摘要
汽轮机是电力产业重要的能源转换设备,结合信息化技术的汽轮机故障检测方法是实现智慧运维的重要手段。针对传统故障检测方法精度低、适应性差、严重依赖人工经验的缺点,提出一种基于深度卷积神经网络的汽轮机转子故障检测方法,实现对汽轮机转子故障端到端的检测;同时建立故障转子数值模型对方法进行评估,实现转子不平衡、平行不对中和角度不对中3种单一简单故障的分类以及3种故障的位置和程度的多任务协同检测,讨论信噪比和通道数对神经网络检测性能的影响。故障类型的检测精度为100%,位置和程度的平均检测精度不低于96.47%。文中提出的方法可以实现多测点振动信号到故障特征的直接映射,摆脱了传统方法对人工经验和信号处理技巧的依赖,具有准确、鲁棒性高的特点。
Steam turbine is an important energy conversion equipment in the power industry.The steam turbine fault detection method combined with information technology is an important means to realize intelligent operation and maintenance.Aiming at the shortcomings of traditional fault detection methods,such as low accuracy,poor adaptability,and heavy reliance on manual experience,this paper presented a method of steam turbine rotor fault detection based on deep convolutional neural network to achieve end-to-end detection,which was evaluated by using fault rotor numerical model,including three simple fault classification of rotor unbalance,parallel misalignment and angular misalignment,as well as the detection of the location and degree of the three faults.And the method implements multi-task collaborative detection.The effect of snr and channel numbers on the detection performance of neural networks was discussed.Fault detection accuracy is 100%,the average detection accuracy of position and degree is not less than 96.47%.The method proposed in this paper can realize the direct mapping of vibration signals from multiple measurement points to fault features.It is free of the traditional methods dependence on artificial experience and signal processing skills,and has the characteristics of high accuracy and robustness.
作者
王崇宇
郑召利
刘天源
谢永慧
张荻
WANG Chongyu;ZHEN Zhaoli;LIU Tianyuan;XIE Yonghui;ZHANG Di(School of Energy and Power Engineering,Xi'an Jiaotong University,Xi'an 710049,Shannxi Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2021年第7期2417-2426,共10页
Proceedings of the CSEE
关键词
智慧汽轮机
故障转子
故障检测
卷积神经网络
深度学习
intelligent steam turbine
fault rotor
fault detection
convolutional neural network
deep learning
