典型快速谱峭图算法的研究及应用

马新娜1 杨绍普2

振动与冲击 ›› 2016, Vol. 35 ›› Issue (15) : 109-114.

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PDF(1879 KB)
振动与冲击 ›› 2016, Vol. 35 ›› Issue (15) : 109-114.
论文

典型快速谱峭图算法的研究及应用

  • 马新娜1     杨绍普2
作者信息 +

Study and application of typical fast kurtogram

  • Ma Xinna 1  Yang Shaopu 2 
Author information +
文章历史 +

摘要

针对工程实际中状态监测下的具体机械部件在特定运行环境中发生故障的原因及类型具有典型性,在分析快速谱峭图算法的基础上,提出了典型快速谱峭图算法。该算法借鉴快速谱峭图算法分层和选取中心频率的思想,以典型故障的三倍特征频率为带宽,快速定位谱峭度最大的频率区间,并将该算法应用于共振解调技术滤波器参数的确定。为了验证该算法的有效性,在滚动轴承故障诊断试验台上进行了振动测试,采用基于典型快速谱峭图算法的共振解调技术进行故障诊断和类型识别。结果表明,基于典型快速谱峭图算法的共振解调技术能够较好的诊断典型故障,具有一定的工程应用价值。

Abstract

In practical engineering, the causes and types of faults occurred on the specific mechanical components are typical. On the basis of analyzing fast kurtogram, the typical fast kurtogram is presented in the paper. With the fast kurtogram idea of hierarchy and selection of center frequency, the typical fast kurtogram takes three times harmonic of the characteristic frequency as bandwidth to quickly find the frequency range with the maximum spectral kurtosis. This algorithm is used to automatically select filter’s parameters of demodulated resonance. In order to verify the validity of the typical fast kurtogram, experiments are carried out on rolling bearing with the experiment platform of freight car. Demodulated resonance based on typical fast kurtogram is used to fault diagnosis and recognition. The results demonstrate the proposed algorithm is reliable and can meet the engineering application.

关键词

典型快速谱峭图算法 / 故障诊断 / 滚动轴承;共振解调

Key words

Typical fast kurtogram / fault diagnosis / rolling bearing / demodulated resonance

引用本文

导出引用
马新娜1 杨绍普2. 典型快速谱峭图算法的研究及应用[J]. 振动与冲击, 2016, 35(15): 109-114
Ma Xinna 1 Yang Shaopu 2 . Study and application of typical fast kurtogram[J]. Journal of Vibration and Shock, 2016, 35(15): 109-114

参考文献

[1] 赵志宏, 杨绍普, 李韶华. 基于Hilbert谱奇异值的轴承故障诊断[J]. 中国机械工程,2013,24(3): 346-350.
Zhao Zhihong, Yang Shaopu, Li Shaohua. Bear- ing fault diagnosis based on Hilbert spectrum and singular value decomposition[J]. China mechani- cal engineering, 2013, 24(3): 346-350. ( in Chinese)
[2] 唐贵基,王晓龙. 基于包络谱稀疏度和最大相关峭度解卷积的滚动轴承早期故障诊断方法[J]. 中国机械工程,2015,26(11):1450-1456.
TangGuiji, WangXiaolong. Diagnosis method for rolling bearing incipient faults based on sparsity of envelope spectrum and maximum correlated kurtosis deconvolution[J]. China mechanical engineering, 2015,26(11):1450-1456. ( in Chinese)
[3] 陈彬强,张周锁,訾艳阳,何正嘉. 机械故障诊断的衍生增强离散解析小波分析框架[J]. 机械工程学报,2014,50(17):77-86.
Chen Bingqiang, Zhang Zhousuo, Zi Yangyang, He Zhengjia. Novel ensemble analytic discrete framelet expansion for machinery fault diagnosis [J]. Journal of mechanical engineering, 2014, 50(17):77-86. ( in Chinese)
[4] 明安波,褚福磊,张炜. 滚动轴承故障特征提取的频谱自相关方法[J]. 机械工程学报,2012, 48(19):65-71.
Ming Anbo, Chu Fulei, Zhang Wei. Feature extracting method in the rolling element bearing fault diagnosis spectrum auto-correlation[J]. Journal of Mechanical Engineering, 2012, 48(19): 65-71, ( in Chinese)
[5] Dwyer R F. Detection of non-gaussian signals by frequency domain kurtosis estimation[C]. Acoustic, speech and signal processing. Boston: IEEE inter-national conference on ICASSP, 1983:607-610.
[6] Randall R B, Antoni J. Rolling element bearing diagnostics-a tutorial[J]. Mechanical System and Signal Processing, 2011, 25:485-520.
[7] AntoniN J. The spectral kurtosis: A useful tool for characterizing non-stationary signals[J]. Mechanical System and Signal Processing, 2006, 20(6):282-307.
[8] Antoni J. Randall R B. The spectral kurtosis: Application to the vibration surveillance and diagnostics of rotating machines[J]. Mechanical system and Signal Processing, 2006, 20(6): 308-331.
[9] Antoni J. Fast computation of the kurtogram for the detection of transient faults[J]. Mechanical Systems and Signal Processing, 2007, 21(1):108-124.
[10] 彭畅,柏林,谢小亮. 基于EEMD、度量因子和快速峭度图的滚动轴承故障诊断方法[J]. 振动与冲击,2012,31(20):143-146.
Peng Chang, Bo Lin, Xie Xiaoliang. Fault diagnosis method of rolling element bearings based on EEMD, measure-factor and fast kurtogram[J]. Journal of Vibration and Shock, 2012,31(20):143-146 . ( in Chinese)
[11] 林京. 连续小波变换及其在滚动轴承故障诊断中的应用[J]. 西安交通大学学报,1999,33(11):108-110.
Lin Jing. Continuous wavelet transform and its application for bearing diagnosis[J]. Journal of xi’an jiaotong university, 1999, 3(11):108-110. ( in Chinese)
[12] Wang Yanxue, Liang Ming. An adaptive SK technique and its application for fault detection of rolling element bearings[J]. Mechanical Systems and Signal Processing,2011,  25(1): 1750-1764.
[13] 代士超,郭瑜,伍星,那靖. 基于子频带谱峭度平均的快速谱峭度图算法改进[J]. 振动与冲击,2015, 34(7):98-102.
Dai Shichao, Guo Yu, Wu Xing, Na Jing. Improvement on fast kurtogram algorithm based on sub-frequency-band spectral kurtosis average[J]. Journal of Vibration and Shock, 2015, 34(7):98-102. ( in Chinese)
[14] BARSZCZ T,JABIONSKI A. A novel method for the optimal band selection for vibration signal demodulation and comparison with the kurtogram[J]. Mechanical Systems and Signal Processing,2011,25(1):431-451.
[15] 蔡艳平,李艾华,石林锁,白向峰,沈金伟. 基于EMD与谱峭度的滚动轴承故障检测改进包络谱分析[J]. 振动与冲击,2011,30(2):167 -191.
Cai Yan-ping, Li Ai-hua, Shi Lin-suo, Bai Xiang-feng, Shen Jin-wei. Roller bearing fault detection using improved envelope spectrum analysis based on EMD and spectrum kurtosis[J]. Journal of Vibration and Shock, 2011,30(2):167-191, ( in Chinese)
[16] 张晓涛,唐力伟,王平,邓士杰. 基于SVD与Fast kurtogram算法的滚动轴承声发射故障诊断[J]. 振动与冲击,2014,33(10):101-105.
Zhang Xiaotao, Tang Liwei, Wang Ping, Deng Shijie. Acoustic emission fault diagnosis of rolling bearing based SVD and Fast kurtogram algorithm[J]. Journal of Vibration and Shock, 2014, 33(10): 101-105. ( in Chinese)
[17] 王宏超,陈进,董广明,从飞云. 基于快速kurtogram算法的共振解调方法在滚动轴承故障特征提取中的应用[J]. 振动与冲击,2013, 32(1):35-37.
Wang Hongchao, Chen Jin, Dong Guangming, Cong Yunfei. Application of resonance demodulation in rolling bearing fault feature extraction based on fast computation of kurtogram[J]. Journal of Vibration and Shock, 2013, 32(1): 35-37. ( in Chinese)
[18] 丁康,黄志东,朴慧斌. 一种谱峭度和Morlet小波的滚动轴承微弱故障诊断方法[J]. 振动工程学报,2014,27(1): 128-135.
Ding Kang, Huang Zhidong, Lin Huibin. A weak fault diagnosis method for rolling element bearings based on Morlet wavelet and spectral kurtosis[J]. Journal of vibration engineering, 2014, 27(1): 128-135. ( in Chinese)
[19] 张晓涛,唐力伟,王平,邓士杰. 轴承故障声发射信号多频带共振解调方法[J]. 振动、测试与诊断,2015,35(2):363-368.
Zhang Xiaotao, Tang Liwei, Wang Ping, Deng Shijie. Multi-band resonance demodulation of rolling bearing fault acoustic emission signal[J]. Journal of vibration, measurement & diagnosis, 2015,35(2): 363-368. ( in Chinese)
[20] 张龙,熊国良,黄文艺. 复小波共振解调频带优化方法和新指标[J],机械工程学报,2015,51(3):129-138.
Zhang Long, Xiong Guoliang, Huang Wenyi. New procedure and index for the parameter optimization of complex wavelet based resonance demodulation [J]. Journal of mechanical engineering, 2015, 51(3): 129-138. ( in Chinese)

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