Anomaly Detection of Automobile Welding Workstation Based on Stacked Autoencoders and Long Short-Term Memory Network

The automobile welding workstation is an important part of vehicle production, and the reliable operation of a large number of vulnerable non-standard parts directly affects the processing quality of the product. Therefore, it is necessary to monitor its real-time status to avoid economic losses caused by unexpected shutdowns. However, the current regular maintenance method relies on the mature experience of the technicians, the cost is high and the maintenance effect is not ideal. In addition, massive amounts of data have also caused difficulties for effective analysis. To solve this problem, an intelligent method named Stacked Autoencoder and Long short-term memory (SAE-LSTM) is proposed for fault detection. First, the time-domain signals, frequency signals, and time-frequency signals are collected by the Visual Components software. Second, the data preprocessing module will be used to initially remove useless system signals. Third, the SAE network is further used to reduce the dimensionality of massive data and maintain the key features of the original system. Finally, the LSTM network is used to predict the upcoming data through real-time collected data and performs fault detection by comparing it with the real data. The proposed method is validated by the real automobile welding workstation. The experimental results demonstrate that the proposed approach can effectively detect equipment failures.