Incipient fault diagnosis of planetary gearboxes based on an adaptive parameter-induced stochastic resonance method

Xu, Bao-Ming; Shi, Jiancong; Zhong, Min; Zhang, Jun

doi:10.1016/j.apacoust.2021.108587

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…From this diagram, it is clear that the ILFB is at level 5. Third, the weak fault information in the ILFB is enhanced by an IGOA‐based SR system after the demodulated signal is compressed with a ratio of 30 34 . Finally, the fault feature f s1 is recognized in the ES, which is shown in Figure 5B.…”

Section: Experimental Validationmentioning

confidence: 99%

“…s is a defined nonlinear action function of adjusting social force, and the equation is as follows:

s(d)=f{e}^{\frac{-d}{l}}-{e}^{-d},

where d and f represent two constants that affect the attractiveness length scale and the intensities of attraction, respectively. Typically, this value is always fixed as d = 0.5 and f = 1.5 34 …”

Section: Background Theoriesmentioning

confidence: 99%

“…More recently, the authors applied a grasshopper optimization algorithm (GOA)-optimized bistable SR system to identify incipient gear faults. 34 Note that GOA searching is very sensitive to the position of the initial optimizing particles. To obtain a more stable output of GOA, the initial point should be carefully selected.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al 33 established an adaptive SR system whose parameters were optimized using the salp swarm algorithm. More recently, the authors applied a grasshopper optimization algorithm (GOA)‐optimized bistable SR system to identify incipient gear faults 34 . Note that GOA searching is very sensitive to the position of the initial optimizing particles.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

Shi

Wang

et al. 2023

Int Journal of Mech Sys Dyn

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Incipient faults of gears and rolling bearings in rotating machineries are very difficult to identify using traditional envelope analysis methods. To address this challenge, this paper proposes an effective local spectrum enhancement-based diagnostic method that can identify weak fault frequencies in the original complicated raw signals. For this purpose, a traversal frequency band segmentation technique is first

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Section: Experimental Validationmentioning

confidence: 99%

“…s is a defined nonlinear action function of adjusting social force, and the equation is as follows:

s(d)=f{e}^{\frac{-d}{l}}-{e}^{-d},

Section: Background Theoriesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

Shi

Wang

et al. 2023

Int Journal of Mech Sys Dyn

Self Cite

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“…In addition to the aforementioned methods, some other methods like Wavelet Packet Transform (WPT), 23 wavelet energy, 24 and adaptive parameter-induced stochastic resonance 25 were employed for gearbox fault detection in different fault severities. Some other signal processing and health indicators are reviewed in Ref.…”

Section: Introductionmentioning

confidence: 99%

Incipient Fault Detection of Helical Gearbox Based on Variational Mode Decomposition and Time Synchronous Averaging

Niaki

Alavi

Ohadi

2022

Structural Health Monitoring

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As the fault detection methods diagnose defects in the earlier stage, the subsequent costs will be reduced. Feature extraction from the vibration signal is the foremost step for incipient fault detection of gearboxes. However, the current statistical features in the time and frequency domains cannot diagnose the early or low intensity faults. In this research, for the first time, besides these features, some other features are extracted by combining variational mode decomposition and time synchronous average (VMD-TSA) to overcome the problem. The combinations have occurred in two ways. First, the Intrinsic Mode Functions (IMFs) of the TSA signal are calculated by VMD, and the Amplitude Energy (AE) and Permutation Entropy (PE) of the first four IMFs are computed. Secondly, the IMFs of vibration signals are calculated, and the TSA features are extracted from the most informative IMF. Moreover, 16 features in the time domain, 13 features in the frequency domain, and 9 features by TSA are extracted from the vibration signals. These features are extracted from healthy and four faulty conditions: crack, spalling, chipping, and wear in three different severities. After feature extraction, the Relief-F algorithm selects the informative features, and selected features are utilized for fault detection by a Feed-Forward Neural network (FNN) classifier. In this study, the ability of the VMD-TSA method is compared with others like Empirical Mode Decomposition-TSA (EMD-TSA) and Ensemble Empirical Mode Decomposition-TSA (EEMD-TSA), which shows that the proposed method is more powerful than others in early fault detection. Besides, the classification accuracy of these methods is compared with some other feature selection methods like Laplacian Score (LS), Principal Component Analysis (PCA), and Minimum Redundancy-Maximum Relevance (MRMR). Also, the performance of the FNN classifier is compared with the Support Vector Machine (SVM). As shown in this study, the VMD-TSA features improve the early fault detection in a positive manner. For instance, the classification accuracy of all features without VMD-TSA in crack fault detection is 93.98%. However, by adding VMD-TSA features, the accuracy grows up to 99.48% in the same conditions.

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Self-Adaptive Stochastic Resonance Rub-Impact Fault Identification Grounded on a New Signal Evaluation Index

Yu,

Wang,

et al. 2023

J Fail. Anal. and Preven.

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Incipient fault diagnosis of planetary gearboxes based on an adaptive parameter-induced stochastic resonance method

Cited by 17 publications

References 28 publications

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

A local spectrum enhancement‐based method and its application in incipient fault diagnosis of rotating machinery

Incipient Fault Detection of Helical Gearbox Based on Variational Mode Decomposition and Time Synchronous Averaging

Self-Adaptive Stochastic Resonance Rub-Impact Fault Identification Grounded on a New Signal Evaluation Index

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