Pitting and cracking, common faults in gear transmissions, manifest distinct characteristics in vibration signals due to their differing generation and characteristics. Extracting and identifying these essential characteristics that reflect the unique traits of pitting and cracks presents a challenging task in gear fault diagnosis. This article initially employs simple Fourier transform method considering vibrational mechanisms to extract the impact waveforms caused by pitting and cracks from dynamic responses, and further proposes that three faults indicate assessing the characteristics of pitting and crack. Subsequently, a novel weak dictionary-based sparse enhancement method has been presented inspired by signal modulation, which can adaptively enhance the frequencies of pitting and crack in the frequency domain while preserving the lower components related to gear meshing frequencies. Ultimately, the effectiveness of the proposed method and indicators was validated through the experiments involving different severities of gear pitting and cracks.
LeoniJTanelliMPalmanA. A new comprehensive monitoring and diagnostic approach for early detection of mechanical degradation in helicopter transmission systems. Expert Syst Appl2022; 210: 118412.
2.
MarkWWagnerMIsaacsonA. Detection and diagnosis of gear-tooth pitting damage by the Average-Log-Ratio ALR algorithm. Mech Syst Signal Process2022; 186: 109911.
3.
GaoDZhuYRenZ. A novel weak fault diagnosis method for rolling bearings based on LSTM considering quasi-periodicity. Knowl Based Syst2021; 231: 107413.
4.
ZioE. Prognostics and Health Management (PHM): where are we and where do we (need to) go in theory and practice. Reliab Eng Syst Saf2022; 218: 108119.
5.
LiYDingKHeG. Vibration mechanisms of spur gear pair in healthy and fault states. Mech Syst Signal Process2016; 81: 183–201.
6.
YangXDingK. Phenomenon-model-based AM-FM vibration mechanism of faulty spur gear. Mech Syst Signal Process2019; 134: 106366.
7.
AnkurSManojCAnandP. Effect of mesh stiffness of healthy and cracked gear tooth on modal and frequency response characteristics of geared rotor system. Mech Mach Theory2017; 107: 261–273.
8.
BittencourtVRemaGFioriD. Influence of tooth crack parameters on bearing vibration signal of a geared rotor. Eng Fail Anal2022; 141: 106700.
9.
WangJDuGZhuZ, et al. Fault diagnosis of rotating machines based on the EMD manifold. Mech Syst Signal Process2020; 135: 106443.
10.
LiFLiRTianL, et al. Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable working conditions. Mech Syst Signal Process2019; 116: 462–479.
11.
QiaoZJZhangCLZhangCL, et al. Stochastic resonance array for designing noise-boosted filter banks to enhance weak multi-harmonic fault characteristics of machinery. Appl Acoust2025; 236(5): 110710.
12.
LiYXDingQYYiYM, et al. Radial basis function interpolation-multiscale optimized dual-threshold permutation entropy and its application to mechanical fault diagnosis. Commun Nonlinear Sci Numer Simul2025; 148: 108904.
13.
SharmaSTiwariSSinghS. Diagnosis of gear tooth fault in a bevel gearbox using discrete wavelet transform and autoregressive modeling. Life Cycle Reliab Saf Eng2019; 8: 21–32.
14.
MaSHanQChuF. Sparse representation learning for fault feature extraction and diagnosis of rotating machinery. Expert Syst Appl2023; 232: 120858.
15.
WangLMaSHanQ. Reweighted dual sparse regularization and convex optimization for bearing fault diagnosis. IEEE Trans Instrum Meas2021; 70: 1–9.
16.
ZhangXLiuZWangL, et al. Bearing fault diagnosis based on sparse representations using an improved OMP with adaptive Gabor sub-dictionaries. ISA Trans2020; 106: 355–366.
17.
LiQ. A comprehensive survey of sparse regularization: fundamental, state-of-the-art methodologies and applications on fault diagnosis. Expert Syst Appl2023; 229: 120517.
18.
LiQLiangSY. An improved sparse regularization method for weak fault diagnosis of rotating machinery based upon acceleration signals. IEEE Sens J2018; 18: 6693–6705.
19.
ZhangLLiYDongL, et al. Gearbox fault diagnosis using multiscale sparse frequency-frequency distributions. IEEE Access2021; 9: 113089–113099.
20.
PanHYangYLiX, et al. Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis. Mech Syst Signal Process2019; 114: 189–211.
21.
CaoZDaiJXuW, et al. Bearing fault diagnosis with frequency sparsity learning. IEEE Trans Instrum Meas2022; 71: 1–10.
22.
QinYZouJTangB, et al. Transient feature extraction by the improved orthogonal matching pursuit and K-SVD algorithm with adaptive transient dictionary. IEEE Trans Ind Informat2020; 16: 215–227.
23.
LiuWZhuRWangJ, et al. Dynamical modeling of spur gear with pitting based on image processing tooth surface. Struct Health Monit2023; 23(1): 283–303.
24.
LiuYShiZShenG, et al. Evaluation model of mesh stiffness for spur gear with tooth tip chipping fault. Mech Mach Theory2021; 158: 104238.
25.
XuXLaiJLohmannC, et al. A model to predict initiation and propagation of micro-pitting on tooth flanks of spur gears. Int J Fatigue2019; 122: 106–115.
26.
LiuWZhuRZhouW, et al. Probability distribution model of gear time-varying mesh stiffness with random pitting of tooth surface. Eng Failure Anal2021; 130: 105782.
27.
MohammedODRantataloM. Gear fault models and dynamics-based modelling for gear fault detection – a review. Eng Failure Anal2020; 117: 104798.
28.
LiQDingXHuangW, et al. Transient feature self-enhancement via shift-invariant manifold sparse learning for rolling bearing health diagnosis. Measurement2019; 148: 106957.
29.
RubinsteinRZibulevskyMEladM, et al. Double sparsity: learning sparse dictionaries for sparse signal approximation. IEEE Trans Signal Process2010; 58: 1553–1564.
30.
ZhouJWangSTongC, et al. Weighted basis pursuit denoising algorithm and its application in gear fault diagnosis. In: 2021 International conference on sensing, measurement & data analytics in the era of artificial intelligence (ICSMD), Nanjing, China, 2021, pp. 1–5. Piscataway, NJ: IEEE.
31.
LiQDingXHeQB, et al. Manifold sensing-based convolution sparse self-learning for defective bearing morphological feature extraction. IEEE Trans Ind Inform2021; 17: 3069–3078.
32.
DingXHeQ. Time–frequency manifold sparse reconstruction: a novel method for bearing fault extraction. Mech Syst Signal Process2016; 80: 392–413.
33.
LiuWZhuRZhouW, et al. Analysis investigation of the influence of sub-surface shear stress distribution of spur gear. Proc Inst Mech Eng Part C J Mech Eng Sci2024; 238(21): 10490–10501.
34.
HeGLiJDingK, et al. Feature extraction of gear and bearing compound faults based on vibration signal sparse decomposition. Appl Acoust2022; 189: 108604.
35.
GuoYJiangSYangY, et al. Gearbox fault diagnosis based on improved variational mode extraction. Sensors2022; 22(5): 1779.
