In intelligentmanufacturing processes such as aerospace production,computer numerical control(CNC)machine tools require real-time optimization of process parameters to meet precision machining demands.These dynamic op...In intelligentmanufacturing processes such as aerospace production,computer numerical control(CNC)machine tools require real-time optimization of process parameters to meet precision machining demands.These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings,highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime.However,varying conditions induce feature distribution shifts,and scarce fault samples limitmodel generalization.Therefore,this paper proposes a causal-Transformer-based meta-learning(CTML)method for bearing fault diagnosis in CNC machine tools,comprising three core modules:(1)the original bearing signal is transformed into a multi-scale time-frequency feature space using continuous wavelet transform;(2)a causal-Transformer architecture is designed to achieve feature extraction and fault classification based on the physical causal law of fault propagation;(3)the above mechanisms are integrated into a model-agnostic meta-learning(MAML)framework to achieve rapid cross-condition adaptation through an adaptive gradient pruning strategy.Experimental results using the multiple bearing dataset show that under few-shot cross-condition scenarios(3-way 1-shot and 3-way 5-shot),the proposed CTML outperforms benchmark models(e.g.,Transformer,domain adversarial neural networks(DANN),and MAML)in terms of classification accuracy and sensitivity to operating conditions,while maintaining a moderate level of model complexity.展开更多
运动过程的升降速控制是CNC(Com puter num ericalcontrol)系统开发中的关键技术难题之一。文中在分别分析了数控系统中梯形、S型和直线加抛物型升降速曲线的基础上,对这几种控制方法各自的优缺点及适用场合进行了比较,并着重讨论了速...运动过程的升降速控制是CNC(Com puter num ericalcontrol)系统开发中的关键技术难题之一。文中在分别分析了数控系统中梯形、S型和直线加抛物型升降速曲线的基础上,对这几种控制方法各自的优缺点及适用场合进行了比较,并着重讨论了速度时间曲线是直线加抛物型的升降速曲线在由步进电机驱动的经济型数控系统中的应用,实验证明。展开更多
基金the National Key Research and Development Program of China(Grant No.2022YFB3302700)the National Natural Science Foundation of China(Grant No.52375486)the Shanghai Rising-Star Program(Grant No.22QB1404200).
文摘In intelligentmanufacturing processes such as aerospace production,computer numerical control(CNC)machine tools require real-time optimization of process parameters to meet precision machining demands.These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings,highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime.However,varying conditions induce feature distribution shifts,and scarce fault samples limitmodel generalization.Therefore,this paper proposes a causal-Transformer-based meta-learning(CTML)method for bearing fault diagnosis in CNC machine tools,comprising three core modules:(1)the original bearing signal is transformed into a multi-scale time-frequency feature space using continuous wavelet transform;(2)a causal-Transformer architecture is designed to achieve feature extraction and fault classification based on the physical causal law of fault propagation;(3)the above mechanisms are integrated into a model-agnostic meta-learning(MAML)framework to achieve rapid cross-condition adaptation through an adaptive gradient pruning strategy.Experimental results using the multiple bearing dataset show that under few-shot cross-condition scenarios(3-way 1-shot and 3-way 5-shot),the proposed CTML outperforms benchmark models(e.g.,Transformer,domain adversarial neural networks(DANN),and MAML)in terms of classification accuracy and sensitivity to operating conditions,while maintaining a moderate level of model complexity.
文摘运动过程的升降速控制是CNC(Com puter num ericalcontrol)系统开发中的关键技术难题之一。文中在分别分析了数控系统中梯形、S型和直线加抛物型升降速曲线的基础上,对这几种控制方法各自的优缺点及适用场合进行了比较,并着重讨论了速度时间曲线是直线加抛物型的升降速曲线在由步进电机驱动的经济型数控系统中的应用,实验证明。
