Predicting the lifespan of robotic arm components based on sensor data fusion

The life prediction of robotic arm components is an important basis for equipment maintenance and production plan optimization. The current existing prediction models have a single data source, making it difficult to achieve reliable and comprehensive prediction of their operating status. In order to improve prediction accuracy and ensure the smooth progress of production work, this article combines sensor data fusion to study the life prediction of robotic arm components. In this article, the arm component is taken as the object, and based on sensor data collection, multi-source data is fused. A prediction model is constructed using support vector machine (SVM). To verify the effectiveness of the model, this article conducts experimental analysis from two aspects: feature recognition and prediction accuracy. In accuracy analysis, compared to artificial neural network (ANN) and convolutional neural network (CNN), the average prediction accuracy of our method has been improved by 11.5% and 12.2%, respectively. The conclusion indicates that the life prediction of robotic arm components based on sensor data fusion has good accuracy results and can provide objective reference for production technology.