Welding defect real-time detection method and system based on high-frequency time sequence data

Abstract

The invention discloses a welding defect real-time detection method and system based on high-frequency time sequence data. The detection method comprises the steps of firstly sampling the collected high-frequency welding time sequence data according to a set window length, marking a defect occurrence time period and a defect type for each sample, and generating a data sample set; training a ResNet and TCN fusion network model by using the generated data sample set to obtain a trained detection model; and finally, obtaining new real-time high-frequency welding data, inputting the new real-time high-frequency welding data into the trained detection model for prediction according to a set window length, and outputting a welding defect category in real time. According to the method, the ResNet network and the TCN are subjected to network structure fusion, the ResNet can be applied to the field of time sequence detection, and for high-frequency welding time sequence data with a large data size and a long sequence length, the training speed is increased in the training process through a parallel convolution calculation mode, and strong real-time prediction is achieved in the prediction process.

Description

technical field [0001] The invention relates to a real-time detection method and system for welding defects based on high-frequency time series data, belonging to the technical field of automatic welding. Background technique [0002] Intelligent welding is one of the most important research topics in the field of intelligent manufacturing, and sensing technology and its information processing are the key elements to realize the intelligentization and automation of the welding process. In recent years, sensing technologies with the characteristics of miniaturization, non-contact and large transmission capacity have been more applied to the real-time control of welding process and quality, such as arc sensing, visual sensing, sound sensing, spectral sensing Wait. These sensors use different information sources to obtain large-scale information related to welding quality. How to mine effective information and timely feedback and use it in the real-time detection of welding qu...

AI Technical Summary

Problems solved by technology

Based on machine vision, on the one hand, the cost is high, and a professional high-definition camera needs to be installed. On the other hand, there are many uncontrollable factors, such as light intensity, plate impurities, camera shooting angle, etc., and require a lot of training. network of

For the modeling of time series data, the most commonly used models are cyclic neural networks such as LSTM and GRU, but the cyclic neural network cannot be calculated in parallel, and the training speed is slow, especially for high-frequency welding time series data, which has a long sequence length and data It is particularly important to be able to perform parallel computing due to the large amount of

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