Application of a whale optimized variational mode decomposition method based on envelope sample entropy in the fault diagnosis of rotating machinery

Lü, Na; Zhou, Tingxin; Wei, Juyao; Yuan, Wanmai; Li, R Q; Li, Meng-Meng

doi:10.1088/1361-6501/ac3470

Cited by 21 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Compared with EEMD, SEMD significantly reduces mode mixing with a more efficient process, ensuring complete removal of the added sine signals. Sample entropy is a suitable metric for assessing the complexity of IMFs owing to its robustness and less biased statistical advantages [15,16]. Accordingly, this study employed SEMD to decompose vibration signals into IMFs by calculating the sample entropies of these IMFs as fault features.…”

Section: Feature Extractionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on SEMD and ISSA-KELMC

Hu,

Zhao,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Enhancing the operational reliability of rotary machinery relies significantly on the effective diagnosis of faults in rolling bearings. This study introduces an innovative method to improve the accuracy of fault diagnosis of rolling bearings during operation. First, we propose a sine empirical mode decomposition (SEMD) designed to effectively mitigate mode mixing and decompose the vibration signals of rolling bearings into a series of intrinsic mode functions. Subsequently, we constructed and optimized a kernel extreme learning machine classifier (KELMC) using the improved sparrow search algorithm (ISSA). Within ISSA, the Opposition-based Learning method is refined and applied to enhance the optimization performance of the sparrow search algorithm. Finally, the paper presents a novel method for the fault diagnosis of rolling bearings based on SEMD and ISSA-KELMC, which can effectively extract the fault features and accurately recognize the fault types of rolling bearings by taking advantage of the SEMD and ISSA-KELMC. The effectiveness of the proposed method was verified through two simulation and fault diagnosis experiments. The results demonstrated the efficiency of the method in diagnosing faults in rolling bearings under both consistent and variable working conditions. This approach is valuable for fault diagnosis and condition monitoring of rotating machinery. 

show abstract

Section: Feature Extractionmentioning

confidence: 99%

Fault diagnosis of rolling bearing based on SEMD and ISSA-KELMC

Hu,

Zhao,

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…In summary, although EMD decomposition, wavelet decomposition and VMD decomposition decompose the wind power data in the time and frequency domains respectively, and make the wind power data stable, there are certain shortcomings in the above methods: (1) the number of modal components cannot be adjusted, and there are problems of over‐decomposition or under‐decomposition 17 ; (2) although the EMD algorithm has been improved, there are still serious problems of modal mixing, 18 and subsequent improved algorithms such as ensemble empirical mode decomposition (EEMD) and complementary ensemble empirical mode decomposition (CEEMD) also suffer from incomplete decomposition or too many components 19 ; (3) although VMD decomposition allows for customization of the number of decomposition levels and generally outperforms the other two decomposition algorithms, the number of decomposition levels is uncertain, and either too low or too high will affect the decomposition performance of VMD 20 ; (4)although the optimization algorithm can be used to achieve the optimization of the parameters of the VMD decomposition algorithm, the existing optimization algorithm has the problems of too slow search and single fitness function. Most of the optimization algorithms can only optimize the number of components from specific indicators such as envelope entropy, 21 envelope sample entropy, 22 and envelope sample entropy 23 when used in the evaluation of the decomposition algorithm after the letter, and cannot find the optimal parameters that can make the decomposition algorithm decomposition performance is high and the decomposition speed is fast.…”

Section: Introductionmentioning

confidence: 99%

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Zhang,

Wang,

et al. 2023

Energy Science & Engineering

View full text Add to dashboard Cite

Wind power prediction enables advance prediction of the future capacity of wind farms to improve production, increase capacity and reduce costs, however, wind power generation data are highly unstable, making it difficult to achieve high‐precision prediction. Signal decomposition methods and optimization algorithms allow for data smoothing and optimization of parameter settings, but the limitations of parameter dependency and the slow optimization search process prevent existing research from being applied in a practical setting. To address these issues, this article proposes a multiscale ultra‐short‐term wind power prediction model based on particle swarm optimization based on greedy dynamic and integrated fitness evaluation method (GD‐IFEM‐PSO) and variational mode decomposition and back propagation network (VMD‐BP). First, this article improves the velocity update formula in the particle swarm optimization algorithm by the proposed GD weight and sets up a fitness function using multiple evaluation metrics by the proposed integrated fitness evaluation method (IFEM), so that the improved particle swarm optimization algorithm achieves high efficiency in the optimization search while ensuring the comprehensiveness of the evaluation. Second, the variable mode decomposition (VMD) decomposition algorithm is used to decompose the historical wind power data to achieve smoothing of the wind power data, and the improved particle swarm optimization algorithm is used to optimize the K and ⍺ values in the VMD decomposition algorithm to improve the comprehensive performance of the decomposition. Then, to achieve data reduction and fast model training, the components are divided into trend components, low‐frequency vibration components, high‐frequency vibration components, and random noise components according to the central frequency, so that the model can better grasp the data trend while reducing the number of components and achieving high prediction accuracy. Finally, the different components are predicted by the BP neural network and the predicted values of the different categories of components are reconstructed into the final wind power prediction values. To demonstrate the rationality and progressiveness of the proposed model, several models are compared on several data sets, and the results show that the proposed model has faster prediction speed and higher prediction performance, and is more suitable for application in real‐world environments.

show abstract

“…For example, in wavelet analysis, minimum Shannon entropy criterion [18], and minimum entropy criterion [19] were used to select an appropriate mother wavelet. Lu et al [20] proposed an envelope sample entropy as a new fitness function to optimize parameters of variational mode decomposition for rotating machinery fault diagnosis. Entropy-based methods have been widely used for pattern recognition and classification.…”

Section: Introductionmentioning

confidence: 99%

Investigations on sample entropy and fuzzy entropy for machine condition monitoring: revisited

Wang

2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Complexity measures typically represented by entropy are capable of detecting and characterizing underlying dynamic changes in a system and they have been considerably studied for machine condition monitoring and fault diagnosis. Various entropies have been developed based on Shannon entropy to meet actual demands. Nevertheless, currently existing research works about complexity measures mainly focus on experimental studies, and their theoretical studies are still going on and not fully explored. In previous studies, it was theoretically and experimentally proved that two complexity measures including correlation dimension and approximate entropy have a ‘‘bilateral reduction” effect. Since sample entropy and fuzzy entropy are two more advanced complexity measures that were developed based on the concept of correlation dimension and approximate entropy, this paper continues conducting theoretical and experimental investigations on sample entropy and fuzzy entropy and exploring their theoretical properties to enrich the domain of complexity measure analysis and its applications to machine condition monitoring. Specifically, this paper theoretically proves and verifies that sample entropy and fuzzy entropy still have a similar “bilateral reduction” effect with correlation dimension and approximate entropy, and they are indeed complexity measures. The relationships between sample entropy, fuzzy entropy, and their key parameters during their calculation are numerically and experimentally studied. Bearing and gear run-to-failure datasets are used to investigate the effectiveness of sample entropy and fuzzy entropy for bearing and gear condition monitoring, and experimental results of sample entropy and fuzzy entropy are well-matched with the theoretical “bilateral reduction” effect of sample entropy and fuzzy entropy. Overall, this paper will provide a guideline for correct uses of sample entropy and fuzzy entropy for engineering applications, especially for machine condition monitoring.

show abstract

Application of a whale optimized variational mode decomposition method based on envelope sample entropy in the fault diagnosis of rotating machinery

Cited by 21 publications

References 36 publications

Fault diagnosis of rolling bearing based on SEMD and ISSA-KELMC

Fault diagnosis of rolling bearing based on SEMD and ISSA-KELMC

Multiscale ultra‐short‐term wind power prediction model based on GD‐IFEM‐PSO and VMD‐BP

Investigations on sample entropy and fuzzy entropy for machine condition monitoring: revisited

Contact Info

Product

Resources

About