Fault feature extraction method for rotating machinery based on a CEEMDAN-LPP algorithm and synthetic maximum index

Lü, Na; Li, Mingliang; Zhang, Guangtao; Li, Ruiqiang; Zhou, Tingxin; Su, Chengguo

doi:10.1016/j.measurement.2021.110636

Cited by 14 publications

(7 citation statements)

References 37 publications

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“…As shown in Figure 7, due to the infuence of noise, only the rotational frequency fr, fault characteristic frequency fi, and weak 2fi can be extracted from the envelope spectrum of IMF1 and IMF2. Finally, the CEEMDAN method is used for analysis, with added white noise of 0.15 and ensemble size and screening iteration values set to 500 and 5000 [40]. Figure 8 shows the frst 4 IMF results after the simulation signal is decomposed by CEEMDAN.…”

Section: Simulation Analysismentioning

confidence: 99%

Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis

Ji,

Huang,

2024

Shock and Vibration

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The rolling bearing is one of the commonly used mechanical components in rotating machinery, and its health directly affects the normal operation of equipment. However, the fault signal of rolling bearing is susceptible to noise interference, which makes it difficult to extract the fault characteristics of the rolling bearing and thus affects the accuracy of the diagnosis results. To address this problem, this paper proposes a method by using a snake optimization algorithm to optimize variational mode decomposition (SOA-VMD) and applies it to the extraction of the fault feature of rolling bearing. First, the minimum Shannon entropy to kurtosis ratio (EKR) is used as the fitness function of SOA to search for the best parameter combination of VMD. Second, the optimized VMD is used to decompose the vibration signal of rolling bearing to obtain K intrinsic mode functions (IMFs). Then, the IMF with the most fault information is selected for reconstruction through EKR. The Teager–Kaiser energy operator (TKEO) spectrum analysis is performed on the reconstructed signal. Finally, this method is used to analyze the simulation signal and rolling bearing vibration signal and compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) algorithms to verify the feasibility and effectiveness of the SOA-VMD method.

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Section: Simulation Analysismentioning

confidence: 99%

Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis

Ji,

Huang,

2024

Shock and Vibration

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“…However, for data sets with sparse sample density, low dimensional embedding effect will be destroyed. Locality Preserving Projection (LPP) [12] is a nonlinear dimension reduction technique for preserving the local structure of data sets, mapping data points from original space to data points in low dimensional space can fully mine local information in high dimensional data space.…”

Section: Related Workmentioning

confidence: 99%

Dimension reduction algorithm of battlefield situation data based on local preserving discriminant projection

Hu,

Wang

2023

International Conference on Electronic Information Engineering and Data Processing (EIEDP 2023)

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“…However, these methods still face various challenges, such as sensitivity to noise, robustness to changing operating conditions, and generalization to new machine types. Due to the fact that the characteristic frequency components of vibration signals formed by faults of gears and bearings are usually masked by inherent vibration and noise signals, the effective extraction of fault characteristic components is the key to achieving accurate fault diagnosis [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Adaptive rotating machinery fault diagnosis method using MKIST

Yi,

Tan,

Yan

et al. 2024

Meas. Sci. Technol.

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An adaptive fault diagnosis method for rotating machinery based on maximum kurtosis incomplete S-transform (MKIST) is proposed in this paper. Due to faults in gears or bearings of rotating machinery, the inherent high-frequency resonance of the system is excited and the vibration signal is modulated. Therefore, extracting fault feature frequencies from the modulated signals is the key to fault diagnosis. To achieve the demodulation of fault frequency components, the incomplete S-transform is first performed on the fault frequency band of the vibration signal, and the module vector group is obtained through module calculation. Subsequently, the kurtosis of all the modulus vectors are calculated and the vector corresponding to the maximum kurtosis is located to adaptively determine the vector corresponding to the fault frequency. The waveform corresponding to the maximum kurtosis vector is the envelope of the fault frequency component in the vibration signal. Then, FFT operation is performed on the envelope to obtain its main frequency, which is matched with the fault mode frequency to achieve fault diagnosis of rotating machinery. Finally, the effectiveness of the proposed method was verified through simulation and actual signal experiments, and the Mean Peak Ratio (MPR) was used as an indicator to evaluate the performance of different fault diagnosis methods under various operating conditions. The experimental results show that the maximum MPR is obtained using the proposed method, demonstrating its stronger noise resistance and demodulation ability.

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Fault feature extraction method for rotating machinery based on a CEEMDAN-LPP algorithm and synthetic maximum index

Cited by 14 publications

References 37 publications

Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis

Research on the Application of Variational Mode Decomposition Optimized by Snake Optimization Algorithm in Rolling Bearing Fault Diagnosis

Dimension reduction algorithm of battlefield situation data based on local preserving discriminant projection

Adaptive rotating machinery fault diagnosis method using MKIST

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