Angular domain intensity feature extraction method for fault vibration signals of hydraulic pump under variable rotating speed condition

PDF(1127 KB)

Journal of Vibration and Shock ›› 2020, Vol. 39 ›› Issue (17) : 142-149.

LIU Siyuan1,2,3, WANG Zhiwei1,2, JIANG Wanlu1,2, LI Xiaoming4, LU Mingli3, LU Zhengdian3

Author information +

History +

Abstract

Extracting feature information sensitive to faults is the key to improve the correctness of hydraulic pump condition assessment, due to severe lack of sensitive feature information of hydraulic pump faults under variable rotating speed condition, the assessment correctness rate is much low. Here, a new concept of angular domain intensity feature and a new feature extraction method for hydraulic pump slipper wear faults were proposed. Non-stationary vibration signals in time domain were converted into stationary ones in angular domain with the order analysis method. According to the definition of vibration intensity and the calculation method in frequency domain, unilateral amplitude value spectra and harmonic frequencies of stationary vibration signals in angular domain were obtained, and then calculation formulas of angular domain intensity feature factors for three vibration signals including displacement, velocity and acceleration were derived. Taking inner edge wear fault of hydraulic pump slipper as an example, the angular domain intensity feature factor of fault vibration signals was extracted under variable rotating speed condition. Qualitative and quantitative contrastive analyses were performed for this feature factor and the order energy one. It was shown that the angular domain intensity feature factor is more sensitive to deterioration development of hydraulic pump slipper wear fault.

Key words

angular intensity feature / variable rotating speed / hydraulic pump / vibration signal / feature extraction

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

LIU Siyuan1,2,3, WANG Zhiwei1,2, JIANG Wanlu1,2, LI Xiaoming4, LU Mingli3, LU Zhengdian3. Angular domain intensity feature extraction method for fault vibration signals of hydraulic pump under variable rotating speed condition[J]. Journal of Vibration and Shock, 2020, 39(17): 142-149

References

[1]孙健,李洪儒.一种基于复合谱与关联熵融合的特征提取方法[J]. 机械工程学报, 2017, 53(24):96-103.
SUN Jian, LI Hongru. Method on Feature Extraction Basd on Composite Spectrum and Relative Entropy Fusion[J]. Journal of Mechanical Engineering, 2017, 53(24):96-103.
[2] 姜万录，刘云杰，朱勇.小波脊线解调与两次EMD分解相结合的故障识别方法及应用研究[J]. 仪器仪表学报, 2013, 34(5):1131-1138.
JIANG Wanlu, LIU Yunjie, ZHU Yong. Resarch on Wavelet Demodulation and Twice EMD Based Fault Identification Method and Its Application[J]. Chinese Journal of Scientific Instrument,2013, 34(5):1131-1138.
[3] 刘玉娇, 姚恩涛, 徐红专. 基于粒子滤波和自回归谱的液压泵故障诊断[J]. 仪器仪表学报, 2012, 33(3):561-567.
LIU Yujiao, YAO Entao, XU Hongzhuan. Fault Diagnosis of Hydraulic Hump Based on Particle Filtering and Autoregressive Spectrum[J]. Chinese Journal of Scientific Instrument, 2012, 33(3):561-567.
[4] Tian Y, Lu C, Wang Z, et al. Fault Diagnosis Based on LMD-SVD and Information-geometric Support Vector Machine for Hydraulic Pumps[J]. Transactions of the Canadian Society for Mechanical Engineering, 2015, 39(3):569-580.
[5] Wang C, Wang Z, Ma J, et al. Fault Diagnosis for Hydraulic Pump Based on EEMD-KPCA and LVQ[J]. Vibroengineering Procedia, 2014, 4:188-193.
[6] 王天杨，李建勇，程卫东．基于瞬时故障特征频率趋势线和故障特征阶比模板的变转速滚动轴承故障诊断[J]. 振动工程学报, 2015, 28(6):1006-1014.
Wang Tianyang, Li Jianyong, Cheng Weidong. The Instantaneous Fault Characteristic Frequency and Fault Characteristic Order Template Based Fault Diagnosis Algorithm for Rolling Bearing under Time-varying Rotational Speed[J]. Journal of Vibration Engineering, 2015, 28(6):1006-1014.
[7] Yang Y, Wang H, Cheng J, et al. A Fault Diagnosis Approach for Roller Bearing Based on VPMCD under Variable Speed Condition[J]. Measurement, 2013, 46(8):2306-2312.
[8] Abboud D, Antoni J, Sieg-Zieba S, et al. Envelope Analysis of Rotating Machine Vibrations in Variable Speed Conditions: A comprehensive treatment[J]. Mechanical Systems & Signal Processing, 2017, 84:200-226.
[9] Tian Y, Ma J, Lu C, et al. Rolling Bearing Fault Diagnosis under Variable Conditions Using LMD-SVD and Extreme Learning Machine[J]. Mechanism & Machine Theory, 2015, 90:175-186.
[10] 赵明,林京.变转速下机械动态信息的自适应提取与状态评估[J]. 机械工程学报, 2015(8):89-89.
ZHAO Ming, LIN Jing. Adaptive Dynamic Information Extraction and State Evaluation for Machinery under Varying Speeds[J]. Journal of Mechanical Engineering, 2015(8):89-89.
[11] 刘坚, 彭富强, 于德介. 基于线调频小波路径追踪阶比跟踪算法的齿轮箱故障诊断研究[J].机械工程学报, 2009 , 45 (7) :81-86.
LIU Jian, PENG Fuqiang, YU Dejie. A New Order Tracking Method Based on Chirplet Path Pursuit and Its Application to Gearbox Fault Diagnosis[J]. Journal of mechinal Engineering, 2009, 45 (7) :81-86.
[12] Zhao M, Lin J, Xu X, et al. Tacholess Envelope Order Analysis and Its Application to Fault Detection of Rolling Element Bearings with Varying Speeds[J]. Sensors, 2013, 13(8):10856-10875.
[13] Wang T, Ming L, Li J, et al. Rolling Element Bearing Fault Diagnosis Via Fault Characteristic Order (FCO) Analysis[J]. Mechanical Systems & Signal Processing, 2014, 45(1):139-153.
[14] 王况, 王科盛, 左明健. 基于阶次分析技术的行星齿轮箱非平稳振动信号分析[J]. 振动与冲击, 2016, 35(5):140-145.
WANG Kuang，WANG Keshang，ZUO Mingjian. Fault Diagnosis of a Planetary Gearbox Based on Order Tracking[J]. Journal of Vibration and Shock, 2016, 35(5):140-145.
[15] 樊新海, 李胜利, 安钢, 等. 装甲车辆传动装置振动烈度监测与评估[J]. 兵工学报,2009,30(3):272-275.
FAN Xinhai, LI Shengli, AN Gang, et al. Monitoring and Evaluation of Vibration Intensity of Armored Vehicle Transmission[J]. Journal of Ordnance, 2009,30 (3): 272-275.
[16] 樊新海, 赵智勇, 安钢, 等. 机械振动烈度的频域算法研究[J].装甲兵工程学院学报,2008,22(1):42-45.
FAN Xinhai, ZHAO Zhiyong, AN Gang, et al. Study on Frequency Domain Algorithm of Mechanical Vibration Intensity[J]. Journal of Armored Force Engineering Institute, 2008,22(1): 42-45.
[17] 马静云. 康复运动中表面肌电信号分析方法研究[D]. 燕山大学, 2015.
MA Jingyun. Research on Surface Electromyography Signal Analysis Method in Rehabilitation Exercise[D]. Yanshan
University, 2015.
[18] 李晓明.变转速液压泵多信息烈度特征状态评估方法研究[D].燕山大学,2018.
LI Xiaoming. Research on Multi-Information Intensity Characteristic Condition Evaluation of Variable Speed Hydraulic Pump[D]. YanShan University, 2018.
[19] 牛慧峰, 姜万录. 免疫支持向量机方法在液压泵故障诊断中的应用[J]. 中国机械工程, 2008, 19(14):0-1763.
NIU Huifeng, JIANG Wanlu. Application of Hybrid Approach Based on Immune Algorithm and Support Vector Machine for Fault Diagnosis of Hydraulic Pump[J]. China Mechanical Engineering, 2008, 19(14): 0-1763.
[20]李凌均, 张周锁, 何正嘉. 基于支持向量数据描述的机械故障诊断研究[J]. 西安交通大学学报, 2003, 37(9): 910-913.
LI Lingjun, ZHANG Zhousuo, HE Zhengjia. Research of Mechanical System Fault Diagnosis Based on Support Vector Data Description[J]. Journal of Xi'an Jiaotong University, 2003, 37(9): 910-913.