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
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