Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed

Sharma, Vikas; Parey, Anand

doi:10.1016/j.engfailanal.2019.104204

Cited by 76 publications

(25 citation statements)

References 38 publications

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“…Lots of direct and combined approaches have been exploited for the fault diagnosis of rotating machinery [58][59][60]. It is noted that variable operational conditions are studied in some analysis methods [61][62][63]. In the light of the advantages in the nonstationary signal analysis, spectral correlation has been successfully used for bearing fault diagnosis [64][65][66].…”

Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

2020

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In the light of the significance of the rotating machinery and the possible severe losses resulted from its unexpected defects, it is vital and meaningful to exploit the effective and feasible diagnostic methods of its faults. Among them, the emphasis of the analysis approaches for fault type and severity is on the extraction of useful components in the fault features. On account of the common cyclostationarity of vibration signal under faulty states, fault diagnosis methods based on cyclostationary analysis play an essential role in the rotatory machine. Based on it, the fundamental definition and classification of cyclostationarity are introduced briefly. The mathematical principles of the essential cyclic spectral analysis are outlined. The significant applications of cyclostationary theory are highlighted in the fault diagnosis of the main rotating machinery, involving bearing, gear, and pump. Finally, the widely-used methods on the basis of cyclostationary theory are concluded, and the potential research directions are prospected.

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Section: Applications Of Cyclostationarity Theory In Fault Diagnosis Of Rotating Machinerymentioning

confidence: 99%

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

2020

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“…According to the envelope spectrum and calculation in Figure 10, only the double-fault frequency 2 f 0 is visible, so it cannot be used as the basis for the fault diagnosis. 24–26 Therefore, we used the CE-ASNBD, ASNBD and CEEMD methods to decompose the original signal, and the results are shown in Figures 11–16. In Figure 11, the frequency f 0 , frequency doubling 2 × f 0 and frequency tripling 3 × f 0 clearly appear.…”

Section: Experimental Data Analysismentioning

confidence: 99%

Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis

Cheng

Chen

Luo

et al. 2020

Advances in Mechanical Engineering

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Adaptive sparsest narrow-band decomposition is the most sparse solution to search for signals in the over-complete dictionary library containing intrinsic mode functions, which transform the signal decomposition into an optimization problem, but the calculation accuracy must be improved in the case of strong noise interference. Therefore, in combination with the algorithm of the complementary ensemble empirical mode decomposition, a new method of the complementary ensemble adaptive sparsest narrow-band decomposition is obtained. In the complementary ensemble adaptive sparsest narrow-band decomposition, the white noise opposite to the paired symbol is added to the target signal to reduce the reconstruction error and realize the adaptive decomposition of the signal in the process of optimizing the filter parameters. The analysis results of the simulation and experimental data show this method is superior to complementary ensemble empirical mode decomposition and adaptive sparsest narrow-band decomposition in inhibiting the mode confusion, endpoint effect, improving the component orthogonality and accuracy, and effectively identifying the gears fault types.

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“…The traditional monitoring and diagnosis methods are mostly based on signal feature extraction performed under uniform speed conditions. However, planetary gearboxes in industrial equipment often work under variable speed conditions [ 21 ]. Order analysis is the most commonly used signal analysis method under variable speed conditions [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions

Zhang

Kuang

et al. 2021

Sensors

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Planetary gearboxes are the key components of large equipment, such as wind turbines, shield machines, etc. The operating state of the planetary gearbox is related to the safety of the equipment as a whole, and its feature extraction technology is essential. In assessing the problem of the non-stationarity of the current signal under variable speed conditions and the difficulty of evaluating the operating state of the planetary gearbox under a tacholess condition, a three-phase current, variable-speed tacholess envelope order analysis method is proposed. Firstly, a tacholess rotation speed estimation is completed by extracting the trend term of the instantaneous frequency of the asynchronous motor’s three-phase currents. The motor slip rate is assumed to be constant. Then, the envelope order analysis signal is obtained by re-sampling in the angular domain. Finally, the features of the envelope order signal are extracted, and a linear discriminant analysis (LDA) algorithm is used to fuse multiple indexes to generate a comprehensive feature reflecting the operating status of the planetary gearbox. The results of the simulation analysis and experimental verification show that the proposed method is effective in evaluating the operating state of the planetary gearbox under variable speed conditions. Compared with the traditional time–frequency ridge extraction method, the tacholess speed estimation method can improve the instantaneous speed estimation accuracy. The comprehensive index of envelope order completes the planetary gearbox state identification process, and a 95% classification accuracy rate is achieved.

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Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed

Cited by 76 publications

References 38 publications

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

Cyclostationary Analysis towards Fault Diagnosis of Rotating Machinery

Complementary ensemble adaptive sparsest narrow-band decomposition method and its applications to the gear crack fault diagnosis

A Three-Phase Current Tacholess Envelope Order Analysis Method for Feature Extraction of Planetary Gearbox under Variable Speed Conditions

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