful as input to adaptive learning. This work realizes deep-learning driven quality prediction and early event detection for quality classification problems, and provides the information necessary for adaptive control algorithms.

超声波焊接 (USW) 过程监控的一个目标是根据监控信号准确预测质量结果。但是，在大多数情况下，仅知道 USW 过程失败是不够的。现代过程自动化应评估信号信息并进行调解以纠正过程问题。识别过程信号何时偏离可接受的最终质量结果，即异常事件开始的时间，有助于控制措施或根本原因分析，使其恢复合规性。提出了一种长短期记忆（LSTM）循环神经网络来监测 USW 和其他时间序列信号并识别这一点。这个深度神经网络经过训练，可以对连续信号的质量结果进行分类。过程监控信号及其采样时间被分成有限段作为该网络的输入。使用分类概率的交叉熵来识别过程信号首先收敛到最终质量类别预测的时间段。此过程使用 USW 质量监控算法和机器人运动故障检测进行演示。这些示例表明，LSTM 网络不仅为 USW 质量预测提供了高精度，而且分类收敛时间与 USW 焊接质量因素中观察到的方差一致。此外，分类收敛时间被证明与特定的机器人运动失败相关，可用作自适应学习的输入。