Monitoring System Design For Predictive Maintenance Of Welding Robot Workstations

With the development of manufacturing and the continuous improvement of robot technology,welding robots have become an indispensable part of modern industrial production.However,welding robots will inevitably have various failures during long-term use,which will have a serious impact on production.Therefore,predictive maintenance of welding robots,predicting faults in advance and dealing with them can effectively improve production efficiency and reduce production costs.This paper proposes a predictive maintenance monitoring system design for welding robot workstations,mainly relying on the Io T cloud platform,data acquisition of welding robots,signal feature extraction using wavelet packet transform,fault classification using LSTM neural network,and a visual monitoring interface using Huichen cloud platform.In terms of data collection,this paper uses Io T technology to upload the operating status data of welding robots to the cloud platform,including important parameters such as current,vibration,and temperature,and monitors and processes the data in real time.In terms of signal feature extraction,wavelet packet transformation is used to preprocess the original data,and the frequency domain and time domain features of multi-scale signals are extracted.In terms of fault classification,the LSTM neural network is used for modeling,and the extracted signal features are input into the neural network for training to realize the fault classification of welding robots.In terms of monitoring interface design,Huichen Cloud Platform is used to design an intuitive,concise and easy-to-operate visual monitoring interface to facilitate engineers to conduct real-time monitoring and early warning processing.The experimental results show that the use of wavelet packet transformation for signal feature extraction can effectively reduce the noise interference of data and improve the recognition of signals,while the fault classification using LSTM neural network can classify and diagnose the faults of welding robots more accurately.Finally,this paper realizes a set of predictive maintenance monitoring system and achieves good results,which provides strong technical support for the intelligent production of welding robots.