Affiliation:
1. School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161006, China
2. The Engineering Technology Research Center for Precision Manufacturing Equipment and Industrial Perception of Heilongjiang Province, Qiqihar 161006, China
Abstract
Aiming at the problem that predicted data do not reflect the operating status of computer numerical control (CNC) machine tools, this article proposes a new combined model based on SE-ResNet and Transformer for CNC machine tool failure prediction. Firstly, the Transformer model is utilised to build a non-linear temporal feature mapping using the attention mechanism in multidimensional data. Secondly, the predicted data are transformed into 2D features by the SE-ResNet model, which is adept at processing 2D data, and the spatial feature relationships between predicted data are captured, thus enhancing the state recognition capability. Through experiments, data involving the CNC machine tools in different states are collected to build a dataset, and the method is validated. The SE-ResNet-Transformer model can accurately predict the state of CNC machine tools with a recognition rate of 98.56%. Results prove the effectiveness of the proposed method in CNC machine tool failure prediction. The SE-ResNet-Transformer model is a promising approach for CNC machine tool failure prediction. The method shows great potential in improving the accuracy and efficiency of CNC machine tool failure prediction. Feasible methods are provided for precise control of the state of CNC machine tools.
Funder
The Basic Research Fund for State-owned Universities in Heilongjiang Province
The Collaborative Education Project for Industry–University Cooperation Supported by the Ministry of Education
The General Research Project on Higher-Education Teaching Reform in Heilongjiang Province
The Key Projects of Qiqihar City Scientific and Technological Plan
The Educational Science Research Project of Qiqihar University
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