基于振动声学的除焦状态检测系统  

Vibrational Acoustics Based Decoking State Detection System

作　　者：王智航 王利恒[1] WANG Zhi-hang;WANG Li-heng(School of Electrical Information,Wuhan Institute of Technology,Wuhan 430000,China)

机构地区：[1]武汉工程大学电气信息学院,湖北武汉430000

出　　处：《机械工程与自动化》2019年第3期10-11,共2页Mechanical Engineering & Automation

基　　金：国家自然科学基金青年基金项目(61703312)

摘　　要：针对传统水力除焦操作中需要人为观测判断、劳动强度过大、环境恶劣的缺陷,提出了基于振动声学的智能除焦状态检测系统,利用智能检测技术完成除焦状态的判读。通过振动传感器对信号进行采集,再完成特性参数的提取,使用模式识别的方式建立除焦状态和振型参数之间的关系,采用BP神经网络将获取的振动信号样本进行傅里叶变换后得到振动信号的幅频曲线。提取不同特征频段的幅值作为特征参数,进行样本学习训练,建立起除焦状态与焦炭塔振动特性之间的学习识别网络,经过训练并且稳定的网络即可用于水力除焦智能检测系统中。Aiming at the defects of traditional hydraulic decoking operation,such as human observation and judgment,excessive labor intensity and harsh environment,a vibrational acoustics-based intelligent decoking state detection system is proposed,which uses intelligent detection technology to complete the decoking state interpretation.The signal is collected by the vibration sensor,and the characteristic parameters are extracted.The relationship between the decoking state and the vibration mode parameter is established by pattern recognition.The amplitude-frequency curve of the vibration signal is obtained by applying BP neural network to Fourier transform of vibration signal sample.The amplitude of different characteristic frequency bands is extracted as the characteristic parameters ,and the sample learning training is carried out to establish a learning recognition network between the decoking state and the vibration characteristics of the coke tower.The trained and stable network can be used in the hydraulic decoking intelligent detection system.

关 键 词：水力除焦 智能检测 频谱分析 神经网络 振动声学 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]