The time point of compound fault evolution plays an important role in bearing healthy stage division and life prediction. However, most existing fault monitoring methods do not pay attention to the identification of compound fault evolution time points. In order to solve this problem, considering the influence of random impact on time series, an evolution identification method of compound faults of rolling bearings based on evolution trend index and bidirectional dichotomy approach square envelope autocorrelation correlation kurtosis (SEACK)gram is proposed. By constructing the SEACK, the variation coefficient of envelope spectrum SEACK (VCESS) is analyzed, and the compound fault evolution trend index VCESS is put forward. According to the characteristics that the evolution time points of different types of faults have great changes corresponding to the evolution trend indicators, the evolution trend of compound faults is monitored and an approximate evolution time point is identified. Furthermore, SEACKgram method combined with bidirectional dichotomy approach strategy is used to accurately locate the evolution time point of compound faults. The effectiveness of the proposed method is verified by the open full-cycle rolling bearing life fault data experiment, which provides a more accurate basis for health state division and life prediction.
CuiLZhaoXLiuD, et al. A spectral coherence cyclic periodic index optimization-gram for bearing fault diagnosis. Measurement2024; 224: 113898.
2.
ShiJWangFHuangY, et al. Rolling bearing fault diagnosis under strong background noise based on ACMD and optimized MOMEDA. J Sens2024; 2024: 3293579.
3.
LiJLuoWBaiM, et al. Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal. Meas Sci Technol2024; 35: 92001.
4.
ChenTGuoLFengT, et al. IESMGCFFOgram: a new method for multicomponent vibration signal demodulation and rolling bearing fault diagnosis. Mech Syst Signal Process2023; 204: 110800.
5.
LiHWangTZhangF, et al. Enhanced adaptive high-frequency resonance technology for bearing fault detection. Struct Health Monit2024; 23: 3430–3445.
6.
ZhangCQiangYHouW, et al. High-fidelity fault signature extraction of rolling bearings via nonconvex regularized sparse representation enhanced by flexible analytical wavelet transform. Struct Health Monit2024; 23: 2869–2891.
7.
FengKJiJCNiQ.A novel gear fatigue monitoring indicator and its application to remaining useful life prediction for spur gear in intelligent manufacturing systems. Int J Fatigue2023; 168: 107459.
8.
SongLLinTJinY, et al. Advancements in bearing remaining useful life prediction methods: a comprehensive review. Meas Sci Technol2024; 35: 92003.
9.
WangHYanCWangZ, et al. Compound fault diagnosis method for rolling bearings based on the multipoint kurtosis spectrum and AO-MOMDEA. Meas Sci Technol2023; 34: 95012.
10.
LiSZhaoMWeiY, et al. Adaptive residual spectral amplitude modulation: a new approach for bearing diagnosis under complex interference environments. Mech Syst Signal Process2024; 220: 111682.
11.
YangMZhangKShengZ, et al. The amplitude modulation bispectrum: a weak modulation features extracting method for bearing fault diagnosis. Reliab Eng Syst Saf2024; 250: 110241.
12.
FengKJiJCNiQ, et al. A novel vibration-based prognostic scheme for gear health management in surface wear progression of the intelligent manufacturing system. Wear2023; 522: 204697.
13.
FengKBorghesaniPSmithWA, et al. Vibration-based updating of wear prediction for spur gears. Wear2019; 426–427: 1410–1415.
14.
HuJZhangLLiangW.Dynamic degradation observer for bearing fault by MTS–SOM system. Mech Syst Signal Process2013; 36: 385–400.
15.
LiuQZhangYSiX, et al. DLVR-NWP: a novel data-driven bearing degradation model for rul estimation. IEEE Trans Instrum Meas2023; 72: 1.
16.
HouBWangDXiaT, et al. Investigations on quasi-arithmetic means for machine condition monitoring. Mech Syst Signal Process2021; 151: 107451.
17.
LiQYanCChenG, et al. Remaining useful life prediction of rolling bearings based on risk assessment and degradation state coefficient. ISA Trans2022; 129: 413–428.
18.
NomanKWangSFengK, et al. Weighted squared envelope dispersion entropy as nonlinear measure for dynamic health monitoring of rotating machineries. NDT & E Int2024; 147: 103207.
19.
MaWYuYGuoL, et al. RFIS-HI: a new health indicator for quantitative condition monitoring of the bearing under variable speed conditions. Struct Health Monit2024; 23: 2407–2422.
20.
LiuYWangJLiY, et al. Feature extraction method based on VMD and MFDFA for fault diagnosis of reciprocating compressor valve. J Vibroeng2017; 19: 6007–6020.
21.
ChenGYanCMengJ, et al. Health condition monitoring of bearings based on multifractal spectrum feature with modified empirical mode decomposition-multifractal detrended fluctuation analysis. Struct Health Monit2022; 21: 2618–2640.
22.
MengJYanCWangZ, et al. Health condition identification of rolling element bearing based on gradient of features matrix and MDDCs-MRSVD. IEEE Trans Instrum Meas2022; 71: 3517313.
23.
WangZLuCWangZ, et al. Fault diagnosis and health assessment for bearings using the Mahalanobis–Taguchi system based on EMD-SVD. Trans Inst Meas Control2013; 35: 798–807.
24.
LiQYanCWangW, et al. Health indicator construction based on MD-CUMSUM with multi-domain features selection for rolling element bearing fault diagnosis. IEEE Access2019; 7: 138528–138540.
25.
MengJYanCChenG, et al. Health indicator of bearing constructed by RMS-CUMSUM and GRRMD-CUMSUM with multi-features of envelope spectrum. IEEE Trans Instrum Meas2021; 70: 3054000.
26.
YangJDelphaC.A new reconstruction-based method using local Mahalanobis distance for incipient fault isolation and amplitude estimation. Mech Syst Signal Process2023; 205: 110803.
27.
WangHYanCZhaoY, et al. SEACKgram: a targeted method of optimal demodulation-band selection for compound faults diagnosis of rolling bearing. Struct Health Monit2025; 24: 223–242.
28.
MaZYuMGeX, et al. Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings. Meas Sci Technol2024; 35: 106134.
29.
PanYYiCSongX, et al. A novel adaptive resonant band detection method based on cyclostationarity for wheelset-bearing compound fault diagnosis. Measurement2023; 213: 112770.
30.
McDonaldGLZhaoQZuoMJ.Maximum correlated kurtosis deconvolution and application on gear tooth chip fault detection. Mech Syst Signal Process2012; 33: 237–255.
31.
WangBLeiYLiN, et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Trans Reliab2020; 69: 401–412.
32.
MengJYanCWenT, et al. Real-time identification of performance degradation stages of rolling element bearings by RVCFI. Meas Sci Technol2022; 33: 85021.
33.
CheginiSNManjiliMBagheri. A new fault diagnosis approaches for detecting the bearing slight degradationMeccanica2020; 55: 261–286.
34.
JiangFZhuZLiW.An improved VMD with empirical mode decomposition and its application in incipient fault detection of rolling bearing. IEEE Access2018; 6: 44483–644493.
35.
MaPZhangHFanW, et al. Early fault diagnosis of bearing based on frequency band extraction and improved tunable Q-factor wavelet transform. Measurement2019; 137: 189–202.
36.
JiangXWangJShiJ, et al. A coarse-to-fine decomposing strategy of VMD for extraction of weak repetitive transients in fault diagnosis of rotating machinesMech Syst Signal Process2019; 116: 668–692.
37.
BabikerALiQMengJ, et al. Initial fault time estimation of rolling element bearing by backtracking strategy, improved VMD and infogram. J Mech Sci Technol2021; 35: 425–437.
38.
ChenGYanCMengJ, et al. Improved VMD-FRFT based on initial center frequency for early fault diagnosis of rolling element bearing. Meas Sci Technol2021; 32: 11504.
