一种复杂噪声环境下的机电系统故障在线监测声学处理方法

doi:10.16180/j.cnki.issn1007-7820.2023.03.009

摘要/Abstract

摘要：

针对复杂背景噪声环境下的机电系统故障检测问题,文中提出了一种基于宽带声学处理的噪声抑制和故障监测方法。该方法以声学信号拾取和处理为出发点,通过对机电设备正常运行状态下声学信号进行采集、数据跟踪和复杂背景噪声抑制,建立系统正常运行状态声纹库,并进一步通过基于宽带声学处理的声纹信号匹配和模式识别技术来实现故障信号的检测与分类,进而实现对机电系统运行状态的在线监测和隐形故障的自主预警。该处理方法将基于数据跟踪的自相关噪声抑制技术与基于宽带声学处理的故障信号检测以及分类判型技术有机结合,可对机电系统早期隐性故障进行监测,有效解决了复杂噪声环境下的机电系统故障检测问题。仿真实验也证明了该处理方法的有效性和良好的实用性。

关键词: 复杂噪声环境, 宽带声学处理, 噪声抑制, 故障监测, 声纹信号匹配, 模式识别, 隐性故障, 分类判型

Abstract:

In view of the problem of electromechanical system fault detection under complex background noise environment, this study proposes a noise suppression and fault monitoring method based on broadband acoustic processing. This method starts from acoustic signal pick-up and processing, and establishes the voiceprint database of the normal operating state of the system by collecting, tracking the data and suppressing the complex background noise of the acoustic signal under the normal operating state of the electromechanical equipment. In addition, the proposed method further realizes the detection and classification of fault signals through the voiceprint signal matching and pattern recognition technology based on broadband acoustic processing, and then realizes the online monitoring of the operating state of the electromechanical system and the autonomous early warning of invisible faults. This processing method organically combines the autocorrelation noise suppression technology based on data tracking and the fault signal detection and classification technology based on broadband acoustic processing, which can monitor the early hidden faults of the electromechanical system and effectively solve the fault detection problem of the electromechanical system in the complex noise environment. The simulation experiment finally proves the effectiveness and good practicability of the proposed method.

Key words: complex noise environment, broadband acoustic processing, noise suppression, fault monitoring, voiceprint signal matching, pattern recognition, hidden failure, classification

中图分类号:

TP277

白兴宇,苟宇涛,姜煜,刘明禹. 一种复杂噪声环境下的机电系统故障在线监测声学处理方法[J]. 电子科技, 2023, 36(3): 55-61.

BAI Xingyu,GOU Yutao,JIANG Yu,LIU Mingyu. An Acoustic Treatment Method for On-Line Fault Monitoring of Electromechanical Systems in Complex Noise Environment[J]. Electronic Science and Technology, 2023, 36(3): 55-61.

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参考文献 18

[1]	车立志, 徐文尚, 刘倩婧. 旋转机械系统故障诊断发展综述[J]. 矿山机械, 2008, 36(12):6-9.
	Che Lizhi, Xu Wenshang, Liu Qianjing. Summarized development state and trends on the fault diagnosis of rotary machinery system[J]. Mining and Processing Equipmenr, 2008, 36(12):6-9.
[2]	侯者非. 强噪声背景下滚动轴承故障诊断的关键技术研究[D]. 武汉: 武汉理工大学, 2010.
	Hou Zhefei. Research on the key techniques for fault diagnosis of rolling element bearing in heavy noise[D]. Wuhan: Wuhan University of Technology, 2010.
[3]	张琛, 郭俊超, 甄冬. 基于小波包时延相关解调的滚动轴承故障诊断方法[J]. 机械设计, 2020, 37(6):24-28. doi: 10.13841/j.cnki.jxsj.2020.06.004
	Zhang Chen, Guo Junchao, Zhen Dong. Fault diagnosis of rolling element bearings based on the wavelet packet transform and the time-delay correlation demodulation analysis[J]. Journal of Mechine Design, 2020, 37(6):24-28. doi: 10.13841/j.cnki.jxsj.2020.06.004
[4]	赵晋泉, 邓晖, 吴小辰, 等. 基于广域响应的电力系统暂态稳定控制技术评述[J]. 电力系统保护与控制, 2016, 44(5):1-9.
	Zhao Jinquan, Deng Hui, Wu Xiaochen, et al. Review on power system transient stability control technologies based on PMU/WAMS[J]. Power System Protectionand Control, 2016, 44(5):1-9.
[5]	邵梦麟, 员钦升, 王丽, 等. 基于振动信号盲源分离的船用空压机故障诊断[J]. 机电设备, 2018, 35(6):6-12.
	Shao Menglin, Yuan Qinsheng, Wang Li, et al. Fault diagnosis of marine air compressor based on blind source separation of vibration signals[J]. Mechanical and Electrical Equipment, 2018, 35(6):6-12.
[6]	Ye Z M, Wu B, Sadeghian A. Current signature analysis of induction motor mechanical faults by wavelet packet decomposition[J]. IEEE Transactions on Industrial Electronics, 2013, 50(6):1217-1228. doi: 10.1109/TIE.2003.819682
[7]	孙小明. 基于LabVIEW和Matlab混合编程的滚动轴承故障诊断系统[J]. 电子科技, 2018, 31(7):11-14.
	Sun Xiaoming. Fault diagnosis system of rolling bearing based on LabVIEW and Matlab mixed programming[J]. Electronic Science and Technology, 2018, 31(7):11-14.
[8]	Li R, He D. Rotational machine health monitoring and fault detection using EMD-Based acoustic emission feature quantification[J]. IEEE Transactions on Instrumentation and Measurement, 2012, 61(4):990-1001. doi: 10.1109/TIM.2011.2179819
[9]	李明晓. 基于改进的EMD方法在滚动轴承故障诊断中的应用[D]. 昆明: 昆明理工大学, 2018.
	Li Mingxiao. Application of improved EMD method in rolling bearing fault diagnosis[D]. Kunming: Kunming University of Science and Technology, 2018.
[10]	赵志宏, 杨绍普, 申永军. 基于独立分量分析与相关系数的机械故障特征提取[J]. 振动与冲击, 2013, 32(6):67-72.
	Zhao Zhihong, Yang Shaopu, Shen Yongjun. Mechanery fault feature extraction based on independent component analysis and correlation coefficient[J]. Journal of Vibration and Shock, 2013, 32(6):67-72.
[11]	张超, 陈建军, 郭迅. 基于EMD能量熵和支持向量机的齿轮故障诊断方法[J]. 振动与冲击, 2010, 29(10):216-220.
	Zhang Chao, Chen Jianjun, Guo Xun. A gear fault diagnosis method based on EMD energy entropy and SVM[J]. Journal of Vibration and Shock, 2010, 29(10):216-220.
[12]	高伟, 胡定玉, 方宇. 采用小波变换和盲源分离的电机轴承故障诊断[J]. 测控技术, 2017, 36(5):51-54.
	Gao Wei, Hu Dingyu, Fang Yu. A fault diagnosis method for motor bearing based on wavelet transform and blind source separation[J]. Measurement and Control Technology, 2017, 36(5):51-54.
[13]	李鹏, 陈永当, 刘广梅, 等. 小波分析在机械故障诊断中的应用[J]. 机电一体化, 2013(10):85-89.
	Li Peng, Chen Yongdang, Liu Guangmei, et al. Application of wavelet analysis in fault diagnosis of machinery[J]. Mechatronics, 2013(10):85-89.
[14]	Fan W, Zhou Q, Li J, et al. A wavelet-based statistical approach for monitoring and diagnosis of compound faults with application to rolling bearings[J]. IEEE Transactions on Automation Science and Engineering, 2018, 15(4):1563-1572. doi: 10.1109/TASE.2017.2720177
[15]	郑洋. 小波变换在旋转机械故障检测中的应用研究[J]. 机械研究与应用, 2008, 21(6):111-112.
	Zheng Yang. Application research of wavelet transform in the fault detection of rotating machinery[J]. Machinery Research and Application, 2008, 21(6):111-112.
[16]	赵阳, 陈捷, 洪荣晶, 等. EMD和独立分量分析在转盘轴承故障诊断中的应用[J]. 轴承, 2015(7):54-59.
	Zhao Yang, Chen Jie, Hong Rongjing, et al. Application of EMD and ICA in fault diagnosis for slewing bearing[J]. Bearings, 2015(7):54-59.
[17]	朱永年, 赵君爱. 小波—BP神经网络在旋转机械故障诊断中的应用[J]. 电子机械工程, 2011, 27(1):56-59.
	Zhu Yongnian, Zhao Junai. Application of the wavelet-BP neural network to rotating machinery fault diagnosis[J]. Electronic and mechanical Engineering, 2011, 27(1):56-59.
[18]	何盼盼. 基于组合阵的近场噪声源定位方法研究[J]. 电子科技, 2016, 29(11):32-34.
	He Panpan. Research on near-field noise sourse locationmethod based on the combinatory[J]. Electronic Science and Technology, 2016, 29(11):32-34.