Journal of Jilin University(Engineering and Technology Edition) ›› 2022, Vol. 52 ›› Issue (11): 2514-2522.doi: 10.13229/j.cnki.jdxbgxb20210355

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Fault diagnosis method of rotating machinery for unlabeled data

Fei CHEN1(),Zheng YANG2,Zhi-cheng ZHANG2,Wei LUO2()   

  1. 1.Sino-German College of Intelligent Manufacturing，Shenzhen Technology University，Shenzhen 518118，China
    2.School of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
  • Received:2021-04-22 Online:2022-11-01 Published:2022-11-16
  • Contact: Wei LUO E-mail:chenfei@sztu.edu.cn;luoweicn@jlu.edu.cn

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Abstract:

Most fault diagnosis algorithms for rotating machinery are labeled and need to be set manually， an unsupervised fault diagnosis algorithm with adaptive parameters for unlabeled fault data by introducing twice anomaly recognitions and clustering algorithms was proposed.The method extracts and selects signal features by improving empirical wavelet transform and Laplace score algorithm, and adopts the unsupervised method of quadratic anomaly identification combined with improved fuzzy C-means clustering for fault identification. Through the verification of the fault data of the rotor system of the electric spindle, the diagnostic accuracy of the proposed method can reach 93%. Compared with the traditional unsupervised diagnostic method, it has good accuracy and robustness.

Key words: fault diagnosis, rotating machinery, unlabeled data, adaptive parameters, unsupervised learning

CLC Number: 

  • TH133.33

Fig.1

Feature extraction flow chart based on SEWT"

Fig.2

Flow chart of KDBSCAN algorithm"

Fig.3

Flow chart of unsupervised fault diagnosis algorithm with adaptive parameters"

Fig.4

Condition monitoring system of motor spindle"

Table 1

Vibration sensor parameters"

参数数值
量程/g±50
灵敏度/（mV·g-1）100
采样频率/Hz0.5~9000
分辨率/g0.0002

Table 2

Sample configurations"

样本类型样本数量
正常样本，编号：0~4950
X方向连续冲击程度1，编号：56~627
X方向连续冲击程度2，编号：63~664
Y方向连续冲击程度1，编号：67~726
Y方向连续冲击程度2，编号：73~775
Z方向连续冲击程度1，编号：78~847
Z方向连续冲击程度2，编号：85~895
不对中，编号：50~556
转速突变，编号：90~923

Fig.5

Visualization of feature matrix identified"

Table 3

Comparison of LOF and DLLOF"

算法准确率α误识别率δ运行时间/s
LOF0.950.166.6
DLLOF0.940.022.1

Table 4

GLOF value of outlier samples identified by DLLOF algorithm"

编号LOFGLOF编号LOFGLOF
51.217.46702.116.79
420.97无712.547.94
502.7810.92721.978.38
513.1712.9732.925.58
522.9111.58742.311.41
532.7410.73752.6314.5
544.1114.64763.2515.02
552.7210.61772.613.39
563.057.12780.98无
572.048.12791.95.14
581.6511.05800.97无
591.637.75811.926.02
602.7713.96821.865.46
611.828.07832.186.32
623.4914.29844.0811.53
634.4615.1852.8210.63
644.3118.05864.0515.2
654.4517.89873.6816.24
664.618.04883.6815.03
672.2112.23892.4113.62
681.748.66901.483.98
692.027.64911.785.87

Fig.6

GLOF value distribution of abnormal sample"

Table 5

PC values of AFCM clustering for abnormal samples under different cluster numbers"

簇数PC值
50.08
60.12
70.09
80.18
90.13

Fig.7

FCM clustering results of final abnormal samples"

Table 6

Comparison of accuracy and running time of AFCM and conventional FCM"

算法准确率运行时长/s
传统FCM聚类0.786.7
AFCM聚类0.932.4
1 Liu R, Yang B, Zio E, et al. Artificial intelligence for fault diagnosis of rotating machinery: a review[J]. Mechanical Systems & Signal Processing, 2018, 108: 33-47.
2 黄海松, 魏建安, 任竹鹏, 等. 基于失衡样本特性过采样算法与SVM的滚动轴承故障诊断[J]. 振动与冲击, 2020, 39(10): 65-74, 132.
Huang Hai-song, Wei Jian-an, Ren Zhu-peng, et al. Rolling bearing fault diagnosis based on imbalanced sample characteristics oversampling algorithm and SVM[J]. Journal of Vibration and Shock, 2020, 39(10): 65-74, 132.
3 何雷, 刘溯奇, 蒋婷, 等. 基于改进LMD与BP神经网络的变速箱故障诊断[J]. 机械传动, 2020, 44(1): 171-176.
He Lei, Liu Su-qi, Jiang Ting, et al. Gearbox fault diagnosis based on improved LMD and BP neural network[J]. Journal of Mechanical Transmission, 2020, 44(1): 171-176.
4 Gilles J. Empirical wavelet transform[J]. IEEE Transactions on Signal Processing, 2013, 61(16): 3999-4010.
5 Yu J, Hua Z, Li Z. A new compound faults detection method for rolling bearings based on empirical wavelet transform and chaotic oscillator[J]. Chaos, Solitons & Fractals, 2016, 89: 8-19.
6 何洋洋, 王馨怡, 董晶. 基于经验小波变换与谱峭度的船舶轴系故障特征提取方法[J]. 中国舰船研究, 2020, 15(): 98-106.
He Yang-yang, Wang Xin-yi, Dong Jing. Fault feature extraction method for marine shafting based on empirical wavelet transform-spectral kurtosis[J]. Chinese Journal of Ship Research, 2020, 15(Sup.1): 98-106.
7 叶益丰. 基于MEWT-KPCA的电主轴故障诊断技术研究[D]. 长春: 吉林大学机械与航空航天工程学院, 2018.
Ye Yi-feng. Fault diagnosis technology of motorized spindle based on MEWT-KPCA[D]. Changchun: School of Mechanical and Aerospace Engineering, Jilin University, 2018.
8 叶柯华, 李春, 胡璇. 基于经验小波变换和关联维数的风力机齿轮箱故障诊断[J]. 动力工程学报, 2021, 41(2): 113-120.
Ye Ke-hua, Li Chun, Hu Xuan. Fault diagnosis of a wind turbine gearbox based on empirical wavelet transform and correlation dimension[J]. Journal of Chinese Society of Power Engineering, 2021, 41(2): 113-120.
9 赵若妤, 马宏忠, 魏旭, 等. 基于EWT及多尺度形态谱的高压并联电抗器故障诊断研究[J]. 电力系统保护与控制, 2020, 48(17): 68-75.
Zhao Ruo-yu, Ma Hong-zhong, Wei Xu, et al. Research on fault diagnosis of a high voltage shunt reactor based on EWT and multiscale spectral spectrum[J]. Power System Protection and Control, 2020, 48(17): 68-75.
10 乔志城, 刘永强, 廖英英. 改进经验小波变换与最小熵解卷积在铁路轴承故障诊断中的应用[J]. 振动与冲击, 2021, 40(2): 81-90, 118.
Qiao Zhi-cheng, Liu Yong-qiang, Liao Ying-ying. Application of improved wavelet transform and minimum entropy deconvolution in railway bearing fault diagnosis[J]. Journal of Vibration and Shock, 2021, 40(2): 81-90, 118.
11 常勇, 包广清, 程思凯, 等. 基于VMD和KFCM的轴承故障诊断方法优化与研究[J]. 西南大学学报: 自然科学版, 2020, 42(10): 146-155.
Chang Yong, Bao Guang-qing, Cheng Si-kai, et al. Optimization and research of a bearing fault diagnosis method based on VMD and KFCM[J]. Journal of Southwest University(Natural Science Edition),2020, 42(10): 146-155.
12 林越, 刘廷章, 唐侃. 基于自适应模糊聚类与核主元分析混合模型的变压器异常检测[J]. 科技通报, 2020, 36(9): 56-60.
Lin Yue, Liu Ting-zhang, Tang Kan. Anomaly detection of power transformer based on KFCM-KPCA hybrid model[J]. Bulletin of Science and Technology, 2020, 36(9): 56-60.
13 贺湘宇, 何清华. 基于有源自回归模型与模糊C-均值聚类的挖掘机液压系统故障诊断[J]. 吉林大学学报: 工学版, 2008, 38(1): 183-187.
He Xiang-yu, He Qing-hua. Fault diagnosis for excavator hydraulic system based on auto-regressive with extra inputs model and fuzzy C-means clustering[J]. Journal of Jilin University(Engineering and Technology Edition), 2008, 38(1): 183-187.
14 王庆锋, 刘家赫, 卫炳坤, 等. 数据驱动的聚类分析故障识别方法研究[J]. 机械工程学报, 2020, 56(18): 7-14.
Wang Qing-feng, Liu Jia-he, Wei Bing-kun, et al. Research on data-driven clustering analysis fault identification method[J]. Journal of Mechanical Engineering, 2020, 56(18): 7-14.
15 院老虎, 连冬杉, 张亮, 等. 基于密集连接卷积网络和支持向量机的飞行器机械部件故障诊断[J]. 吉林大学学报: 工学版, 2021, 51(5): 1635-1641.
Yuan Lao-hu, Lian Dong-shan, Zhang Liang, et al. Fault diagnosis of key mechanical components of aircraft based on densenet and support vector machine [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(5): 1635-1641.
16 徐忠兰. 基于SOM神经网络的煤矿用防爆柴油机故障诊断[J]. 煤矿机械, 2021, 42(4): 175-177.
Xu Zhong-lan. Fault diagnosis of mine explosion-proof diesel engine based on SOM neural network[J]. Coal Mine Machinery, 2021, 42(4): 175-177.
17 Lindeberg T. Scale-Space Theory in Computer Vision[M]. Berlin: Springer, 1994.
18 Gilles J, Heal K. A parameterless scale-space approach to find meaningful modes in Histograms-application to image and spectrum segmentation[J]. International Journal of Wavelets Multiresolution & Information Processing, 2014, 12(6): 1450044.
19 蔡艳平, 李艾华, 王涛, 等. 基于EMD-Wigner-Ville的内燃机振动时频分析[J]. 振动工程学报, 2010, 23(4): 430-437.
Cai Yan-ping, Li Ai-hua, Wang Tao, et al. I.C. engine vibration time-frequency analysis based on EMD-Wigner-Ville[J]. Journal of Vibration Engineering, 2010, 23(4): 430-437.
20 He X, Cai D, Niyogi P. Laplacian score for feature selection[C]∥Advances in Neural Information Processing Systems 18, Vancouver, British Columbia, Canada, 2005: 507-514.
21 欧璐, 于德介. 基于拉普拉斯分值和模糊C均值聚类的滚动轴承故障诊断[J]. 中国机械工程, 2014, 25(10): 1352-1357.
Lu Ou, Yu De-jie. Rolling bearing fault diagnosis based on laplacian score and fuzzy C-means clustering[J]. China Mechanical Engineering, 2014, 25(10): 1352-1357.
22 Peng H, Long F, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2005, 27(8): 1226-1238.
23 Breunig M M, Kriegel H P, Ng R T, et al. LOF: identifying density-based local outliers[C]∥ACM Sigmod International Conference on Management of Data, Dallas, United States, 2000: 93-104.
24 朱庆生,唐汇,冯骥.一种基于自然最近邻的离群检测算法[J]. 计算机科学, 2014, 41(3): 282-284, 311.
Zhu Qing-sheng, Tang Hui, Feng Ji. Outlier detection algorithm based on natural nearest neighbor[J]. Computer Science, 2014, 41(3): 282-284, 311.
25 Ester M, Kriegel H P, Sander J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise[C]∥International Conference on Knowledge Discovery and Data Mining, Orlando, USA, 1996: 226-231.
26 王光, 林国宇. 改进的自适应参数DBSCAN聚类算法[J]. 计算机工程与应用, 2020, 56(14): 45-51.
Wang Guang, Lin Guo-yu. Improved adaptive parameter DBSCAN clustering algorithm[J]. Computer Engineering and Applications, 2020, 56(14): 45-51.
27 Bezdek J C, Ehrlich R, Full W. FCM: the fuzzy C-means clustering algorithm[J]. Computers & Geosciences, 1984, 10(2): 191-203.
28 Wang W, Zhang Y. On fuzzy cluster validity indices[J]. Fuzzy Sets & Systems, 2007, 158(19): 2095-2117.
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