基于机床状态信息监测的智能维护理论与方法

As a carrier of advanced manufacturing technology, CNC equipment has become an important basis for the development of domestic manufacturing, and the requirements of machining accuracy and reliability of digital manufacturing system is also increasing, and the research of intelligent maintenance theory and method provides effective protection for safe and reliable operation based on the machine condition information monitoring. For the key components of the machine tool, the project researches on feature vector data fusion based on the degree of relevance and intelligent identification theory, achieving fault intelligent prediction and diagnosis; researches on adaptive control theroy baded on the power bond graph and fuzzy logic, proposes a new method to optimize control rules,maintaining process stability; Researches on comprehensive error compensation theory based on the homogeneous coordinate transformation and fuzzy neural network, improving and maintaining machining precision; Researching on RBF network technology and reliability maintenance theory, proposes the strategy of multi-agent coordination maintenance; And then buildes the intelligent maintenance theory and methods system based on machine status information monitoring. The above of theory and method system will be applied on the self-developed six-cooperatively working digital manufacturing platform to realize the validation ,and provide new ideas and methods for the intelligence manufacture, greatly promote the sustained and rapid development of China's equipment manufacturing industry.

数控装备作为先进制造技术的载体，已成为国内制造业发展的重要基础，同时对于数字化制造系统的加工精度及安全可靠性的要求也日益提高，研究基于机床状态信息监测的智能维护理论与方法体系为其安全可靠运行提供有力的保障。项目针对机床关键部件，研究基于程度相关性的特征向量数据融合及基于超球面支持向量机的故障辨识理论，实现故障智能预测与诊断；研究基于功率键合图和模糊逻辑的自适应控制理论，提出控制规则优化新方法，维护加工稳定性；研究基于齐次坐标变换法及模糊神经网络的综合误差补偿理论，提高和维护加工精度；研究基于径向基网络技术及可靠性维护理论，提出多智能体协同维护策略；进而构建基于机床状态信息监测的智能维护理论与方法体系，并在自主研发的六轴联动数字化制造平台上取得实际验证，为机床的智能化制造提供新的思路和方法，极大地推动我国装备制造业的持续快速发展。

数控装备作为先进制造技术的载体，已成为制造业发展的重要基础。随着对于数字化制造系统的加工精度及安全可靠性的要求日益提高，研究基于机床状态信息监测的智能维护理论与方法体系为其安全可靠运行提供有力的保障。本项目针对机床关键部件，开展了面向多传感器信息源的早期微弱故障诊断理论方法研究，提出了基于振动冲击信号自适应SR检测的早期故障诊断方法和基于Fourier深度卷积模型的多振动信号组合式融合方法，实现了设备早期微弱故障辨识。进行了基于机床状态监测的加工过程自适应控制理论与方法研究，建立了数控加工切削力模糊自适应控制系统，实现了基于动态特性的切削参数模糊自适应在线优化。进行了基于机床关键部件信息的多误差综合补偿技术研究，实现了五轴空间刀具误差动态补偿、基于在机检测的工件模型调整误差控制，设计了五轴高精高速完整轨迹规划策略。进行了基于网络的以可靠性为中心的多智能体协同维护理论研究， 在基于振动冲击信号自适应 SR 检测的早期故障诊断研究、基于振动信号混沌特性分析的切削颤振在线监测预报研究、基于振动信号多特征分析和融合的刀具磨损在线监测研究等基础研究内容的基础上，设计并实现了数控机床在线振动监测系统。项目揭示了机床状态信息与智能维护技术间的内在联系，阐明了多智能体协同维护机理，形成了智能维护理论与方法体系，为制造装备智能化、高端化发展提供了理论支撑。